Taxonomy Bootcamp

Taxonomy Bootcamp is happening again on the 16-17th October.

As a partnership organisation, NetIKX is able to offer members a 25% discount, the code for which has been sent to all members. If you are a member and have not received this, please email info[at]

 Key features:

  • Essential tips you can start applying right away to managing your taxonomy
  • New approaches to dealing with common issues such as getting business buy-in, and governance
  • Latest applications of taxonomies including NLP, semantics and machine learning
  • How to make the most of cutting-edge technologies and industry-leading software

 For more details and to register, go to:

Blog for the September 2018 Seminar: Ontology is cool!

Our first speaker, Helen Lippell, is a freelance taxonomist and is an organiser of the annual Taxonomy Boot Camp in London.  She also works with organisation on constructing thesauri, ontologies and link data repositories.   As far as she is concerned, the point of ontology construction is to model the world to help meet business objectives, and that’s the practical angle from which she approached the topic.  Taxonomies and ontologies are strongly related.  Taxonomies are concerned with the relationships between the terms used in a domain, ontologies focus more on describing the things within the domain and the relationships between them.  Neither is inherently better: you choose what is appropriate for your business need.  An ontology offers greater capabilities and a gateway to machine reasoning, but if you don’t need those, the extra effort will not be worth it.  A taxonomy can provide the controlled vocabularies which help with navigation and search.

Using fascinating examples, Helen, listed a number of business scenarios in which ontologies can be helpful: information retrieval, classification, tagging and data manipulation.  She is doing a lot of work currently on an ontology that will help in content aggregation and filtering, automating a lot of processes that are currently manual.

Implementing an ontology project is not trivial.  It starts with a process of thoroughly understanding and modelling everything connected to the particular domain in which the project and business operate.  Information professionals are well suited to link between the people with technical skills and others who know the business better and can advocate for the end-users of these systems.

Finally, Helen discussed the software that can facilitate this work, both free and to be purchased.  Her talk was followed by an exercise where we produced our own model, with plenty of help and advice from the speakers. We looked at problems in London that we could help solve such as guiding visitors to London or a five-year ecology plan.  It was fun, although we were not quite up to achieving a high-quality product ready to change the world!

In the second part of the meeting, we heard from Silver Oliver, an information architect.  Again, there was a short talk and then a practical exercise.   We learnt that Domain Modelling is fundamental to compiling successful taxonomies, controlled vocabularies and classifications schemes, as well as formal ontologies.  When you set out to model a domain, it is beneficial to engage as many voices and perspectives as possible.   It is helpful to do this before you start exploring tools and implementations so that you don’t exclude people from being able to participate with their different views and perspectives.   The exercise that followed looked at creating a website focusing on food and recipes, which was a pleasant topic to work on in our small groups.

The seminar finished with a set of recommendations:

  • Don’t dive into software: start with whiteboards.
  • Don’t work alone data modelling in the corner. Domain modelling is all about understanding he domain, through conversation and building shared language.
  • Be wary of getting inspiration from other models you believe to be similar. Start with conversations instead – though stealing ideas ca be useful!
  • Rather than ‘working closed’ and revealing your results at the end – keep the processes open and show people what you are doing.
  • An evolving ontology of the domain is a good way to capture these discussions and agreements about what things mean.
  • Rather than evolving a humongous monolithic domain model which is hard to get your head around, work with smaller domains with bounded contexts.

That led to a break with refreshments and general conversations based on our experiences during the afternoon.

Extract from a report by Conrad Taylor.

If you want to read the full account of this seminar – follow this link:

Blog for the July 2018 seminar: Machines and Morality: Can AI be Ethical?

In discussions of AI, one issue that is often raised is that of the ‘black box’ problem, where we cannot know how a machine system comes to its decisions and recommendations. That is particularly true of the class of self-training ‘deep machine learning’ systems which have been making the headlines in recent medical research.

Dr Tamara Ansons has a background in Cognitive Psychology and works for Ipsos MORI, applying academic research, principally from psychology, to various client-serving projects. In her PhD work, she looked at memory and how it influences decision-making; in the course of that, she investigated neural networks, as a form of representation for how memory stores and uses information.

At our NetIKX seminar for July 2018, she observed that ‘Artificial Intelligence’ is being used across a range of purposes that affect our lives, from mundane to highly significant. Recently, she thinks, the technology has been developing so fast that we have not been stepping back enough to think about the implications properly.

Tamara displayed an amusing image, an array of small photos of round light-brown objects, each one marked with three dark patches. Some were photos of chihuahua puppies, and the others were muffins with three raisins on top! People can easily distinguish between a dog and a muffin, a raisin and an eye or doggy nose. But for a computing system, such tasks are fairly difficult. Given the discrepancy in capability, how confident should we feel about handing over decisions with moral consequences to these machines?

Tamara stated that the ideas behind neural networks have emerged from cognitive psychology, from a belief that how we learn and understand information is through a network of interconnected concepts. She illustrated this with diagrams in which one concept, ‘dog’, was connected to others such as ‘tail’, ‘has fur’, ‘barks’ [but note, there are dogs without fur and dogs that don’t bark]. From a ‘connectionist’ view, our understanding of what a dog is, is based around these features of identity, and how they are represented in our cognitive system. In cognitive psychology, there is a debate between this view and a ‘symbolist’ interpretation, which says that we don’t necessarily abstract from finer feature details, but process information more as a whole.

This connectionist model of mental activity, said Tamara, can be useful in approaching some specialist tasks. Suppose you are developing skill at a task that presents itself to you frequently – putting a tyre on a wheel, gutting fish, sewing a hem, planning wood. We can think of the cognitive system as having component elements that, with practice and through re-inforcement, become more strongly associated with each other, such that one becomes better at doing that task.

Humans tend to have a fairly good task-specific ability. We learn new tasks well, and our performance improves with practice. But does this encapsulate what it means to be intelligent? Human intelligence is not just characterised by ability to do certain tasks well. Tamara argued that what makes humans unique is our adaptability, the ability to learnings from one context and applying them imaginatively to another. And humans don’t have to learn something over many, many trials. We can learn from a single significant event.

An algorithm is a set of rules which specify how certain bits of information are combined in a stepwise process. As an example, Tamara suggested a recipe for baking a cake.

Many algorithms can be represented with a kind of node-link diagram that on one side specifies the inputs, and on the other side the outputs, with intermediate steps between to move from input to output. The output is a weighted aggregate of the information that went into the algorithm.

When we talk about ‘learning’ in the context of such a system – ‘machine learning’ is a common phrase – a feedback or evaluation loop assesses how successful the algorithms are at matching input to acceptable decision; and the system must be able to modify its algorithms to achieve better matches.

Tamara suggests that at a basic level, we must recognise that humans are the ones feeding training data to the neural network system – texts, images, audio etc. The implication is that the accuracy of machine learning is only as good as the data you give it. If all the ‘dog’ pictures we give it are of Jack Russell terriers, it’s going to struggle at identifying a Labrador as a dog. We should also think about the people who develop these systems – they are hardly a model of diversity, and women and ethnic minorities are under-represented. The cognitive biases of the developer community can influence how machine learning systems are trained, what classifications they are asked to apply, and therefore how they work.

If the system is doing something fairly trivial, such as guessing what word you meant to type when you make a keyboarding mistake, there isn’t much to worry about. But what if the system is deciding whether and on what terms to give us insurance, or a bank loan or mortgage? It is critically important that we know how these systems have been developed, and by whom, to ensure that there are no unfair biases at work.

Tamara said that an ‘AI’ system develops its understanding of the world from the explicit input with which it is fed. She suggested that in contrast, humans make decisions, and act, on the basis of myriad influences of which we are not always aware, and often can’t formulate or quantify. Therefore it is unrealistic, she suggests, to expect an AI to achieve a human subtlety and balance in its .

However, there have been some very promising results using AI in certain decision-making contexts, for example, in detecting certain kinds of disease. In some of these applications, it can be argued that the AI system can sidestep the biases, especially the attentional biases, of humans. But there are also cases where companies have allowed algorithms to act in highly inappropriate and insensitive ways towards individuals.

But perhaps the really big issue is that we really don’t understand what is happening inside these networks – certainly, the really ‘deep learning’ networks where the hidden inner layers shift towards a degree of inner complexity which it is beyond our powers to comprehend. This is an aspect which Stephanie would address.
Stephanie Mathieson is the policy manager at ‘Sense About Science’, a small independent campaigning charity based in London. SAS was set up in 2002 as the media was struggling to cope with science-based topics such as genetic modification in farming, and the alleged link between the MMR vaccine and autism.

SAS works with researchers to help them to communicate better with the public, and has published a number of accessible topic guides, such as ‘Making Sense of Nuclear’, ‘Making Sense of Allergies’ and other titles on forensic genetics, chemical stories in the press, radiation, drug safety etc. They also run a campaign called ‘Ask For Evidence’, equipping people to ask questions about ‘scientific’ claims, perhaps by a politician asking for your vote, or a company for your custom.

But Stephanie’s main focus is around their Evidence In Policy work, examining the role of scientific evidence in government policy formation. A recent SAS report surveyed how transparent twelve government departments are about their use of evidence. The focus is not about the quality of evidence, nor the appropriateness of policies, just on being clear what evidence was taken into account in making those decisions, and how. In talking about the use of Artificial Intelligence in decision support, ‘meaningful transparency’ would be the main concern she would raise.

Sense About Science’s work on algorithms started a couple of years ago, following a lecture by Cory Doctorow, the author of the blog Boing Boing, which raised the question of ‘black box’ decision making in people’s lives. Around the same time, similar concerns were being raised by by the independent investigative newsroom ‘ProPublica’, and Cathy O’Neil’s book ‘Weapons of Math Destruction’. The director of Sense About Science urged Stephanie to read that book, and she heartily recommends it.

There are many parliamentary committees which scrutinise the work of government. The House of Commons Science and Technology Committee has an unusually broad remit. They put out an open call to the public, asking for suggestions for enquiry topics, and Stephanie wrote to suggest the role of algorithms in decision-making. Together with seven or eight others, Stephanie was invited to come and give a presentation, and she persuaded the Committee to launch an enquiry on the issue.

The SciTech Committee’s work was disrupted by the 2016 snap general election, but they pursued the topic, and reported in May 2018. (See

Stephanie then treated us to a version of the ‘pitch’ which she gave to the Committee.

An algorithm is really no more than a set of steps carried out sequentially to give a desired outcome. A cooking recipe, directions for how to get to a place, are everyday examples. Algorithms are everywhere, many implemented by machines, whether controlling the operation of a cash machine or placing your phone call. Algorithms are also behind the analysis of huge amounts of data, carrying out tasks that would be beyond the capacity of humans, efficiently and cheaply, and bringing a great deal of benefit to us. They are generally considered to be objective and impartial.

But in reality, there are troubling issues with algorithms. Quite rapidly, and without debate, they have been engaged to make important decisions about our lives. Such a decision would in the past have been made by a human, and though that person might be following a formulaic procedure, at least you can ask a person to explain what they are doing. What is different about computer algorithms is their potential complexity and ability to be applied at scale; which means, if there are biases ingrained in the algorithm, or in the data selected for them to process, those shortcomings will also be applied at scale, blindly, and inscrutably.

  • In education, algorithms have been used to rank teachers, and in some cases, to summarily sack the ‘lower-performing’ ones.
  • Algorithms generate sentencing guidelines in the criminal justice system, where analysis has found that they are stacked against black people.
  • Algorithms are used to determine credit scores, which in turn determine whether you get a loan, a mortgage, a credit card, even a job.
  • There are companies offering to create a credit score for people who don’t have a credit history, by using ‘proxy data’. They do deep data mining, investigate how people use social media, how they buy stuff online, and other evidences.
  • The adverts you get to see on Google and Facebook are determined through a huge algorithmic trading market.
  • For people working for Uber or Deliveroo, their bosses essentially are algorithms.
  • Algorithms help the Government Digital Service to decide what pages to display on the Web site. The significance is, that site is the government’s interface with the public, especially now that individual departments have lost their own Web sites.
  • A recent Government Office for Science report suggests that government is very keen to increase its use of algorithms and Big Data – it calls them ‘data science techniques’ – in deploying resources for health, social care and the emergency services. Algorithms are being used in the fire service to determine which fire stations might be closed.

In China, the government is developing a comprehensive ‘social credit’ system – in truth, a kind of state-run reputation ranking system – where citizens will get merits or demerits for various behaviours. Living in a modestly-sized apartment might add points to your score; paying bills late or posting negative comments online would be penalised. Your score would then determine what resources you will have access to. For example, anyone defaulting on a court-ordered fine will not be allowed to buy first-class rail tickets, or to travel by air, or take a package holiday. That scheme is already in pilots now, and is supposed to be fully rolled out as early as 2020.

(See Wikipedia article at and Wired article at

Stephanie suggested a closer look at the use of algorithms to rank teacher performance. Surely it is better to do so using an unbiased algorithm? This is what happened in the Washington school district in the USA – an example described in some depth in Cathy O’Neil’s book. At the end of the 2009–2010 school year, all teachers were ranked, largely on the basis of a comparison of their pupils’ test scores between one year and the next. On the basis of this assessment, 2% of teachers were summarily dismissed and a further 5% lost their jobs the following year. But what if the algorithms were misconceived, and the teachers thus victimised were not bad teachers?

In this particular case, one of the fired teachers was rated very highly by her pupils and their parents. There was no way that she could work out the basis of the decision; later it emerged that it turned on this consecutive-year test score proxy, which had not taken into account the baseline performance from which those pupils came into her class.

It cannot be a good thing to have such decisions taken by an opaque process not open to scrutiny and criticism. Cathy O’Neil’s examples have been drawn from the USA, but Stephanie is pleased to note that since the Parliamentary Committee started looking at the effects of algorithms, more British examples have been emerging.


  • They are often totally opaque, which makes them unchallengeable. If we don’t know how they are made, how do we know if they are weighted correctly? How do we know if they are fair?
  • Frequently, the decisions turned out by algorithms are not understood by the people who deliver that decision. This may be because a ‘machine learning’ system was involved, such that the intermediate steps between input and output are undiscoverable. Or it may be that the service was bought from a third party. This is what banks do with credit scores – they can tell you Yes or No, they can tell you what your credit score is, but they can’t explain how it was arrived at, and whether the data input was correct.
  • There are things that just can’t be measured with numbers. Consider again that example of teacher rankings; the algorithm just can’t process issues such as how a teacher deals with the difficult issues that pupils bring from their home life, not just the test results.
  • Systems sometimes cannot learn when they are wrong, if there is no mechanism for feedback and course correction.
  • Blind faith in technology can lead to the humans who implement those algorithmically-made decisions failing to take responsibility.
  • The perception that algorithms are unbiased can be unfounded – as Tamara had already explained. When it comes to ‘training’ the system, which data do you include, which do you exclude, and is the data set appropriate? If it was originally collected for another purpose, it may not fit the current one.
  • ‘Success’ can be claimed even when people are having harm done to them. In the public sector, managers may have a sense of problems being ‘fixed’ when teachers are fired. If the objective is to make or save money, and teachers are being fired, and resources saved to be redeployed elsewhere, or profits are being made, it can seem like the model is working. The fact that that objective defined at the start has been met, makes it justify itself. And if we can’t scrutinise or challenge, agree or disagree, we are stuck in that loop.
  • Bias can exist within the data itself. A good example is university admissions, where historical and outdated social norms which we don’t want to see persist, still lurk there. Using historical admissions data as a training data set can entrench bias.
  • Then there is the principle of ‘fairness’. Algorithms consider a slew of statistics, and come out with a probability that someone might be a risky hire, or a bad borrower, or a bad teacher. But is it fair to treat people on the basis of a probability? We have been pooling risk for decades when it comes to insurance cover – as a society we seem happy with that, though we might get annoyed when the premium is decided because of our age rather than our skill in driving. But when sending people to prison, are we happy to tolerate the same level of uncertainty within data? And is past behaviour really a good predictor of future behaviour? Would we as individual be happy to treated on the basis of profiling statistics?
  • Because algorithms are opaque, there is a lot of scope for ‘hokum’. Businesses are employing algorithms; government and its agencies, are buying their services; but if we don’t understand how the decisions are made, there is scope for agencies to be sold these services by snake oil salesmen.

What next?

In the first place, we need to know where algorithms are being used to support decision-making, so we know how to challenge the decision.

When the SciTech committee published its report at the end of May, Stephanie was delighted that they took her suggestion to ask government to publish a list of all public-sector uses of algorithms, and where that use is being planned, where they will affect significant decisions. The Committee also wants government to identify a minister to provide government-wide oversight of such algorithms, where they are being used by the public sector, to co-ordinate departments’ approaches to the development and deployment of algorithms, and such partnerships with the private sector. They also recommended ‘transparency by default’, where algorithms affect the public.

Secondly, we need to ask for the evidence. If we don’t know how these decisions are being made, we don’t know how to challenge them. Whether teacher performance is being ranked, criminals sentenced or services cut, we need to know how those decisions are being made. Organisations should apply standards to their own use of algorithms, and government should be setting the right example. If decision-support algorithms are being used in the public sector, it is so important that people are treated fairly, that someone can be held accountable, and that decisions are transparent, and that hidden prejudice is avoided.

The public sector, because it holds significant datasets, actually holds a lot of power that it doesn’t seem to appreciate. In a couple of cases recently, it’s given data away without demanding transparency in return. A notorious example was the 2016 deal between the Royal Free Hospital and Google DeepMind, to develop algorithms to predict kidney failure, which led to the inappropriate transfer of personal sensitive data.

In the Budget of November 2017, the government announced a new Centre for Data Ethics and Innovation, but it hasn’t really talked about its remit yet. It is consulting on this until September 2018, so maybe by the end of the year we will know something. The SciTech Committee report had lots of strong recommendations for what its remit should be, including evaluation of accountability tools, and examining biases.

The Royal Statistical Society also has a council on data ethics, and the Nuffield Foundation set up a new commission, now the Convention on Data Ethics. Stephanie’s concern is that we now have several different bodies paying attention, but they should all set out their remits to avoid the duplication of work, so we know whose reports to read, and whose recommendations to follow. There needs to be some joined-up thinking, but currently it seems none are listening to each other.

Who might create a clear standard framework for data ethics? Chi Onwurah, the Labour Shadow Minister for Business, Energy and Industrial Strategy, recently said that the role of government is not to regulate every detail, but to set out a vision for the type of society we want, and the principles underlying that. She has also said that we need to debate those principles; once they are clarified, it makes it easier (but not necessarily easy) to have discussions about the standards we need, and how to define them and meet them practically.

Stephanie looks forward to seeing the Government’s response to the Science and Technology Committee’s report – a response which is required by law.

A suggested Code of Conduct came out in late 2016, with five principles for algorithms and their use. They are Responsibility – someone in authority to deal with anything that goes wrong, and in a timely fashion; Explainability – and the new GDPR includes a clause giving a right to explanation, about decisions that have been made about you by algorithms. (Although this is now law, but much will depend on how it is interpreted in the courts.) The remaining three principles are Accuracy, Auditability and Fairness.

So basically, we need to ask questions about the protection of people, and there have to be these points of challenge. Organisations need to ensure mechanisms of recourse, if anything does go wrong, and they should also consider liability. In a recent speakimg engagement on this topic, Stephanie was speaking to a roomful of lawyers, and to them she said, they should not see this as a way to shirk liability, but think about what will happen.

This conversation is at the moment being driven by the autonomous car industry, who are worried about insurance and insurability. When something goes wrong with an algorithm, whose fault might it be? Is it the person who asked for it to be created, and deployed it? The person who designed it? Might something have gone wrong in the Cloud that day, such that a perfectly good algorithm just didn’t work as it was supposed to? ‘People need to get to grips with these liability issues now, otherwise it will be too late, and some individual or group of individuals will get screwed over,’ said Stephanie, ‘while companies try to say that it wasn’t their fault.’

Regulation might not turn out to be the answer. If you do regulate, what do you regulate? The algorithms themselves, similar to the manner in which medicines are scrutinised by the medicines regulator? Or the use of the algorithms? Or the outcomes? Or something else entirely?

Companies like Google, Facebook, Amazon, Microsoft – have they lost the ability to be able to regulate themselves? How are companies regulating themselves? Should companies regulate themselves? Stephanie doesn’t think we can rely on that. Those are some of the questions she put to the audience.

Tamara took back the baton. She noted, we interact extensively with AI though many aspects of our lives. Many jobs that have been thought of as a human preserve, thinking jobs, may become more automated, handled by a computer or neural network. Jobs as we know them now may not be the jobs of the future. Does that mean unemployment, or just a change in the nature of work? It’s likely that in future we will be working side by side with AI on a regular basis. Already, decisions about bank loans, insurance, parole, employment increasingly rely on AI.

As humans, we are used to interacting with each other. How will we interact with non-humans? Specifically, with AI entities? Tamara referenced the famous ‘ELIZA’ experiment conducted 1964–68 by Joseph Weizenbaum, in which a computer program was written to simulate a practitioner of person-centred psychotherapy, communicating with a user via text dialogue. In response to text typed in by the user, the ELIZA program responded with a question, as if trying sympathetically to elicit further explanation or information from the user. This illustrates how we tend to project human qualities onto these non-human systems. (A wealth of other examples are given in Sherry Turkle’s 1984 book, ‘The Second Self’.)

However, sometimes machine/human interactions don’t happen so smoothly. Robotics professor Masahiro Mori studies this in the 1970s, studying people’s reaction to robots made to appear human. Many people responded to such robots with greater warmth as they were made to appear more human, but at a certain point along that transition there was an experience of unease and revulsion which he dubbed the ‘Uncanny Valley’. This is the point when something jarring about the appearance, behaviour or mode of conversation with the artificial human makes you feel uncomfortable and shatters the illusion.

‘Uncanny Valley’ research has been continued since Mori’s original work. It has significance for computer-generated on-screen avatars, and CGI characters in movies. A useful discussion of this phenomenon can be found in the Wikipedia article at

There is a Virtual Personal Assistant service for iOS devices, called ‘Fin’, which Tamara referenced (see Combining an iOS app with a cloud-based computation service, ‘Fin’ avoids some of the risk of Uncanny Valley by interacting purely through voice command and on-screen text response. Is that how people might feel comfortable interacting with an AI? Or would people prefer something that attempts to represent a human presence?

Clare Parry remarked that she had been at an event about care robots, where you don’t get an Uncanny Valley effect because despite a broadly humanoid form, they are obviously robots. Clare also thought that although robots (including autonomous cars) might do bad things, they aren’t going to do the kind of bad things that humans do, and machines do some things better than people do. An autonomous car doesn’t get drunk or suffer from road-rage…

Tamara concluded by observing that our interactions with these systems shapes how we behave. This is not a new thing – we have always been shaped by the systems and the tools that we create. The printing press moved us from an oral/social method of sharing stories, to a more individual experience, which arguably has made us more individualistic as a society. Perhaps our interactions with AI will shape us similarly, and we should stop and think about the implications for society. Will a partnership with AI bring out the best of our humanity, or make us more machine-like?

Tamara would prefer us not to think of Artificial Intelligence as a reified machine system, but of Intelligence Augmented, shifting the focus of discussion onto how these systems can help us flourish. And who are the people that need that help the most? Can we use these systems to deal with the big problems we face, such as poverty, climate change, disease and others? How can we integrate these computational assistances to help us make the best of what makes us human?

There was so much food for thought in the lectures that everyone was happy to talk together in the final discussion and the chat over refreshments that followed.  We could campaign to say, ‘We’ve got to understand the algorithms, we’ve got to have them documented’, but perhaps there are certain kinds of AI practice (such as those involved in medical diagnosis from imaging input) where it is just not going to be possible.

From a blog by Conrad Taylor, June 2018

Some suggested reading



The implications of blockchain for information management

Account of a NetIKX meeting with Marc Stephenson & Noeleen Schenk (Metataxis),
and John Sheridan (The National Archives) — 6 July 2017.

by Conrad Taylor

Blockchain is a technology which was first developed as the technical basis for the cryptocurrency Bitcoin, but there has been recent speculation that it might be useful for various information management purposes too. There is quite a ‘buzz’ around the topic, yet it is too complex for many people to figure out, so it’s not surprising that the 6 July 2017 NetIKX seminar, ‘The implications of Blockchain for KM and IM’, attracted the biggest turnout of the year so far.

This blog post available as a PDF.Image of the PDF of this article, with a link
PDF available for download — This article is also available as a nicely formatted PDF, with some extra notes. Nine pages, 569 KB

The seminar took the form of three presentations, two from the consultancy Metataxis and one from The National Archive. The table group discussions which followed were simply open and unstructured discussions, with a brief period at the end for sharing ideas.

The subject was indeed complex and a lot to take in. In creating this piece I have gone beyond what we were told on the day, done some extra research, and added my own observations. I hope this will make some things clearer, and qualify some of what our speakers said, especially where it comes to technical details.

MARC STEPHENSON gives a technical overview

The first speaker was Marc Stephenson, Technical Director at Metataxis, the information architecture and information management consultancy. In the limited time available, Marc attempted a technical briefing.

Marc’s first point was that it’s not easy to define blockchain. It is not just a technology, but also a concept and a framework for ways of working with records and information; and it has a number of implementations, which differ in significant ways from each other. Marc suggested that, paradoxically, blockchain can be described as ‘powerful and simple’, but also ‘subtle, and difficult to understand’. Even with two technical degrees under his belt, Marc confessed it had taken him a while to get his head around it. I sympathise!

The largest and best-known implementation of blockchain so far is the infrastructure for the digital cryptocurrency ‘Bitcoin’ – so much so that many people get the two confused (and others, in my experience, think that some of the features of Bitcoin are essential to blockchain – I shall be suggesting otherwise).

Wikipedia (at offers this definition:

A blockchain […] is a distributed database that maintains a continuously growing list of ordered records called blocks. Each block contains a timestamp and a link to a previous block. By design, blockchains are inherently resistant to modification of the data — once recorded, the data in a block cannot be altered retroactively. Through the use of a peer-to-peer network and a distributed timestamping server, a blockchain database is managed autonomously… [A blockchain is] an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way. The ledger itself can also be programmed to trigger transactions automatically.

Marc then dug further into this definition, but in a way which left some confused about what is specific to Bitcoin and what are the more generic aspects of blockchain. Here, I have tried to tease these apart.

Distributed database — Marc said that a blockchain is intended to be a massively distributed database, so there may be many complete copies of the blockchain data file on server computers in many organisations, in many countries. The intention is to avoid the situation in which users of the system have to trust a single authority.

I am sceptical as to whether blockchains necessarily require this characteristic of distribution over a peer-to-peer network, but I can see that it is valuable where there are serious issues of trust at stake. As we heard later from The National Archive, it is also possible to create similar distributed ledger systems shared between a smaller number of parties which already trust each other.

Continuously growing chain of unalterable ‘blocks’ — The blockchain database file is a sequential chain divided into ‘blocks’ of data. Indeed, when blockchain was first described by ‘Satoshi Nakamoto’, the pseudonymous creator of the system in 2008, the phrase ‘block chain’ was presented as two separate words. When the database is updated by a new transaction, no part of the existing data structure is overwritten. Instead, a new data block describing the change or changes (in the case of Bitcoin, a bundle of transactions) is appended to the end of the chain, with a link that points back to the penultimate (previous) block; which points back to the previous one; and so on back to the ‘genesis block’.

One consequence of this data structure is that a very active blockchain that’s being modified all the time grows and grows, potentially to monstrous proportions. The blockchain database file that maintains Bitcoin has now grown to 122 gigabytes! Remember, this file doesn’t live on one centralised server, but is duplicated many times across a peer-to-peer network. Therefore, a negative consequence of blockchain could be the enormous expense of computing hardware resources and energy involved in a blockchain system.

(As I shall later explain, there are some peculiar features of Bitcoin which drive its bloat and its massive use of computational resources; for blockchains in general, it ain’t necessarily so.)

Timestamping — when a new block is created at the end of a chain, it receives a timestamp. The Bitcoin ‘timestamp server’ is not a single machine, but a distributed function.

Encryption — According to Marc, all the data in a blockchain is encrypted. More accurately, in cryptocurrency system, crucial parts of the transaction data do get encrypted, so although the contents of the blocks are a matter of public record, it is impossible to work out who was transferring value to whom. (It is also possible to implement a blockchain without any encryption of the main data content.)

Managed autonomously — For Bitcoin, and other cryptocurrencies, the management of the database is done by distributed software, so there is no single entity, person, organisation or country in control.

Verifiable blocks — It’s important to the blockchain concept that all the blocks in the chain can be verified by anyone. For Bitcoin, this record is accessible at the site

Automatically actionable — In some blockchain systems, blocks may contain more than data; at a minimum they can trigger transfers of value between participants, and there are some blockchain implementations – Ethereum being a notable example – which can be programmed to ‘do’ stuff when a certain condition has been met. Because this happens without user control, without mediation, all of the actors can trust the system.

Digging into detail

In this section, I am adding more detail from my own reading around the subject. I find it easiest to start with Bitcoin as the key example of a blockchain, then explore how other implementations vary from it.

‘Satoshi Nakamoto’ created blockchain in the first place to implement Bitcoin as a digital means to hold and exchange value – a currency. And exchange-value is a very simple thing to record, really, whereas using a blockchain to record more complex things such as legal contracts or medical records adds extra problems – I’ll look at that later. Let’s start by explaining Bitcoin.

Alice wants to pay Bob. Alice ‘owns’ five bitcoins – or to put it more accurately, the Bitcoin transaction record verifies that she has an entitlement to that amount of bitcoin value: the ‘coins’ do not have any physical existence. She might have purchased them online with her credit card, from a Bitcoin broker company such as eToro. Now, she wants to transfer some bitcoin value to Bob, who in this story is providing her with something for which he wants payment, and has emailed her an invoice to the value of 1.23 BTC. The invoice contains a ‘Bitcoin address’ – a single-use identifier token, usually a string of 34 alphanumeric characters, representing the destination of the payment.

To initiate this payment, she needs some software called a ‘Bitcoin wallet’. Examples are breadwallet for the iPhone and iPad, or Armory for Mac, Linux and Windows computers. There are also online wallets. Users may think, ‘the wallet is where I store my bitcoins’. More accurately, the wallet stores the digital credentials you need to access the bitcoin values registered in the blockchain ledger against your anonymised identity.

Launching her wallet, Alice enters the amount she wants to send, plus the Bitcoin address provided by Bob, and presses Send.

For security, Alice’s wallet uses public-private key cryptography to append a scrambled digital signature to the resulting message. By keeping her private key secret, Alice is guaranteed that no-one can spoof Bitcoin into thinking that the message was sent to the system by anyone else other than her. The Bitcoin messaging system records neither Alice’s nor Bob’s identity in the data record, other than in deeply encrypted form: an aspect of Bitcoin which has been criticised for its ability to mask criminally-inspired transactions.

At this stage, Alice is initiating no more than a proposal, namely that the Bitcoin blockchain should be altered to show her wallet as that bit ‘emptier’, and Bob’s a bit ‘fuller’. Implementing computers on the network will check to see whether Alice’s digital signature can be verified with her public key, that the address provided by Bob is valid, and that Alice’s account does in fact have enough bitcoin value to support the transaction.

If Alice’s bitcoin transaction proposal is found to be valid and respectable, the transaction can be enacted, by modifying the blockchain database (updating the ledger, if you like). As Marc pointed out, this is done not by changing what is there already, but by adding a new block to the end of the chain. Multiple transactions get bundled together into one Bitcoin block, and the process is dynamically managed by the Bitcoin server network to permit the generation of just one new such block approximately every ten minutes – for peculiar reasons I shall later explain.

Making a block: the role of the ‘hash’

The blocks are generated by special participating servers in the Bitcoin network, which are called ‘miners’ because they get automatically rewarded for the work they do by having some new Bitcoin value allocated to them.

In the process of making a block to add to the Bitcoin blockchain, the first step is to gather up the pending transaction records, which are placed into the body of the new block. These transaction records themselves are not encrypted, though the identities of senders and receivers are. I have heard people say that the whole blockchain is irreversably encrypted, but if you think about it for a second, this has to be nonsense. If the records were rendered uninspectable, the blockchain would be useless as a record-keeping system!

However, the block as a whole, and beyond that the blockchain, has to be protected from accidental or malicious alteration. To do this, the transaction data is put through a process called ‘cryptographic hashing’. Hashing is a well-established computing process which feeds an arbitrarily large amount of data (the ‘input’ or ‘message’) through a precisely defined algorithmic process, which reduces it down to a fixed-length string of digits (the ‘hash’). The hashing algorithm used by Bitcoin is SHA-256, created by the US National Security Agency and put into the public domain.

By way of example, I used the facility at to make an SHA-256 hash of everything in this article up to the end of the last paragraph (in previous edits, I should add; I’ve made changes since). I got 9F0B 653D 4E6E 7323 4E03 B04C F246 4517 8A96 DFF1 7AA1 DA1B F146 6E1D 27B0 CA75 (you can ignore the spaces).

The hash string looks kind of random, but it isn’t – it’s ‘deterministic’. Applying the same hashing algorithm to the same data input will always result in the same hash output. But, if the input data were to be modified by even a single character or byte, the resulting hash would come out markedly different.

Note that the hash function is, for all practical purposes, ‘one-way’. That is, going from data to hash is easy, but processing the hash back into the data is impossible: in the case of the example I just provided, so much data has been discarded in the hashing process that no-one receiving just the hash can ever reconstitute the data. It is also theoretically possible, because of the data-winnowing process, that another set of data subjected to the same hashing algorithm could output the same hash, but this is an extremely unlikely occurrence. In the language of Bitcoin, the hashing process is described as ‘collision-resistant’.

The sole purpose of this hashing process is to build a kind of internal certificate, which gets written into a special part of the block called the ‘header’. Here, cryptography is not being used to hide the transaction data, as it might in secret messaging, but to provide a guarantee that the data has not been tampered with.

Joining the hash of the transaction data in the header are some other data, including the current timestamp, and a hash of the header of the preceding block in the chain. These additions are what gives the blockchain its inherent history, for the preceding block also contained a hash of the header of the block before that, and so on down the line to the very first block ever made.

The role of the ‘miner’ in the Bitcoin system

Now, as far as I can tell, there is nothing in principle wrong with having the blockchain-building process run by one trusted computer, with the refreshed blockchain perhaps being broadcast out at intervals and stored redundantly on several servers as a protection against disaster.

But that’s not the way that Bitcoin chose to do things. They wanted the block-writing process to be done in a radically decentralised way, by servers competing against each other on a peer-to-peer network; they also chose to force these competing servers to solve tough puzzles which are computationally very expensive to process. Why?

Because intimately entangled in the way the Bitcoin ecology builds blocks, is the way that new bitcoins are minted; at present the ‘reward’ from the system to a miner-machine for successfully solving the puzzle and making the latest block in the chain is a fee of 12.5 fresh new bitcoins, worth thousands of dollars at current exchange rates. That’s what motivates private companies to invest in mining hardware, and take part in the game.

This reward-for-work scheme is why the specialised computers that participate in the block-building competition are called ‘miners’.

Let’s assume that the miner has got as far through the process as verifying and bundling the transaction data, and has created the hash of the data for the header. At this point the Bitcoin system cooks up a mathematical puzzle based on the hash, which the ‘miner’ system making the block has to solve. These mathematical puzzles (and I cannot enlighten you more about their precise nature, it’s beyond me!) can be solved only by trial and error methods. Across the network, the competing miner servers are grinding away, trying trillions of possible answers, hashing the answers and comparing them to the header hash and the puzzle instructions to see if they’ve got a match.

This consumes a lot of computing power and energy – in 2014, one bitcoin ‘mining farm’ operator, Megabigpower in Washington state USA, estimated that it was costing 240 kilowatt-hours of electricity per bitcoin earned, the equivalent of 16 gallons of petrol. It’s doubtless gone up by now. The hashing power of the machines in the Bitcoin network has surpassed the combined might of the world’s 500 fastest supercomputers! (See ‘What is the Carbon Footprint of a Bitcoin?’ by Danny Bradbury:

When a miner ‘thinks’ it has a correct solution, it broadcasts to the rest of the network and asks other servers to check the result (and thanks to the hash-function check, though solving the problem is hard, checking the result is easy). All the servers which ‘approve’ the solution – strangely, it’s called a ‘nonce’ – will accept the proposed block, now timestamped and with a hash of the previous block’s header included to form the chainlink, and they update their local record of the blockchain accordingly. The successful miner is rewarded with a transaction which earns it a Block Reward, and I think collects some user transaction fees as well.

Because Bitcoin is decentralised, there’s always the possibility that servers will fall out of step, which can cause temporary forks and mismatches at the most recent end of the blockchain, across the network (‘loose ends’, you might call them). However, the way that each block links to the previous one, plus the timestamping, plus the rule that each node in the network must work with the longest extant version it can find, means that these discrepancies are self-repairing, and the data store is harmonised automatically even though there is no central enforcing agency.

The Bitcoin puzzle-allocation system dynamically adjusts the complexity of the puzzles so that they are being solved globally at a rate of about only six an hour. Thus although there is a kind of ‘arms race’ between competing miners, running on ever faster competing platforms, the puzzles just keep on getting tougher and tougher to crack, and this is what controls the slow increase in the Bitcoin ‘money supply’. Added to this is a process by which the rate of reward for proof-of-work is being slowly decreased over time, which in theory should make bitcoins increasingly valuable, rewarding the people who own them and use them.

As I shall shortly explain, this computationally expensive ‘proof-of-work’ system is not a necessary feature of blockchain per se, and other blockchains use a less expensive ‘proof-of-stake’ system to allocate work.

Disentangling blockchain from Bitcoin

To sum up, in my opinion the essential characteristics of blockchain in general, rather than Bitcoin in particular, are as follows (and compare this with the Wikipedia extract quoted earlier):

  • A blockchain is a data structure which acts as a consultable ledger for recording sequences of facts, statuses, actions or transactions which occur over time. So it is not a database in the sense that a library catalogue is; still less could it be the contents of that library; but the lending records of that library could well be in blockchain form, because they are transactions over time.
  • New data, such as changes of status of persons or objects, are added by appending blocks of re-formed data; each block ‘points’ towards the previous one, and each block also gets a timestamp, so that together the blocks constitute a chain from oldest to newest.
  • The valuable data in the blocks are not necessarily encrypted (contrary to what some people say), so that with the right software, the record is open to inspection.
  • However, a fairly strong form of cryptographic hashing is applied to the data in each block, to generate a kind of internal digital certificate which acts as a guarantee that the data has not become corrupted or maliciously altered. The hash string thus generated is recorded in the head of the block; and the whole head of the block will be hashed and embedded in the head of the following block, meaning that any alteration to a block can be detected.

And I believe we can set aside the following features which are peculiarities of Bitcoin:<

The Bitcoin blockchain is a record of all the transactions which have ever taken place between all of the actors within the Bitcoin universe, which is why it is so giganormous (to coin a word). Blockchains which do not have to record value exchange transactions can be much smaller and non-global in scope – my personal medical record, for example, would need to journal only the experiences of one person.
All the data tracked by the Bitcoin blockchain has to live inside the blockchain; but blockchain systems can also be hybridised by having them store secure and verified links to other data repositories. And that’s a sensible design choice where the entire data bundle contains binary large objects (BLOBs) such as x-rays, scans of land title deeds, audio and video recordings, etc.
The wasteful and computationally expensive ‘proof of work’ test faced by Bitcoin miners is, to my mind, totally unnecessary outside of that kind of cryptocurrency system, and is a burden on the planet.

Marc shows a block

In closing his presentation, Marc displayed a slide image of the beginning of the record of block number 341669 inside the Bitcoin blockchain, from back in February 2015 when the ‘block reward’ for solving a ‘nonce’ was 25 Bitcoins. You can follow this link to examine the whole block on The PDF version of this article contains some screen captures of this online record.

That block carries records of 1,031 transactions, of a value of 1,084 BTC, and it is about 377 KB in size (and remember, these blocks add up!) The transaction record data can be clearly read, even thought it will not make much sense to human eyes because of the anonymisation provided by the encrypted user address of the sender, and the encrypted destination address for the receiver. Thus all we can see that ‘17p3BWzFeqh7DLELpodxt2crQjisvDbC95’ sent 50 BTC to ‘1HEhEpnDhRMUEQSxSWeV3xBoxdSHjfMZJ5’

Other cryptocurrencies, other blockchain methods

Bitcoin has had quite a few imitators; a July 17 article by Joon Ian Wong listed nine other cryptocurrencies – Ethereum, Etherium Classic, Ripple, Litecoin, Dash, NEW, IOTA, Monero and EOS. (Others not mentioned include Namecoin, Primecoin, Nxt, BlackCoin and Peercoin.) That article also points to how unstable the exchange values of cryptocurrencies can be: in a seven-day period in July, several lost over 30% of their dollar values, and $7 billion of their market value was wiped out!

From our point of view, what’s interesting is a couple of variations in how alternative systems are organised. Several of these systems have ditched the ‘proof-of-work’ competition as a way of winning the right to make the next block, in favour of some variant of what’s called ‘proof-of-stake’.

As an example, consider Nxt, founded in late 2013 with a crowdsourced donation campaign. A fixed ‘money’ supply of a billion NXT coins was then distributed, in proportion initially to the contributions made; from this point, trading began. Within the Nxt network, the right to ‘forge’ the next block in the transaction record chain is allocated partly on the basis of the amount of the currency a prospective ‘forger’ holds (that’s the Stake element), but also on the basis of a randomising process. Thus the task is allocated to a single machine, rather than being competed for; and without the puzzle-solving element, the amount of compute power and energy required is slight – the forging progess can even run on a smartphone! As for the rewards for ‘playing the game’ and forging the block, the successful block-forger gains the transaction fees.

Marc specifically mentioned Ethereum, founded in 2014–15, the currency of which is called the ‘ether’. In particular he referred to how Ethereum supports ‘Smart Contracts’, which are exchange mechanisms performed by instructions in a scripting language being executed on the Etherium Virtual Machine – not literally a machine, but a distributed computing platform that runs across the network of participating servers. Smart contracts have been explored by the bank UBS as a way of making automated payments to holders of ‘smart bonds’, and a project called The DAO tried to use the Etherium platform to crowdfund venture capital. The scripts can execute conditionally – the Lighthouse project is a crowdfunding service that makes transfers from funders to projects only if the funding campaign target has been met.

Other uses of blockchain distributed ledgers

In October 2015, a feature article in The Economist pointed out that ‘the technology behind bitcoin lets people who do not know or trust each other build a dependable ledger. This has implications far beyond the cryptocurrency.’ One of the areas of application they highlighted was the secure registration of land rights and real estate transactions, and a pioneer in this has been Lantmäteriet, Sweden’s Land Registry organisation.

Establishing a blockchain-based publicly inspectable record about the ownership (and transfer of ownership) of physical properties poses some different problems than those which simply transfer currency. The base records can include scans of signed contracts, digital photos, maps and similar objects. What Lantmäteriet aims to collect in the blockchain are what it dubs ‘fingerprints’ for these digital assets – SHA-256 hashes computed from the digital data. You cannot tell from a fingerprint what a person looks like, but it can still function as a form of identity verification. As a report on the project explains:

‘A purchasing contract for a real estate transaction that is scanned and becomes digital is an example. The hash that is created from the document is unique. For example, if a bank receives a purchasing contract sent via email, the bank can see that the document is correct. The bank takes the document and run the algorithm SHA-256 on the file. The bank can then compare the hash with the hash that is on the list of verification records, assuming that it is available to the bank. The bank can then trust that the document really is the original purchasing contract. If someone sends an incorrect contract, the hash will not match. Despite the fact that email has a low level of security, the bank can feel confident about the authenticity of the document.’

(‘The Land Registry in the blockchain’ —

In the UK, Her Majesty’s Land Registry has started a project called ‘Digital Street’ to investigate using blockchain to allow property ownership changes to to close instantaneously. Greece, Georgia and Honduras have similar projects underway.

In Ghana, there is no reliable nationwide way of registering ownership of land and property, but a nonprofit project called Bitland is drawing up plans for a blockchain-verified process for land surveys, agreements and documentation which – independent of government – will provide people with secure title ( As they point out, inability to prove ownership of land is quite common across Africa, and means that farmers cannot raise bank capital for development by putting up land as security.

Neocapita is a company which is developing Stoneblock as a decentralised blockchain-based registration service for any government-managed information, such as citizen records. They are working in collaboration with the United Nations Development Program, World Vision, and two governments (Afghanistan and Papua New Guinea), initially around providing a transparent record of aid contributions, and land registry.

NOELEEN SCHENK on blockchain and information governance

After Marc Stephenson had given his technical overview of Blockchain, Noeleen Schenk (also of Metataxis) addressed the issue of what these developments may mean for people who work with information and records management, especially where there are issues around governance.

Obviously there is great interest in blockchain in financial markets, securities and the like, but opportunities are also being spotted around securing the integrity of the supply chain and proving provenance. Walmart is working with IBM on a project which would reliably track foodstuffs, from source to shelf. The Bank of Canada is looking towards using blockchain methods to verify customer identities onwards, on the basis that the bank has already gone through identity checks when you opened your account. Someone in the audience pointed out that there are also lots of applications for verified records of identity in the developing world, and Noeleen mentioned that Microsoft and the UN are looking at methods to assist the approximately 150 million people who lack proof of identity.

Google DeepMind Health is looking at using some blockchain-related methods around electronic health records, in a concept called ‘Verifiable Data Audit’ which would automatically record every interaction with patient data (changes, but also access). They argue that health data needn’t be as radically decentralised as in Bitcoin’s system – a federated structure would suffice – nor is proof-of-work an appropriate part of the blockmaking process in this context. The aim is to secure trust in the data record (though ironically, DeepMind’s was recently deemed to have handled 1.6 million Royal Free Hospital patient records inappropriately).

Noeleen referred to the ISO standard on records management, ISO 15489-1, which gives as the characteristics of ‘authoritative records’ – meeting standards for authenticity, reliability, integrity and usability. What has blockchain to offer here?

Well, where a blockchain is managed on a decentralised processing network, one advantage can be distributed processing power, and avoidance of the ‘single point of failure’ problem. The use of cryptographic hashes ensures that the data has not been tampered with, and where encryption is used, it helps secure data against unauthorised access in the first place.

Challenges to be solved

Looking critically at blockchain with an information manager’s eye, Noeleen noticed quite a few challenges, of which I highlight some:

Private blockchains are beginning to make their appearance in various sectors (the Walmart provenance application is a case in point). This raises questions of what happens when different information management systems need to interoperate.
In many information management applications, it is neither necessary nor desirable to have all of the information actually contained within the block (the Lantmäteriet system is a case in point). Bringing blockchain into the picture doesn’t make the problem of inter-relating datasets go away.
Blockchain technology will impact the processes by which information is handled, and people’s roles and responsibilities with that process. Centres of control may give way to radical decentralisation.
There will be legal and regulatory implications, especially where information management systems cross different jurisdictions.
Noeleen has noticed that where people gather (with great enthusiasm) to discuss what blockchain can do, there seems to be very poor awareness amongst them of well-established record-keeping theory, principles, and normal standards of practice. The techies are not thinking about information management requirements enough.
These issues require information professionals to engage with the IT folks, and advocate the incorporation of information and record keeping principles into blockchain projects, and the application of information architectural rigour.


Following Noeleen’s presentation, there were some points raised by the audience. One question was how, where the blockchain points to data held externally, that external data can itself be verified, and how it can be secured against inappropriate access.

Someone made the point that is is possible to set up a ‘crypotographic storage system’ in which the data is itself encrypted on the data server, using well established public-private key encryption methods, and therefore accessible only to those who have access to the appropriate key. As for the record in the blockchain, what that stores could be the data location, plus the cryptographic hash of the data, so that any tampering with the external data would be easy to detect.

What blockchain technology doesn’t protect against, is bad data quality to start with. I’m reminded of a recent case in which it emerged that a sloppy clinical coder had entered a code on a lady’s record, indicating that she had died of Sudden Infant Death Syndrome (happily, she was very much alive). That transaction can never be erased from the blockchain – but it doesn’t stop the record being corrected after.


Blockchain and the Archive: the TNA experience
Our third presentation was from John Sheridan, the Digital Director at The National Archives (TNA), with the title ‘Application of Distributed Ledger Technology’. He promised to explain what kinds of issues the Archive worries about, and where they think blockchains (or distributed ledgers more generally) might help. On the digital side of TNA, they are now looking at three use-cases, which he would describe.

John remarked that the State gathers information ‘in order to make Society legible to it’ – so that it might govern. Perhaps The Domesday Book was one of the world’s first structured datasets, collected so that the Norman rulers might know who owned what across the nation, for taxation purposes. The Archive’s role, on the other hand, is to enable the citizen to see the State, and what the State has recorded, by perusing the record of government (subject to delays).

Much of the ethos of the TNA was set by Sir Hilary Jenkinson, of the Public Record Office (which merged with three other bodies to form TNA in 2003). He was a great contributor to archive theory, and in 1922 wrote A Manual of Archive Administration (text available in various formats from The Internet Archive, TNA still follows his attitude and ideas about how information is appraised and selected, how it is preserved, and what it means to make that information available.

An important part of TNA practice is the Archive Descriptive Inventory – a hierarchical organisation of descriptions for records, in which is captured something of the provenance of the information. ‘It’s sort of magnificent… it kind of works,’ he said, comparing it to a steam locomotive. But it’s not the best solution for the 21st century. It’s therefore rather paradoxical that TNA has been running a functional digital archive with a mindset set that is ‘paper all the way down’ – a straight line of inheritance from Jenkinson, using computers to simulate a paper record.

Towards a second-generation digital archive

It’s time, he said, to move to a second-generation approach to digital archive management; and research into disruptive new technologies is important in this.

For the physical archive, TNA practice has been more or less to keep everything that is passed to it. That stuff is already in a form that they can preserve (in a box), and that they can present (only eyes required, and maybe reading spectacles). But for the digital archive, they have to make decisions against a much more complex risk landscape; and with each generation of technological change, there is a change in the digital preservation risks. TNA is having to become far more active in making decisions about what evidences the future may want to have access to; and, which risks they will seek to mitigate, and which ones they won’t.

They have decided that one of the most important things TNA must do, is to provide evidence for purposes of trust – not only in the collection they end up with, but also in the choices that they have made in respect of that collection. Blockchain offers part of that solution, because it can ‘timestamp’ a hash of the digital archive asset (even if they can’t yet show it to the public), and thereby offer the public an assurance, when the archive data is finally released, that it hasn’t been altered in the meantime.

Some other aims TNA has in respect of the digital archive is being more fluid about how an asset’s context is described; dealing with uncertainties in provenance, such as about when a record was created; and permitting a more sophisticated, perhaps graduated form of public access, rather than just now-you-can’t-see-it, now-you-can. (They can’t simply dump everything on the Web – there are considerations of privacy, of the law of defamation, of intellectual property and more besides.)

The Archangel project

Archangel is a brand new project in which TNA is engaged together with the University of Surrey’s Centre for the Digital Economy and the Open Data Institute. It is one of seven projects which EPSRC is funding to look at different contexts of use for distributed ledger technology. Archangel is focused specifically on public digital archives, and they will try to work with a group of other memory institutions.

The Archangel project will not be using the blockchain methods which Marc had outlined. Apparently, they have their own distributed ledger technology (DLT), with ‘permissioned’ access.

The first use-case, which will occupy them for the first six months, will focus on a wide variety of types of research data held by universities: they want to see if they can produce sets of hashes for such data, such that at a later date when the findings of the research are published, and the data is potentially archived, any question of whether the data has been tampered with or manipulated can be dealt with by cryptographic assurance spread across a group of participating institutions. (The so-called ‘Climategate’ furore comes to mind.)

The second use-case is for a more complex kind of digital object. For example, TNA preserves the video record of proceedings of The Supreme Court. In raw form, one such digital video file could weigh in at over a terabyte! Digital video transcoding methods, including compression algorithms, are changing at a rapid pace, so that in a decade’s time it’s likely that the digital object provided to the public will have to have been converted to a different file format. How is it possible to create a crypographic hash for something so large? And is there some way of hashing not the bit sequence, but the informational content in the video?

It’s also fascinating to speculate about how machines in future might be able to interpret the informational content in a video. At the moment, a machine can’t interpret the meaning in someone’s facial expressions – but maybe in the future?

For this, they’ll be working with academics who specialise in digital signal processing. They are also starting to face similar questions with ‘digital surrogates’ – digital representations of an analogue object.

The third use case is about Deep Time. Most people experimenting with blockchain have a relatively short timescale over which a record needs to be kept in verifiable form, but the aspirations of a national archive must looks to hundreds, maybe thousands of years.

Another important aspect of the Archangel project is the collaboration which is being sought between memory institutions, which might reach out to each other in a concerted effort to underscore trust in each others’ collections. On a world scale this is important because there are archives and collections at significant risk – in some places, for example, people will turn up with Kalashnikovs to destroy evidence of human rights abuses.

Discussions and some closing thoughts

TABLE-GROUP DISCUSSIONS: NetIKX meetings typically feature a ‘second half’ which is made up of table-group discussions or exercises, followed by a summing-up plenary discussion. However, the speakers had not organised any focused discussion topics, and certainly the group I was in had a fairly rambling discussion trying to get to grips with the complexity and novelty of the subject. Likewise, there was not much ‘meat’ that emerged in the ten minutes or so of summing up.

One suggestion from Rob Begley, who is doing some research into blockchain, was that we might benefit from reading Dave Birch’s thoughts on the topic – see his Web site at However, it’s to be borne in mind that Birch comes at the topic from a background in electronic payments and transactions.

MY OWN CLOSING THOUGHTS: There is a lot of excitement – one might say hype – around blockchain. As Noeleen put it, in the various events on blockchain she had attended, a common attitude seems to be ‘The answer is blockchain! Now, what was the problem?’ As she also wisely observed, the major focus seems to be on technology and cryptocurrency, and the principles of information and records management scarcely get a look-in.

The value of blockchain methods seem to centre chiefly on questions of trust, using a cryptographic hashing and a decentralised ledger system to create a hard-to-subvert timestamped record of transactions between people. The transactional data could be about money (and there are those who suggest it is the way forward for extending banking services in the developing world); the application to land and property registration is also very promising.

Another possible application I’m interested in could be around ‘time banking’, a variant of alternative currency. For example in Japan, there is a scheme called ‘Fureai Kippu’ (the ‘caring relationship ticket’) which was founded in 1995 by the Sawayaka Welfare Foundation as a trading scheme in which the basic unit of account is an hour of service to an elderly person who needs help. Sometimes seniors help each other and earn credits that way, sometimes younger people work for credits and transfer them to elderly relatives who live elsewhere, and some people accumulate the credits themselves against a time in later life when they will need help. It strikes me that time-banking might be an interesting and useful application of blockchain – though Fureai Kippu seems to get on fine without it.

When it comes to information-management applications which are non-transactional, and which involve large volumes of data, a blockchain system itself cannot cope: the record would soon become impossibly huge. External data stores will be needed, to which a blockchain record must ‘point’. The hybrid direction being taken by Sweden’s Lantmäteriet, and the Archangel project, seems more promising.

As for the event’s title ‘ The implications of Blockchain for KM and IM’ — my impression is that blockchain offers nothing to the craft of knowledge management, other than perhaps to curate information gathered in the process.

Some reading suggestions

Four industries blockchain will disrupt
‘Two billion people lack access to a bank account. Here are 3 ways blockchain can help them’
TED talk, Don Tapscott on how the blockchain is changing money and business
Why Ethereum holds so much promise
Wikipedia also has many valuable articles about blockchain, cryptographic hashing, etc

Blog for the June 2018 seminar in Leeds.

In June 2018, NetIKX held a seminar led by NetIKX in cooperation with ISKO UK.  We were proud to have a meeting outside London to offer more to our members.  Our main speaker, Ewan David, Talked about a particular aspect of the electronic medical records system.  He hoped that future development in this area would be based on open standards.  Conrad Taylor contributed an interesting overview of background information highlighting that information and knowledge are central to the practice of medicine.  This means that for modern medicine there is pressure to use digital systems to improve patient care and increase knowledge sharing.  But the application of computers to health care and patient records is complex, involving as it does confidential patient records.

Ewan David has been active in health informatics for over 35 years. He is an independent consultant, both with NHS bodies as well as on the industrial provider side. He has also been the chair of the British Computer Society Primary Healthcare Group.   He is now CEO of Inidus a new company committed to delivering a secure cloud-based platform for health and social care applications.  He advocates an approach that seeks to end vendor lock-in in order to liberate data.

Digital technologies have delivered transformational change in banking, finance, travel and retail, do there appears to be a big opportunity to the same for healthcare.  However progress so far has resulted in data silos.  The GP practice has a system; the hospital site could have many systems, storing data in proprietary formats making it difficult to share data between them.  Once systems are in place there is a heavy penalty in terms of time and focus that makes change unlikely.  The vendor market for big hospital system is dominated by four American companies.  The picture is similar in the pharmacy sector, and also maternity systems.  There has been no significant new entrant to the UK digital health market for 25 years.  As a result there is very little innovation and this blocks transformational change.  The technology and business models are locked into the last century and there is little motivation to change.

Ewan believes there is a need to move to Open Platforms.  This would make the data that healthcare applications need available in an open, computable, shareable format.  The information needed is data about an individual patient, medical knowledge and information about resources available to call on.  What any clinician does, and therefore what supporting applications need to do, is to combine these kinds of information so that the patient’s health issues can be diagnosed, and a course of action chosen within the constraints of the resources available.

There are barriers to entry to the healthcare technology market – regulatory barriers and issues of privacy and clinical safety.  The commercial environment is difficult for new entrants to the market.  Using open platforms could open up to new suppliers as you work with a vendor neutral information model and clear standards that any application will comply with.  This allows purchasers to move between vendors without the need to transform the underlying data.   Some experiments have been done so far.  One by Moscow City Council and another with Leeds in the UK. Using open standards has allowed more involvement from the people who know healthcare intimately – the practitioners. The benefits of the system is that work can be done on limited areas and then combined rather than producing an overarching system that attempts to do everything.  Components of the system can then be changed much more easily, removing one of the major barriers to innovation.

After this important talk, we moved to discussion which was lively and enthusiastic.  The discussion ranged from how patients could be involved in producing appropriate records, some of the useful innovations recently seen in healthcare systems and the relevance of anonymised research data.  We considered the road that would be needed to move towards more flexible and appropriate systems.   Ewan summed up the successful seminar by reiterating that a more open system is what is needed and that most in the health service agree it makes sense.  However, it is likely to be a slow process to persuade the major vendors to commit to progressively opening up the data.  But hopefully commissioners will have some leverage over the vendors and change will happen.

This blog is an extract from a report by Conrad Taylor.
For the full report please follow this link:  Organising Medical and Health Related Information

Titus Oates claims that there is a Catholic plot against the King’s life

Trust and Integrity in Information

Trust and Integrity in Information

An account by Conrad Taylor of the May 2018 meeting of the Network for Information and Knowledge Exchange. Speakers — Hanna Chalmers of Ipsos MORI, Dr Brennan Jacoby of Philosophy at Work, and Conrad Taylor.

Titus Oates reveals the Popish Plot to Charles II

Fake News 1688: the ‘Popish Plot’. Titus Oates ‘revealing’ to King Charles II his totally fabricated tale of a plot to assassinate the monarch: many accused were executed.
(Listen to BBC’s ‘In Our Time’ podcast.)


In the last couple of years there has been much unease about whether the news, information and opinions we find in the media can be trusted. This applies not only to the established print and broadcast media, but also the new digital media – all further echoed and amplified, or undermined, by postings, sharing, comments and trollings on social media platforms.

In the last two years, as news channels were dominated by a divisive US presidential election, and the referendum on whether Britain should leave the EU, various organisations concerned with knowledge and information have been sitting up and paying attention – in Britain, led by the Chartered Institute of Library and Information Professionals (CILIP), and the UK chapter of the International Society for Knowledge Organization (ISKO UK). The Committee of NetIKX also determined to address this issue, and so organised this afternoon seminar.

The postmodern relativism of the 1980s seems back to haunt us; the concept of expertise has been openly rubbished by politicians. Nevertheless, as information and knowledge professionals, we still tend to operate with the assumption that there are objective truths out there. Taking decisions on the basis of true facts is something we value – whether for managing our personal well-being, or contributing to democratic decision-making.
Before this seminar was given its title, the Committee referred to it as being about the problem of ‘fake news’. But as we put it together, it became more nuanced, with two complementary halves. The first half, curated by Aynsley Taylor, focused on measuring people’s trust in various kinds of media, and what this ‘trust’ thing is anyway. The second half, which I curated and included a game-like group discussion exercise, looked at causes and symptoms of misinformation in the media, and how (and with whom) we might check facts.

Ipsos MORI: a global study of trust in media

Our first speaker was Hanna Chalmers of Ipsos MORI, a global firm known to the UK public for political polling, but which has as its core business helping firms to develop viable products, testing customer expectation and experience, and doing research for government and the public sector. Hanna is a media specialist, having previously worked at the BBC, and as Head of Research at Universal Music, before switching to the agency side.
Hanna presented a ‘sneak preview’, pre-publication, of Ipsos MORI research into people’s opinions about the trustability of different forms of media. This 26-country global study had 27,000 survey respondents, and encompassed most developed markets. The company put up its own money for this, to better inform conversations with clients, and to test at scale some hypotheses they had developed internally. Hanna warned us not to regard the results as definitive; Ipsos MORI sees this as the first iteration of an ongoing enquiry, but already providing food for thought.

Issues of trust in media formerly had a low profile for commerce, but is now having an impact on many of Ipsos MORI’s clients. (Even if a company has no political stance of its own, it has good reason not to be seen advertising in or otherwise supporting media sources popularly perceived as ‘toxic brands’.)

The study’s headline findings suggest that the ‘crisis of trust in the media’ that commentators warn about may not be as comprehensive and universal as is thought. However, in the larger and more established economies, a significant proportion of respondents claim that their trust in media has declined over the last five years.

Defining ‘trust’

Trust, said Hanna, is a part of almost every interaction in everyday life. (If you buy a chicken from a supermarket, for example, you trust it has been handled properly along the supply chain.) However, what trust actually means in any given circumstance is highly dependent on context.

The Ipsos MORI team chose this working definition: Trust broadly characterises a feeling of reasonable confidence in our ability to predict behaviour. They identified two elements for further exploration, based on the ideas of Stephen MR Covey, an American author.

1. Is the action committed with a good intention? Does the other party act with our best interests at heart? In the case of a news media outlet, that would imply them acting with integrity, working towards an error-free depiction of events. However, the definition of ‘best interest’ is nowadays contentious. Many people seek news sources that reflect their own point of view, rejecting what is counter to their opinions.

2. Does the other party reliably meet their obligations? In the case of media, defining obligations is not easy. Not all media outlets aim to provide an objective serving of facts; many are undoubtedly partisan. Within new media, much blog content is opinion presented as fact; where sources are cited, they are often unreliable. The news media world is pervaded by a mix of reportage, opinion and advertising, re-written PR and spin, making media more difficult to trust than other spheres of discourse.

Why is trust in media so precarious?

Hanna invited the audience to offer possible answers to this; we responded:

  • When we read a story in the news, how do we know if it is true? How can we check?
  • The Web has lowered the barrier to spreading narratives and opinions. More content is being presented without going through some editorial ‘gatekeeping’ process.
  • There are powerful individuals and interests who want us to distrust the media – fostering public distrust in journalism is advantageous to them.
  • It’s a problem that the media uses its own definition of trust.
  • Personal ‘confirmation bias’ – where people trust narrators whose opinions, beliefs, values and outlooks they share.
  • The trend towards 24-hour news, and other pressures, mean that news gets rushed out without adequate fact-checking, and stripped of context.

And let’s not blame only the media. Hanna cited a 2015 study by Columbia University and the French National Institute, which found that in 59% of instances of link-sharing on social media (e.g. Facebook), the sharer had not clicked through to check out the content of the link. (See Washington Post story in The Independent, 16 June 2016.)

How the survey worked

As already described, the survey engaged in January 2018 with 27,000 people, across 26 countries, and asked about their levels of trust in the media. The sample sets were organised to be nationally representative of age, gender and educational attainment.

The questions asked included:

  • To what extent, if at all, do you trust each of the following to be
    a reliable source of news and information?

    [See below for explanation of what ‘the following’ were.]
  • How good would you say each of the following is at providing
    news and information that is relevant to you?
  • To what extent, if at all, do you think each of the following acts
    with good intentions in providing you with news and information?
  • How much, if at all, would you say your level of trust in the following
    has changed in the past five years?
  • How prevalent, if at all, would you say that ‘fake news’ is in
    the news and information provided to you by each of the following?

    (This was accompanied by a definition of ‘fake news’ as ‘often sensational
    information disguised as factual news reporting’.)

‘The following’ were, for each of these questions, five different classes of information source – (a) newspapers as a class, (b) social media as a class, (c) radio television as a class, (d) people we know in real life, and (e) people whom we know only through the Internet.

(In response to questions from the audience, Hanna explained that to break it down to an assessment of trust in particular ‘titles’, e.g. trust in RT vs BBC, or trust in The Guardian vs The Daily Express, would have been too complicated. It would have also made inter-country comparisons impossible.)

In parallel, the team conducted a literature review of other studies of trust in the media.

Hanna’s observations

Perhaps the decline in trust in advanced economies is because the recent proliferation of media channels (satellite TV, social media, news websites, online search and reference sources) means we have a broader swathe of resources for fact-checking, and which expose us to alternative narratives. That doesn’t necessarily mean we trust these alternatives, but awareness of a disparity of narratives may drive greater scepticism.

But driving in the other direction, the rise of social media magnifies the ‘echo chamber’ phenomenon where people cluster around entrenched positions, consider alternative narratives to be untruths, and social polarisation increases.

With the proliferation of media channels, competition for eyes and ears, and a scramble to secure advertising revenue, even long-established media outlets are trying to do more with fewer people – and making mistakes in the process. Social media helps those mistakes and inaccuracies take on lives of their own, before they can be corrected.

‘There is a propensity for consumers to skewer brands that mess up, and remember it’ said Hanna. ‘But it also leads to less than ideal shows of transparency [by brands] after mistakes happen.’ As an example, she mentioned the Equifax credit-rating agency’s data breach of May–July 2017, when personal details of 140+ million people were hacked. It took months for Equifax to come clean about it.

Why is there more trust in the media from people with higher levels of education? Hanna suggested it may be because they are more confident in their ability to discriminate and evaluate between news sources. (Which is paradoxical, in a way, if ‘greater trust in media’ equates to ‘more critical consumption of media’ – something we later explored in discussion.)

Trust, however, remains fairly robust overall, especially in print media, and big broadcast sources such as TV and radio. The category which Ipsos MORI labelled as ‘online websites’ was trusted markedly less. (For them, this label means news and information sites not linked to a traditional publishing model – thus the ‘BBC News’ website would not be counted by Ipsos MORI as an ‘online website’.)

Carrying the study forward

Ipsos MORI wants to carry this work forward, and has set up a global workstream for it. Meanwhile, what might the media themselves take away from this study? Hanna offered these thoughts:

  • Media should trust their their audiences, and be transparent about mistakes and clarifications. This does not happen enough – and it applies to advertisers as well. They forget that we are able to check facts, and are more media-savvy and better educated and sceptical than in the past.
  • Media needs to be more transparent about its funding models. It was clear, when Mark Zuckerberg was being questioned by American and EU legislators, that many had no idea about how Facebook makes its money.
  • Editorial and distribution teams would benefit from greater diversity. That would put more points of view in the newsroom.

In closing, Hanna quoted the American sociologist Ronald S Burt: ‘The question is not whether to trust, but who to trust’. Restoring equilibrium and strengthening trust in the media is important for democracy. She suggested that media owners and communicators need to take responsibility for the accuracy and trustability of their communications.

Questions and comments for Hanna

One audience member wondered if differing levels of trust had shown up across the gender divide. Hanna replied, across the world women display a little more trust – but it’s a smaller differential than that linked to educational attainment.

Several people expressed surprise at greater educational attainment correlating with greater trust in media – surely those better educated are more likely to be cynical (or more kindly, ‘critical’)? Claire Parry pointed out that more educated people are also statistically more likely to work in the media (or know someone who does).

But someone else suggested that the paradox is resolved if we consider that more educated people may tend more firmly to discriminate between particular publications, broadcasters and online news sources, and follow ones they trust while ignoring others. If such a person is asked, ‘how much do you trust newspapers’ and they interpret that question as ‘how much do you trust the newspapers that you yourself read’, they are more likely to answer positively. How questions are understood and reacted to by different people is, of course, a major vulnerability of survey methodologies.

This leads on to an issue which David Penfold raised, and which has been on my mind too. Is there validity in asking people how much they trust a whole category of media, when there are such huge discrepancies in quality of trustworthiness within each category?

I would certainly not be able to answer this survey. If you ask me about trusting print media, I would come back with ‘Do you mean like The Guardian or like The Daily Express or The Daily Mail? Do you mean like Scientific American or The National Enquirer?’ To lump them together and ask me to judge the trustability of a whole category feels absurd to me. Likewise, there are online information sources which I find very trustworthy, while others are execrable. Even on Facebook, I have ‘online-only friends’ who reliably point me towards science-backed information, and I have grown to trust them, while others are entertaining but purvey a lot of nonsense.

Hanna remarked that the whole project is crying out for qualitative research, to which Ainsley added ‘If someone will pay for it!’ Traditional forms of qualitative research (interviews, focus groups) are indeed expensive, but perhaps the micronarrative-plus-signifiers approach embodied in SenseMaker methodology could be tackle these questions. This can scale to find patterns in vast arrays of input, cost-effectively, and can be deployed to track ongoing trends over time. (We got a taste of how that works from Tony Quinlan at the March 2018 NetIKX meeting).

A further caveat was put forward by by David Penfold: just because a source of news and opinion is trusted, it doesn’t mean it’s right. A lot of people trusted The Daily Mail in the 1930s, when it was preaching support for Hitler and promoting anti-semitic views.

Dave Clarke thought that the survey insights were valuable; it was good to see so much quantitative data. He offered to connect the Ipsos MORI team with people he has been working with in the ‘Post-Truth’ area (of which we would hear more later that afternoon).

Martin Fowkes wondered about comparisons between very different countries and media environments. In the UK we can sample a wide spectrum of political news, but in some countries the public is fed a line supporting the leadership’s political agenda. In such conditions, if you ask these poll questions, people may ‘game and gift’ their responses, playing safe. Hanna acknowledged that problem, and suggested that each separate country could be a study in itself.

Aynsley and Hanna agreed with Dion Lindsay that this project was in the nature of a loss-leader, which might help their market to show more interest in funding further research. Also, it is important to Ipsos MORI to be able to demonstrate thought leadership to its client base through such work.


Brennan Jacoby on the philosophical basis of trust

Dr Brennan Jacoby

Dr Brennan Jacoby FRSA is the founder and principal of the consultancy Philosophy at Work

Aynsley then introduced Dr Brennan Jacoby, whom he first saw speaking about trust at the Henley Business Centre. A philosopher by trade, Brennan would unpick what trust actually might mean.

Brennan explained that his own investigations into the concept of trust started while he was doing his doctoral work on betrayal (resulting in ‘Trust and Betrayal: a conceptual analysis’, Macquarie University 2011. Much discussion in the literature about trust contrasts trust with betrayal, but fails to define the ‘trust’ concept in the first place. In 2008, Brennan started his consulting practice ‘Philosophy at Work’. Trust was the initial focus, and remains a strong element of his work with organisations.

Brennan asked each of us to think of a brand we consider trustworthy – it could be a media brand, but not necessarily. We came up with quite a variety! – cBeebies, NHS, Nikon, John Lewis…

He told us that one time when he tried this exercise, someone shouted ‘RyanAir!’ She then explained that all RyanAir promise to do is to get you from A to B as cheaply as possible – and that they do. It seems a telling example, illustrating a breadth of interpretations around what it means to be trustworthy (is it just predictability, or is it something more?)

Critiquing the Trust Barometer

Edelman is an American public relations firm. Over the last 18 years it has published an annual ‘Trust Barometer’ report (see the current one at, which claims to measure trust around the world in government, NGOs, business, media and leaders.

(Conrad notes: there is some irony, in that Edelman has in the past acted to deflect antitrust action against Microsoft, created a fake front group of ‘citizens’ to improve WalMart’s reputation, and worked to deflect public disapproval of News Corporation’s phone hacking, oil company pollution and the Keystone XL pipeline project, amongst others.)

In the Trust Barometer 2018 report, they chose to separate ‘journalism’ from ‘media outlets’ for the first time, reflecting a growing perception that those information sources which are social platforms, such as Facebook, have been ‘hijacked’ by different causes and viewpoints and have become untrustworthy, while professional journalists may still be considered worthy of trust.

It’s interesting to see how Edelman actually asked their polling question. It went: ‘When looking for general news and information, how much would you trust each type of source for general news and information?’, followed by a list of sources, and a nine-point scale against each. Again, this survey fails to define what trust is. If we think about to the Covey definition cited by Hanna, a respondent might say, ‘Yes I trust journalists [because I think they are competent to deliver the facts]’; another respondent might say, ‘yes, I trust journalists [because I think they have good intentions].’ Someone might also say, ‘Well, I have to trust journalists, because in my country I have no choice.’

A philosophy of trust

The role of philosophy in society, said Brennan, should be to solve problems and be practical. Conceptual work isn’t merely of academic interest, but can make key distinctions which can suggest ways forward. So let’s consider the concepts of trust, trustworthiness, and finally distrust.

The word Trust can connote a spread of meanings. There’s trust in individuals, whom we meet face to face, but also those we will never meet; we may consider trust in organisations, in machinery and artefacts, or in artificial intelligence. This diversity of application may be why many conversations around trust shy away from more specificity. But a lack of specificity leaves us unable to distinguish trust from other things.

Trust may be distinguished from mere reliance. The philosophical literature agrees by and large that trust is a kind of reliance, but not just ‘mere reliance’. As an American, Brennan has no choice but to rely on Donald Trump as President – you might say count on him – given that he (Brennan) doesn’t have access to the same information and power. But Brennan doesn’t trust him. Or suppose at work you need to delegate a responsibility to someone new to the role. You have to rely on the person, but you are not quite sure you can trust them.

Special vulnerability. What distinguishes trust from mere reliance is a special kind of vulnerability. To set the scene for a thought experiment, Brennan told a story about the German philosopher Immanuel Kant (1724–1804). He was known for being obsessive about detail. The story goes that as he took his regular walk around town, the townsfolk would set the time on their clocks by the regularity of his appearance. Imagine that one day Kant sleeps in, and that day the townsfolk don’t know what time it is. They might feel annoyed, but would they feel betrayed by him? Probably not.

But now, suppose there is a town hall meeting where the citizens discuss how to be sure of the time, and Kant says, ‘Well, I take a walk at the same time each day, so you can set your clocks by me!’ But suppose one day he sleeps in or decides not to go for his walk. Now the citizens might feel let down, even betrayed. Because of Kant’s offer at the town meeting, they are not just ‘counting on’ him, they are ‘trusting’ him. They thought they had an understanding with him, which set up their expectations in a way they didn’t have before. They may say, ‘We don’t just expect that Kant will walk by at a regular time – we think he ought to.’ There is a distinction here between a predictive expectation, and what we could call a normative one.

Trust = optimistic acceptance of special vulnerability

So, Brennan suggests, we should think about trust as an acceptance of vulnerability; or more precisely, an optimistic acceptance of a special vulnerability. An ordinary kind of vulnerability might be like being vulnerable to being knocked down while crossing the road, or being caught in a rain-shower. This special vulnerability, which is the indicator of trust, is vulnerability to being betrayed by someone in a way that does us harm. There is a moral aspect to this kind of vulnerability, tied up in agreements and expectations.

Regarding the ‘optimism’ factor – suppose you need to access news from a single source, because you live in a country where the media is controlled by the State. That makes you vulnerable to whether or not you are being told the truth. You may say, ‘Well, it’s my nation’s TV station, I have to count on them.’ But suppose you have travelled to other countries and seen how differently things are arranged abroad, you may not be very optimistic about that reliance.

To sum up: Trust is when we optimistically accept our vulnerability in relying on someone.

Trust is not always a good thing!

Brennan showed a picture of a gang of criminals in New South Wales who had holed up in a house together and stayed hidden from the police, until one went to the police and betrayed the others. Did he do good or bad? Consider whistleblowing, where it can be morally positive, or there is good reason, to be distrustful or ‘treacherous’. Trust, after all, can enable abuses of power. Perhaps we should not be getting too flustered about an alleged ‘crisis of trust’ – perhaps it would not be a bad thing if trust ebbs away somewhat – because to be wary of trusting may be rational and positive.

Brennan notes, people may be thinking ‘Hey, if we are not going to trust anyone or anything, we’re not going to make it out the front door!’ But that’s only true if we think reliance and trust are exactly the same. Separating those concepts allows to get on with our lives, while retaining a healthy level of wariness and scepticism.

Baroness Onora O’Neill speaking about trust and distrust
at a TEDx event at the Houses of Parliament in June 2013.


Brennan recommended reading or listening to Baroness Onora O’Neill, an Emeritus Professor of the University of Cambridge who has written and spoken extensively on political philosophy and ethics, using a constructivist interpretation of the ethics of Kant. O’Neill places great emphasis on the importance of trust, consent, and respect for autonomy in a just society. Brennan told us that she gave a TED talk some years ago (2013), in which she argued that we should aim for appropriately placed trust (and appropriately placed distrust).
See talk video at


When trust is appropriately placed, usually it is because it is placed in someone who is (or at least, is perceived to be) ‘trustworthy’. So what does that mean?Three things are important for trustworthiness, said Brennan; they relate quite well to Stephen MR Covey’s two points.

Competence — As the Australian moral philosopher Dr Karen Jones puts it, ‘the incompetent deserve our trust almost as little as the malicious.’ But in the sphere of media, a further distinction is useful – between technical competence and practical competence. Technical competence is the ability to do the thing that someone is counting on us for – so, will Facebook not give our details to a third party? If we expect them to prevent that, and they know that, are they competent to do so? Practical competence is, further, the ability to track the remit, to be on the same page as what one is being counted on to do.

Suppose you are away travelling, and you ask someone to look after your house while you are away. You may feel confident that they are technically competent to check on security, feed the cat, etc. You probably don’t think you need to leave a note saying ‘Please don’t paint the bathroom.’ You take it for granted that they know what it means to be a house-sitter. If you come back and find the whole place redecorated, even if you love the result, you’re not going to ask them to house-sit again.

This analogy and analysis is important in Facebook’s situation, because there has been a disconnect about what the parties are expecting. It would seem Facebook saw their relationship with us to be different from what we would have assumed. Perhaps the solution is to have a more explicit conversation about expectations.

Dion asked if these conditions of competence are not more to do with reliability than trust, and Brennan agreed. They are the preconditions for trustworthiness, but they are not sufficient.

Integrity of character — this is where the full definition of trustworthiness comes in. Reliability is all one may hope for from an animal, or a machine. Trust further involves the acceptance of a moral responsibility and commitment. Linking back to previous discussion, Brennan said that trust is a relationship that can be had only between members of ‘the moral community’. Reliability is what we expect from an autonomous vehicle; trust is what we might extend to its programmers. And programmers may be deemed to be trustworthy (or not), because they can have Character.

So if we have a media source competent at its job, and committed to doing it, we can so far only rely on them to do what we think they will always do. That is not enough to elicit trust. Assessing trustworthiness involves assessment of moral values, and integrity of character.

How do we assess ‘good character’? Many people are likely to ascribe that value to people like themselves, with whom they share an understanding of the right thing to do. We expect others to do certain things, but adding the factor of obligation clarifies things. For example, we might predict that hospitals will keep missing care targets; but additionally we expect that hospitals ought to care and not kill: this is the constitutive expectation which governs the relationship between users and services.

Brennan noted something unusual (and valuable) about how Mark Zuckerberg apologised after the recent Cambridge Analytica scandal. When most companies screw up, they apologise in a manner that responds to predictive expectations (‘we promise not to miss-sell loans again’, ‘we will never again use flammable cladding on residential buildings’). Zuckerberg’s apology said – ‘Look, sorry, we were wrong – we did the wrong thing.’ That’s valuable in building trust (if you believe him, of course): he was addressing the normative expectations. The anger that feeds the growth of distrust is driven by a sense of moral hurt – what I thought ought to have happened, didn’t.


In his final segment, Brennan analysed the concept of distrust as involving scepticism, checking up, and ‘hard feelings’.

Showing images of President Trump and Matt Hancock (UK Secretary of State for Digital, Culture, Media and Sport) Brennan remarked: you may be sceptical about what Trump says he will do or did do; you might check up on evidence of promises and actions; you may have feelings of resentment too. As for Hancock (who also has various demerits to his reputation) – well, said Brennan, he doesn’t trust either of these men, but that doesn’t mean he distrusts both of them. He actively distrusts Trump because of his experience of the man; until recently he didn’t even know Hancock existed, so the animosity isn’t there. There’s an absence of trust, but also an absence of distrust: it’s not binary, there’s a space in the middle.

That could be significant when we talk about trusting the media, and building trust in this space. If we are going to survey or study the degree to which people trust the media, we must be careful to ensure that the questions we put to people correctly distinguish between distrust and an absence of trust; and perhaps distinguish also between mere reliance and true trust.

Perhaps in moving things forward, it may be too ambitious, or even misguided, to aim for an increase in trust? Perhaps the thing to aim for in our media and information sources is Reliability, because that is something we can check up on (e.g. fact-checking), regardless of subjective feelings of trust, distrust, or an absence of trust.

Q&A for Brennan

Bill Thompson (BBC) noted that a Microsoft researcher, danah boyd, who examined the social lives of networked teens, talks about the ‘promise’ that is made: that is, a social media network offers you a particular experience with them, and if you feel that promise has been betrayed, distrust arises. Matt Hancock had not offered Brennan anything yet… The question then is, what is the promise we would like the media to make to us, on which we could base a relationship of trust?

Brennan agreed. Do we know what expectations we have of the media? Have we tried to communicate that expectation? Have the media tried to find out? Bill replied, media owners and bosses can get very defensive very quickly, and journalists will complain that people don’t understand how tough their jobs are. But that’s no way to have a conversation!

Naomi Lees wondered about trust in the context of the inquiry into the June 2017 fire disaster at Grenfell Tower (the inquiry was about to start on a date shortly after this meeting). There is much expectation that important truths will and should be revealed. She thought that was an advance compared to the inquiry into the Hillsborough disaster, where there was a great deal of misinformation and police cover-up, and it took years for the truth to come out.


Conrad Taylor on ‘A Matter of Fact’

After a brief refreshment break, the seminar entered its second part, with a focus not so much on trust and trustworthiness, more on the integrity and truthfulness of news and factual information – both in the so-called ‘grown up media’ of print journalism and broadcasting, and the newer phenomena of web sites and social media platforms.

To open up this half of the topic, I had put together a set of slides, which has been converted to an enhanced PDF with extended page comments. It also has an appendix of 13 pages, with 80 annotated live links to relevant organisations, articles and other resources online.

I was eager to leave 50 minutes for the table-groups exercise I had devised, so my own spoken presentation had to be rushed in fifteen minutes. Because a reader can pretty much make sense of much of my presentation by downloading the prepared PDF and reading the page comments, I shall just summarise my talk briefly below.

Please download the PDF resource file; it may be freely distributed

A matter of fact, or a matter of opinion?

I started with a display of claims that have been seen in the media, particularly online. Some (‘Our rulers are shape-shifting reptilians from another planet’) are pretty wild; ‘MMR vaccine has links to autism’ has been comprehensively disproved in the medical literature; but others such as ‘Nuclear energy can never be made safe’ have been made in good faith, and are valid topics for debate.

Following events such as Russia’s annexation of Crimea, the 2016 US Presidential election, the 2017 Brexit referendum, and the war in Syria, more people and organisations have been expressing alarm at the descent into partisanship, propaganda and preposterous claims in both the established and new media. In the UK, this has included knowledge and information management organisations.

CILIP, the Chartered Institute for Library and Information Professionals, took the lead with its ‘Facts Matter’ campaign for information literacy. ISKO UK, at its September 2017 conference, hosted a panel called ‘False Narratives: developing a KO community response to post truth issues.’ (Full audio available; see links in PDF.) Dave Clarke of Synaptica ran a two-day seminar at St George’s House in January 2018 examining ‘Democracy in a Post-Truth Information Age’, and its report is also available; most recently, ISKO UK returned to the topic within a seminar on ‘Knowledge Organization and Ethics’ (again, audio available).

Dodgy news stories are not new. Rather akin to modern partisan disinformation campaigns was Titus Oates’ 1678 claim to have discovered a ‘Popish Plot’ to assassinate King Charles II (a complete fabrication, but it led to the judicial murder of a couple of dozen people).

Beyond ‘fake news’ to a better-analysed taxonomy

Cassie Staines recently argued on the blog of the fact-checking charity Full Fact that we should stop using the label ‘fake news’. She says: ‘The term is too vague to be useful, and has been weaponised by politicians.’ (Chiefly by Donald Trump, who uses it as a label to mobilise his supporters against quality newspapers and broadcasters who say things he doesn’t like). The First Draft resource site for journalists suggests a more nuanced taxonomy spanning satire and parody, misleading use of factual information by omitting or manipulating context, impersonation of genuine news sources, and completely fabricated, malicious content.

The term ‘post-truth’ got added to the Oxford English Dictionary in 2016, defined as ‘relating to or denoting circumstances in which objective facts are less influential in shaping public opinion than appeals to emotion and personal belief.’ If we want a snappy label, perhaps this one is better than ‘fake news’, and Dave Clarke appropriated it for his project the Post Truth Forum (PTF), to which I am also a recruit. PTF has attempted a more detailed two-level typology.

I briefly mentioned conspiracy theories and rumours such as ‘the 9/11 attacks were an inside job’. A 2014 article in the American Journal of Political Science, ‘Conspiracy Theories and the Paranoid Style(s) of Mass Opinion’ rejects the idea that these are unique to ignorant right-wingers, and says that there is more of a link to a ‘willingness to believe in other unseen, intentional forces and an attraction to Manichean narratives.’ (A certain tendency to conspiracy theory can also be found amongst elements on the environmentalist, left-libertarian and anarchist communities – which is not to say that everyone in those communities is a ‘conspiracist’.)

Misleading health information (anti-vaccination rumours, touting ‘alternative’ nutrition-based cancer treatments) is a category that has been characterised as a public health risk. In the case of the rubbish touted by Mike Adams’ site ‘Natural News’, there is a clearly monetised motive to sell dietary supplements.

Transparency and fact checking

Validating news in a ‘post-truth’ world brings up the question of transparency of information sources. It’s hard to check stories in the media against facts, when the facts are being covered up! Governments are past masters at the cover-up, and it is a constant political struggle to bring public service truths and data, policies and true intentions out into the open. Even then, they are subject to being deliberately misrepresented, distorted, spun and very selectively presented by politicians and partisan media. Companies have done the same, examples being Volkswagen, Carillion, Syngenta; and public relations organisations stand ready to take money to help these dodgy activities.

Karen A Schriver

Karen Schriver speaks about the quest for Plain Language and transparency in American public life & business.
(Listen to podcast.)

But even when information is available, it is often not truly accessible to the public – because it may be badly organised, badly worded, badly presented – not through malice, but because of misunderstanding, incompetence and lack of communication skills. This is where information designers, plain language specialists, technical illustrators and data-diagrammers have skills to contribute. I suggest listening to a podcast of an interview with my friend Dr Karen Schriver, who was formerly Professor of Rhetoric and Document Design at Carnegie-Mellon University: she speaks about the Plain Language movement in the USA, and its prospects (again, link in PDF).

When it comes to reality checking, sometimes common sense is a good place to start. I took apart an article in London’s Evening Standard quoting a World-Wide Fund for Nature estimate that the UK uses 42 billion plastic straws annually. Do the maths! That would mean that each one of our 66 million population, from infant child to aged pensioner, on average uses 636 straws a year. Is this credible? BBC Reality Check looked into this, and a very different claim made by DEFRA (8.5 billion/year), and found that both figures came from the consultancy ‘Eunomia’, whose estimating methodologies and maths are open to question.

To be fair to journalists, it is hard for them to check facts too. In my slide deck I list a number of pressures on them. Amongst the most problematic are shrinking newsroom budgets and staffing, time pressures in the 24-hour news cycle, and more information coming in via tweets and social media and YouTube, especially from conflict and disaster zones abroad. There are projects and organisations trying to help journalists (and the public) through this maze; I have already mentioned Full Fact and First Draft, and a new one is DMINR from City University of London School of Journalism.


Group exercise: contested truths, trust in sources

Our seminar participants gathered in table groups of about six or seven. To the tables, I distributed five sheets each bearing a headline, referring to a fairly well-known (mostly UK-centric) current affairs issue, as follows:

  • Anti-Semitism is rife in Labour Party leadership
  • London threatened by wave of youth violence
  • Global warming means we must de-carbonise
  • Immigration responsible for UK housing crisis
  • 9,500 die annually in London because of air pollution

Divide The Dollar Game

Using ‘divide the dollar’ with British pennies to rapidly select two of the topics to discuss.)

‘Divide the dollar’

I asked the teams to use a ‘divide-the-dollar’ game to quickly select two of the presented choices of topics on which their table would concentrate. (Each person took three coins, put two on their personal first choice, and one on their second choice; the group added up the result and adopted the two top scorers).

Tag and talk

I also presented a sheet of ‘tags’ denoting possible truth and comprehension issues which might afflict these narratives, such as ‘State-sponsored trolling’ or ‘hard to understand science’. Table groups were encourage to write tags onto the sheets for their chosen topics – quickly at first, without discussion – and then start deciding which of these factors were dominant in each case.

The final part of the exercise was to think about how we might start ‘fact-checking’ each news topic. Which information sources, or research methods, would you most trust in seeking clarity? Which would you definitely distrust? And finally, though in the time limit we didn’t really get into this, can people identify their own biases and filters, which might impede objective investigation of the issues?

A lively half-hour exercise ensued, with the environmental/pollution topics emerging on most tables as the favourite case studies. Problems getting to grips with the science was identified as a key difficulty in assessing claim and counter-claim about these. I then spent the last ten minutes pulling out some shared observations from the tables.

It was all a bit of a scramble, but NetIKX audiences like their chance to engage actively in small groups (it’s one of the USPs of NetIKX, which we try to do at most meetings), Perhaps it points its way to an exercise which could be repeated, if not in content, then using the same method around a different subject.

My own reflections

DCMS report on ‘fake news’

Disinformation and ‘fake news’ Interim Report. published by the House of Commons Digital, Culture, Media and Sport Committee in July 2018. The report lambasts the social media platforms, but is eerily silent about disinformation and slanted reporting in Britain’s tabloid press. (Download the report.)

I personally think that being sceptical of all sources of information is healthy, and none can demand our trust until they have earned it. This is true whatever the information channel. In that respect I agree with Brennan Jacoby, and with Baroness O’Neill.

Our seminar had focused primarily on political opinions and news stories, and in this field the control and manipulation of information is a weapon. To cope, on the one hand we need better access to fact-checking resources; on the other we need to understand the political agenda and motivations and pressures on each publisher and broadcaster — and, indeed, commercial or government or NGO entity which is trying to spin us a line.

Amongst librarians there are calls for promoting so-called ‘information literacy’ and critical thinking habits, from an early age. I would add that the related idea of ‘media literacy’ also has merit.

I have a strong interest in the field of science communication. Some of the most pressing problems of our age are best informed by science, including land and agriculture management, the treatment of diseases, climate change risks, future energy policy, and the challenges of healthcare. But here we have a double challenge: on the one hand, most people are ill-equipped to understand and evaluate what scientists say; on the other, powerful commercial and nationalist interests are working to undermine scientific truth and profit from our ignorance.

Two related aspects of science communication we might further look at are understanding risk, and understanding statistics. The information-and-knowledge gang keeps itself artificially apart from those who work with data and mathematics – that too would be a gulf worth bridging.

— Conrad Taylor, May 2018

Tony Quinlan explaining how to interpret SenseMaker signifiers. The pink objects behind him are the micro-narratives we produced during the exercise, on ‘super-sticky’ Post-It notes. Photo Conrad Taylor.

Working in Complexity – SenseMaker, Decisions and Cynefin

Account of a NetIKX meeting with Tony Quinlan of Narrate, 7 March 2018, by Conrad Taylor

At this NetIKX afternoon seminar, we got a very thorough introduction to Cynefin, ananalytical framework that helps decision-makers categorise problems surfacing in complex social, political and business environments. We also learned about SenseMaker®, an investigative method with software support, which can gather, manage and visualise patterns in large amounts of social intelligence, in the form of ‘narrative fragments’ with quantitative signifier data attached.

Tony Quinlan explaining how to interpret SenseMaker signifiers. The pink objects behind him are the micro-narratives we produced during the exercise, on ‘super-sticky’ Post-It notes. Photo Conrad Taylor.

Tony Quinlan explaining how to interpret SenseMaker signifiers. The pink objects behind him are the micro-narratives we produced during the exercise, on ‘super-sticky’ Post-It notes. Photo Conrad Taylor.

The leading architect of these analytical frameworks and methods is Dave Snowden, who in 2002 set up the IBM’s Cynefin Centre for Organisational Complexity and founded the independent consultancy Cognitive Edge in 2005.

Our meeting was addressed by Tony Quinlan, CEO and Chief Storyteller of consultancy Narrate (, which has been using Cognitive Edge methodology since 2008. Tony, with his Narrate colleague Meg Odling-Smee, ran some very engaging hands-on exercises for us, which gave us better insight into what SenseMaker is about. Read on!

What follows is, as usual, my personal account of the meeting, with some added background observations of my own. (I have been lucky enough to taken part in three Cognitive Edge workshops, including one in which Tony himself taught us about SenseMaker.)

The power of narrative

Tony Quinlan also used to work for IBM, in internal communications and change management; he then left to practice as an independent consultant. Around 2000, he set up Narrate, because he recognised the valuable information that is held in narratives. Then in 2005, as Dave Snowden was setting up Cognitive Edge, Tony became aware of the Cynefin Framework – a stronger theoretical basis for understanding the significance of narrative, and how one might work effectively with it.

There several ways of working with narratives in organisations, and numerous practitioners. There’s a fruitful workshop technique called ‘Anecdote Circles’, well described in a workbook from the Anecdote consultancy. (See their ‘Ultimate Guide to Anecdote Circles’ in PDF. There is also the ‘Future Backwards’ exercise, which Ron Donaldson demonstrated to NetIKX at a March 2017 meeting.  These methods are good, but they require face-to-face engagement in a workshop environment.

A problem arises with narrative enquiry when you want to scale up – to collect and work with lots of narratives – hundreds, thousands, or more. How do you analyse so many narratives without introducing expert bias? Tony found that the SenseMaker approach offered a sound solution and, so far, he’s been involved in about 50 such projects, in 30 countries around the world.

I was reminded by Tony’s next comment of the words of Karl Marx: ‘The philosophers have only interpreted the world, in various ways. The point, however, is to change it.’

Tony remarked that there is quite a body of theory behind the Cognitive Edge worldview, combining narrative-based enquiry with complexity science and cognitive neuroscience insights. But the real reasons behind any SenseMaker enquiry are: ‘How do we make sense of where we are? What do we do next?’ So we were promised a highly practical focus.

A hands-on introduction to SenseMaker

Tony and Meg had prepared an exercise to give us direct experience of what SenseMaker is about, using an arsenal of stationery: markers, flip-chart pages, sticky notes and coloured spots!

Collecting narratives:   The first step in a SenseMaker enquiry is to pose an open-ended question, relevant to the enquiry, to which people respond with a ‘micro-narrative’. To give us an exercise example, Tony said: ‘Sit quietly, and think of an occasion which inspired/pleased you, or frustrated you, in your use of IT [support] in your organisation (or for freelances, with an external organisation you contact to get support).’

Extra-large Post-It notes had been distributed to our tables. Following instructions, we each took one, and wrote a brief narrative about the experience we’d remembered. After that, we gave our narrative a title. We were also given sheets of sticky-backed, coloured dots. We took seven each, all of the same colour, and wrote our initials on them. We each took one of our dots, and stuck it on our own narrative sticky note. Then, we all came forward and attached our notes to the wall of the room.

Adding signifiers: Tony now drew our attention to where he and Meg had stuck up four posters. On three, large triangles were drawn, each with a single question, and labels at the triangle corners. The fourth was drawn with three stripes, each forming a spectrum. (This description makes better sense if you look at our accompanying diagrams.) In SenseMaker practice these are called ‘triads’ and ‘dyads’ respectively, and they are both kinds of SenseMaker ‘signifiers’.

For example, the first triad asked us: ‘In the story you have written, indicate which needs were being addressed’. The three corners were labelled ‘Business needs’, ‘Technology needs’ and ‘People’s needs’. We were asked each to take one of our initialled, sticky dots and place it within the triangle, based on how strongly we felt each element was present within our story.

As for the dyads, we were to place our dot at a position along a spectrum between opposing extremes. For example, one prompted: ‘People in this example were…?’ with one end of the spectrum labelled ‘too bureaucratic’ and the other ‘too risky’.

In the diagrams below I have represented how our group’s total results plotted out over the triads and dyads, but I have made all the dots the same colour (for equal visual weight); and, obviously, there are no identifying initials.

Figure 1 Triad set

Figure 1 Triad set

Figure 2 Dyad compilation

Figure 2 Dyad compilation

A few observations on the exercise

  • When SenseMaker signifiers are constructed, a dyad – also referred to as a ‘polarity’ – has outer poles, which are often equally extreme opposites (‘too bureaucratic – too risky’). But in designing a triad, the corners are made equally positive, equally negative, or neutral.
  • It strikes me that deciding on effective labels to use takes some considerable thought and skills, especially for triads. Over the years, Cognitive Edge has developed sets of repurposable triads and dyads, often with assistance from anthropologists.
  • A real-world SenseMaker enquiry would typically have more signifier questions – perhaps six triads and two dyads.
  • For practical operational reasons, we all placed our dots on a common poster. This probably means that people later in the queue were influenced by where others had already placed their dots. In a real SenseMaker implementation, each person sees a blank triangle, for their input only. Then the responses are collated (in software) across the entire dataset.
  • Because the results of a SenseMaker enquiry are collated in a database, the capacity of such an enquiry is practically without limit.
  • There can be further questions, e.g. to ascertain demographics. This allows for explorations of the data, such as, how do opinions of males differ from those of females? Or young people compared to their elders?
  • SenseMaker results are anonymised, but the database structure in which responses are collected means that we can correlate a response on one signifier, with the same person’s response on another. For our paper exercise, we had to forgo that anonymity by using initials on coloured dots.

Our exercise gathered retrospective narratives, collected in one afternoon. But SenseMaker can be set up as an ongoing exercise, with each narrative fragment and its accompanying signifiers time-stamped. So, we can ask questions like ‘were customers more satisfied in May than in April or March?’

Analysing the results

Calling us to order, Tony talked through our results. At first, he didn’t even look at our narratives on the wall. It’s hard to assess lots of narratives without getting lost in the detail. It’s still more difficult if you have to wade through megabytes of digital audio recordings – another way some narratives have been collected in recent years.

But the signifiers can be thrown up en masse on a computer screen in a visual array, as they were on our posters. Then it’s easy to spot significant clusterings and outliers, and you can drill down to sample the narratives with a single click on a dot. Even with our small sample we could see patterns coming up. One dyad showed that most people thought the IT department was to blame for problems.

With SenseMaker software support, this can scale. Tony recalled a project in Delhi with 1,500 customers of mobile telecoms, about what helped and what didn’t when they needed support. A recent study in Jordan, about how Syrian refugees can be better supported, gathered 4,000 responses.

This was an enlightening exercise, giving NetIKX participants a glimpse of how SenseMaker works. But just a glimpse, cautioned Tony: the training course is typically three days.

Why do we do it like this?

Now it was time for some theory, including cognitive science, to explain the thinking behind SenseMaker.

How do humans make decisions? Not as rationally as we might like to believe, and not just because emotions get in the way. As humans we evolved to be pattern-matching intelligences. We scan information available to us, typically picking up just a tiny bit of what is available to us, and quickly match it against pre-stored response patterns. (And, as Dave Snowden has remarked, any of our hominid ancestors who spent too long pondering the characteristics of the leopard bounding towards them didn’t get to contribute to the gene pool!)

‘But there’s worse news,’ said Tony. ‘We don’t go for the best pattern match; we go for the first one. Then we are into confirmation bias, which is difficult to snap out of.’ (Ironically for knowledge management practice, maybe that means ‘lessons learned’ thinking can set us up for a fall – blocking us from seeing emerging new phenomena.)

Patterns of thinking are influenced by the cultures in which we are embedded, and the narratives we have heard all our lives. Those cultures and stories may be in the general social environment, or in our subcultures (e.g. religious, political, ethnic); they could be formed in the organisation in which we work; they could come at us from the media. All these influences shape what information we take in, and what we filter out; and how we respond and make decisions.

Examining people’s micro-narratives shows us the stories that people tell about their world, which shape opinions and decisions and behaviour. In SenseMaker, unlike in polls and questionnaires, we gather the stories that come to people’s minds when asked a much more open-ended prompting question. SenseMaker questions are deliberately designed to be oblique, without a ‘right answer’, thus hard to gift or game.

You don’t necessarily get clean data by asking straight questions, because there’s that strong human propensity to gift or to game – to give people the answer we think they want to hear, or to be awkward and say something to wind them up. In the Indian project with mobile service customers, when poll questions asked customers they would recommend the service to others, the responses were overwhelmingly positive. But in the SenseMaker part of the research, about 20% of those who claimed they would definitely recommend the company’s service, were shown by the triads to really think the diametric opposite.

Social research methods that do use straight questions are not without value, but they are reaching the limits of what they can do, and are often used in places where they no longer fit: where dynamics are complex, fluid and unpredictable. But complexity is not universal, said Tony; it is one domain amongst a number identified in the Cynefin Framework.

The Cynefin Framework

Figure 3 Diagram of the Cynefin domains, with annotations

Figure 3 Diagram of the Cynefin domains, with annotations

Cynefin, explained Tony, is a Welsh word (Dave Snowden is Welsh). It means approximately ‘The place(s) where I belong.’ Cynefin is a way of making sense of the world: of human organisation primarily. It is represented by a diagram, shown in Fig. 3, and lays out a field of five ‘domains’:

  • Simple. For problems in this domain, the relationship between cause and effect is obvious: if there is a problem, we all know how to fix it. (More recently, this domain is labelled ‘Obvious’, because Simple sounds like Easy. It may be Obvious we need to dig a tunnel through a mountain, but it’s not Easy…) Organisations define ‘best practice’ to tackle Obvious issues.
  • Complicated. In this domain, there are repeatable and predictable chains of cause and effect, but they are not so easy to discern. A jet engine is sometimes given as a metaphorical example of such complicatedness. In this domain, problem-solving often involves the knowledge of an expert, or an analytical process.
  • Chaotic. In this domain, we can’t discern a relationship between cause and effect at all, because the interacting elements are so loosely constrained. Chaos is usually a temporary condition, because a pattern will emerge, or somebody will take control and impose some sort of order. In Chaos, you don’t have time to assess carefully: decisive action is needed, in the hope that good things will emerge and can be encouraged. (And as Dave sometimes says, ‘Light candles and pray!’)
  • Complex. This is a domain in which various ‘actors and factors’ – animate and inanimate – do respond to constraints, and can be attracted to influences, but those constraints and influences are relatively elastic, and there are many interactions and feedback loops that are hard to fathom. Such a system is more like a biological or ecological system than a mechanical one. Cognitive Edge practitioners have a battery of techniques for experimentation in this space, as Tony would soon describe.
  • Disorder – that dark pit in the middle of the diagram represents problems where we cannot decide into which of the other domains this situation fits.

Finally, Tony pointed out a feature on the borderlands between Obvious and Chaotic, typically drawn like a cliff or fold. This is there to remind us that if people act with complete blind conviction that things are really simple and obvious, and Best Practice is followed without question, the organisation can be blindsided to major changes happening in their world. One day, when you pull the levers, you don’t get the response you have come to expect, and you have a crisis on your hands. And if you simply try to re-impose the rules, it can make things worse.

But with chaos may come opportunity. Once you have a measure of control back, you have a chance to be creative and try something new. And as we prepared to take a refreshment break, Tony urged us, ‘Don’t let a good crisis go to waste!’

Working in complex adaptive systems

Tony recalled that his MBA course was predicated on the idea that things are complicated, but there is a system for working things out. The corollary: if things don’t work out, either you didn’t plan well or you failed in implementation (‘are you lazy or stupid?’) Later, when he saw the Cynefin model, he was relieved to note that you can be neither lazy nor stupid and things can still go pear-shaped, in a situation of complexity.

In Cognitive Edge based practice, when you find you are operating in the domain of complexity, the recommendation is to initiate ‘safe-to-fail’ probes and experiments. Here are some working principles:

  • Obliquity — don’t go after an intractable problem directly. A misplaced focus can have massive unintended consequences. Tony has done work around problems of radicalisation and contemporary terrorism, e.g. in Pakistan and the Middle East. Western authorities and media operate as if radicalisation was a fruit of Islamic fundamentalism – but from a Jordanian perspective, a very significant factor is when young people don’t have jobs, are bored and frustrated, and don’t have a stake in society.
  • Diversity — The perspective you bring to a problem shapes how you see it. In the complex domain we can’t rely on solutions from experts alone. On the Jordanian project, to review SenseMaker inquiry results, they brought together experts from the UN, economists and government officials – but also Syrian refugees, and unemployed Jordanian youth. When the experts started to rubbish the SenseMaker data, saying it didn’t fit their experience in the field, the refugees, the youth and government officials were standing up and saying, ‘But that is our experience; we recognise it intimately.’
  • Experimentation — In a complex situation you cannot predict how experiments will work out, so you try a few things at the same time. Some won’t work. That’s why probes and experiments must be safe-to-fail: if an experiment is going to fail, you don’t want catastrophic consequences; you want to pull the plug and recover quickly. (If none of your experiments fail, it probably means you have been too timid with your experimental alternatives.)
  • Feedback — Experimenting in complex situations, we try to nudge and evolve the system. You don’t set up ‘a solution’ and come back in two years to see how it went – by then, things may have evolved to a point you can’t recover from. You need constant feedback to monitor the evolving situation for good or bad effects, and to spot when unexpected things happen.

When monitoring, it’s better to ask people what they do, rather than what they think. You’d be surprised how many respondents claim to a think a certain way, but that isn’t what they actually do or choose.

Even with the micro-narrative approach, you have to be careful in your evaluation. Meaning is not only in the words, and responses may be metaphorical, or even ironic. That can be tricky if you are working across cultures.

Safe-to-fail in Broken Hill: My personal favourite Snowden anecdote illustrating ‘safe-to-fail’ experiments comes from work Dave did with Meals on Wheels and the Aboriginal communities around Broken Hill, NSW, Australia. How could that community’s diets be improved to avoid Type II diabetes?

Projects were proposed by community members. 13 were judged ‘coherent’ enough to be given up to Aus$ 6,000 each: bussing elders to eat meals in common; sending troublesome youngsters to the bush to learn how to hunt; farming desert pears; farming wild yabbies (crayfish; see picture).


Results? Some flopped (bussing elders); some merged (farming desert pears and yabbies); some turned out to work synergistically (hunting lessons for youth generated a meat surplus to supply a restaurant, using traditional killing and cooking practices). Nothing failed catastrophically.

The crucial role of signifiers

In a SenseMaker enquiry, only the respondents can say what their stories mean; interacting with well designed signifiers is very powerful in this regard. Tony recalled one project with young Ethiopian women; their narratives were presented to UNDP gender experts, who were asked to read them and fill out the SenseMaker signifiers as they thought the young women might. The experts’ ideas are not unimportant; but, they significantly differed from the responses ‘from the ground’, which can be important in policymaking. SenseMaker de-privileges the expert and clarifies the voice of the respondent. Dave Snowden refers to this as ‘disintermediation’.

When you design a SenseMaker framework, you do it in such a way that it doesn’t suggest a ‘right’ answer. As an example of the latter, Tony showed a linear scale asking about a conference speaker’s presentation skills, ranging from ‘poor’ to ‘excellent’ (and looking embarrassingly like the NetIKX evaluation sheets!). ‘If I put this up, you know what answer I’m looking for.’

In contrast, he showed a triad version prepared in the course of work with a high street bank. The overall prompt asked ‘This speaker’s strengths were…’ and the three corners of the triad were marked [a] relevant information; [b] clear message; [c] good presentation skills. Tony took a sheaf of about a hundred sheets evaluating speakers at an event, and collated the ‘dots’ onto master diagrams. One speaker had provoked a big cluster of dots in the ‘relevant information’ corner. Well, relevance is good – but evidently, his talk had been unclear, and his presentation skills poor.

Tony showed a triad that was used in a SenseMaker project in Egypt. The question was, ‘What type of justice is shown in your story?’ and the corners were marked [a] revenge, getting your own back; [b] restorative, reconciling justice; and [c] deterrence, to warn others from acting as the perpetrator had done.

Tony then showed a result from a similar project in Libya, which collected about 2,000 micro-narratives. The dominant form of justice? Revenge. This was cross-correlated with responses about whether the respondents felt positively or negatively about their story, and the SenseMaker software displayed that by colouring the dots on a spectrum, green to red. And what this showed was, people felt good in that culture and context about revenge being the basis of justice.

In SenseMaker evaluation software (‘Explorer’, see end), if you want to make even more sense, you click on a dot and up comes the text of the related micro-narrative. Or, you can ask to see a range of stories in which the form of justice people felt good about was of the deterrent type. In this case, those criteria pulled up a subset of 171 stories, which the project team could then page through.

From analysis to action: another exercise

SenseMaker wasn’t created for passive social research projects. It is action-oriented. An important question used in a lot of Cognitive Edge projects is, ‘What can we do to have fewer stories like that, and more stories like this?’ That question is a useful way to encourage people to think about designing interventions, without flying away into layers of abstraction. You get stakeholders together, show them the patterns, and ask, ‘What does this mean?’ Using as a guide the idea of ‘more stories like these, fewer like those,’ you then collectively design interventions to work towards that.

Tony had more practical exercises for us, to help us to understand this analytical and intervention-designing process.

Here is the background: about five years ago, a big government organisation was worried about how its staff perceived its IT department. Tony conducted a SenseMaker exercise with about 500 participants, like the one we had done earlier – the same overall question to provoke micro-narratives, and the same or similar triad and dyad signifier questions.

Now we were divided into five table groups. Each group was given sheets of paper with labelled but blank triads on them. We were each of us to think about where on the triad we would expect most answers to have come, then make a mark on the corresponding triad. Then Tony showed us where the results actually did come in.

I’m not going into detail about how this exercise went, but it was interesting to compare our expectations as outsiders, with the actual historical results. This ‘guessing game’ is also useful to do with the stakeholder community in a real SenseMaker deployment, because it raises awareness of the divergence between perceptions and reality.

Ideas can come from the narratives

In SenseMaker, micro-narratives are qualitative data; the signifier responses, which resolve into dimensional co-ordinates, are in numerical form, which can be be more easily computed, pattern-matched, compared and visualised with the aid of machines. This assists human cognition to home in on where salient issue clusters are. Even an outsider without direct experience of the language or culture can see those patterns emerging on the chart plots.

But when it comes to inventing constructive interventions, it pays to dip down into the micro-narratives themselves, where language and culture are very important.

In a project in Bangladesh, the authorities and development agency partners had spent years trying to figure out how to encourage rural families to install latrines in their homes, instead of the prevailing behaviour of ‘open defecation’ in the fields. Tony’s initial consultations were with local experts, who said they would typically focus on one of three kinds of message. First, using family latrines improves public health, avoiding water-borne diseases and parasites. Second, it reduces risk (e.g. avoiding sexual molestation of women). Third, it reduces the disgust factor. Which of those messages would be most effective in making a house latrine a desirable thing to have?

A SenseMaker enquiry was devised, and 500 responses collected. But when the signifier patterns were reviewed, no real magic lights came on. Yes, one of the triads which asked ‘in your story, a hygienic latrine was seen as [a] healthy [b] affordable [c] desirable’ returned a strong pattern answers indicating ‘healthy’. But that could be put down to years of health campaigns – which had nevertheless not persuaded people to install latrines.

Get a latrine, have a happy marriage!   But behind every dot is a story. The team in the UK asked the team in Dhaka to translate a cluster of some 19 stories from Bengali and send them over. There they found a bunch of stories which conveyed this message: if you install a latrine, you’ve got a better chance of a good marriage! One such story told of a young man, newly married, who got an ear-wigging from his mother-in-law, who told him in no uncertain terms what a low-life he was for not having a latrine in the house for her wonderful daughter…

Another story was from a village where there were many girls of marriageable age. Their families were receiving proposals from nearby villages. A young man came with his family to negotiate for a bride, and after a meal and some conversation, a guest asked to use the toilet. The girl’s father simply indicated some bushes where the family did their business. Immediately, the negotiations were broken off. The young man’s family declared that they could not establish a relationship with a family which did not have a latrine. Before long, the whole village knew why the marriage had been cancelled – and why! Shamed and chastened, the girl’s family did invest in a latrine, and the girl eventually found a husband.

As an outcome of this project, field officers have been equipped with about twenty memorable short stories, along similar lines about the positive social effect of having a latrine, and this is having an effect. If the narratives had not been mined as a resource, this would not have happened.

SenseMaker meets Cynefin

As our final exercise, Tony distributed some of the micro-narratives contributed to the project at that government organisation five years ago. We should identify issues illustrated by the narratives, and for each one we discovered, we should write a summary label on a sticky-backed note.

He placed on the wall a large poster of the Cynefin Framework diagram, and invited us to bring our notes forward, and stick them on the diagram to indicate whether we thought that problem was in the Complex domain, or Complicated, or Obvious or Chaotic, or along one of the borders… That determines whether you think there is an obvious answer, or something where experts need to consulted, or if we are in the domain of Complexity and it’s most appropriate to devise those safe-to-fail experimental interventions.

We just took five minutes over this exercise; but Tony explained, he has presided over three-hour versions of this. For the government department, he had this exercise done by groups constituted by job function: directors round one table, IT users round another, and so on. All had the same selection of micro-narratives to consider; each group interpreted them according to their shared mind-set. For the directors, just about everything was Obvious or Complicated, soluble by technical means and done by technologists. The system users considered a lot more problems to be in the Complex space, where solutions would involve improving human relations.

On that occasion, table teams were then reformulated to have a diverse mix of people, and the rearranged groups thought up direct actions that could solve the simple problems, research which could be commissioned to help solve complicated problems, and as many as forty safe-to-fail experiments to try out on complex problems. The whole exercise was complete within one day. Many of the practical suggestions which came ‘from the ground up’ were not that expensive or difficult to implement, either.

SenseMaker: some technical and commercial detail

Tony did not have time to go into the ‘nuts and bolts’ of SenseMaker, so I have done some online study to be able to tell our readers more, and give some links.

We had experienced a small exercise with the SenseMaker approach, but the real value of the methods come when deployed on a large scale, either one-off or continuously. Such SenseMaker deployments are supported by a suite of software packages and a database back end, maintained by Cognitive Edge (CE). Normally an organisation wanting to use SenseMaker would go through an accredited CE practitioner consultancy (such as Narrate), which can select the package needed, help set it up, and guide the client all the way through the process to a satisfactory outcome, including helping the client group to design appropriate interventions (which software cannot do).

SenseMaker® Collector   After initial consultations with the client and the development of a signification framework, an online data entry platform called Collector is created and assigned a specific URL. Where all contributors have Internet access, for example an in-company deployment, they can directly add their stories and signifier data into an interface at that URL. Where collection is paper-based, the results will have to be manually entered later by project administrators with Internet access.

A particularly exciting recent trend in Collector is its implementation on mobile smart devices such as Apple iPad, with its multimedia capabilities. Narrative fragment capture can now be done as an audio recording with communities who cannot read or write fluently, so long as someone runs the interview and guides the signification process.


My favourite case study is one that Tony was involved in, a study in Rwanda of girls’ experience commissioned by the GirlHub project of the Nike Foundation. A cadre of local female students very quickly learned how to use tablet apps to administer the surveys; the micro-narratives were captured in audio form, stored on the device, and later uploaded to the Collector site when an Internet connection was available.

Using iPads for SenseMaker collecting: The SenseMaker Collector app for iOS was first trialled in Rwanda in 2013. Read Tony’s blog post describing how well it worked. The project as a whole was written up in 2014 by the Overseas Development Institute (‘4,000 Voices: Stories of Rwandan Girls’ Adolescence’) and the 169-page publication is available as a 10.7 MB PDF.

SenseMaker® Explorer   Once all story data has been captured, SenseMaker Explorer software provides a suite of tools for data analysis. These allow for easy visual representation of data, amongst the simplest being the distribution of data points across a single triad to identify clusters and outliers (very similar to what we did with our poster exercise earlier). By drawing on multiple signifier datasets and cross-correlating them, Explorer can also produce more sophisticated data displays, for example a kind of 3D display which Dave Snowdon calls a ‘fitness landscape’ (a term probably based on a computation method used in evolutionary biology – see Wikipedia, ‘Fitness landscape’, for examples of such graphs). Explorer can also export data for analysis in other statistical packages.

A useful page to visit for an overview of the SenseMaker Suite of software is — it features a short video in which Dave Snowden introduces how SenseMaker works, against a series of background images of the software screens, including on mobiles.

That page also gives links to eleven case studies, and further information about ‘SCAN’ deployments. SCANs are preconfigured, standardised SenseMaker packages around recurrent issues (example: safety), which help an organisation to implement a SenseMaker enquiry faster and more cheaply than if a custom tailored deployment is used.

Contacting Narrate

Tony and Meg have indicated that they are very happy to discuss SenseMaker deployments in more detail, and Tony has given us these contact details:

Tony Quinlan, Chief Storyteller
mobile: +44 (0) 7946 094 069

First Meeting Outside London: Organising Medical and Health-related Information – Leeds – 7 June 2018

We have now planned our first meeting outside London. This will be in Leeds on Thursday 7 June and the topic will be Medical Information. The meeting will be a joint one with ISKO UK. Speakers will include Ewan Davis.

There will be no charge for attending this meeting, but you must register. For more information and to register, follow the link above.

Making true connections in a complex world – Graph database technology and Linked Open Data – 25th January 2018

Conrad Taylor writes:

The first NetIKX meeting of 2018, on 25 January, looked at new technologies and approaches to managing data and information, escaping the limitations of flat-file and relational databases. Dion Lindsay introduced the concepts behind ‘graph databases’, and David Clarke illustrated the benefits of the Linked Data approach with case studies, where the power of a graph database had been enhanced by linking to publicly available resources. The two presentations were followed by a lively discussion, which I also report here.

The New Graph Technology of Information – Dion Lindsay

dionlindsayDion is an independent consultant well known to NetIKX members. He offered us a simple introduction to graph database technology, though he avers he is no expert in the subject. He’d been feeling unclear about the differences between managing data and information, and thought one way to explore that could be to study a ‘fashionable’ topic with a bit of depth to it. He finds graph database technology exciting, and thinks data- and information-managers should be excited about it too!

Flat-file and relational database models

In the last 40 years, the management of data with computers has been dominated by the Relational Database model devised in 1970 by Edgar F Codd, an IBM employee at their San José Research Center.

FLAT FILE DATABASES. Until then (and also for some time after), the model for storing data in a computer system was the ‘Flat File Database’ — analogous to a spreadsheet with many rows and columns. Dion presented a made-up example in which each record was a row, with the attributes or values being stored in fields, which were separated by a delimiter character (he used the | sign, which is #124 in most text encoding systems such as ASCII).

Example: Lname, Fname, Age, Salary|Smith, John, 35, £280|
Doe, Jane 28, £325|Lindsay, Dion, 58, £350…

In older flat-file systems, each individual record was typically input via a manually-prepared 80-column punched card, and the ingested data was ‘tabulated’ (made into a table); but there were no explicit relationships between the separate records. The data would then be stored on magnetic tape drives, and searching through those for a specific record was a slow process.

To search such a database with any degree of speed required loading the whole assembled table into RAM, then scanning sequentially for records that matched the terms of the query; but in those early days the limited size of RAM memory meant that doing anything clever with really large databases was not possible. They were, however, effective for sequential data processing applications, such as payroll, or issuing utility bills.


The IBM 2311 (debut 1964) was
an early hard drive unit with 7.25 MB storage. (Photo from Wikimedia Commons user
‘I, Deep Silence’

HARD DISKS and RELATIONAL DATABASES. Implementing Codd’s relational database management model (RDBM) was made possible by a fast-access technology for indexed file storage, the hard disk drive, which we might call ‘pseudo-RAM’. Hard drives had been around since the late fifties (the first was a component of the IBM RAMAC mainframe, storing 3.75 MB on nearly a ton of hardware), but it always takes time for the paradigm to shift…

By 1970, mainframe computers were routinely being equipped with hard disk packs of around 100 MB (example: IBM 3330). In 1979 Oracle beat IBM to market with the first Relational Database Management System (RDBMS). Oracle still has nearly half the global market share, with competition from IBM’s DB2, Microsoft SQL Server, and a variety of open source products such as MySQL and PostgreSQL.

As Dion pointed out, it was now possible to access, retrieve and process records from a huge enterprise-level database without having to read the whole thing into RAM or even know where it was stored on the disk; the RDBMS software and the look-up tables did the job of grabbing the relevant entities from all of the tables in the system.

TABLES, ATTRIBUTES, KEYS: In Codd’s relational model, which all these RDBMS applications follow, data is stored in multiple tables, each representing a list of instances of an ‘entity type’. For example, ‘customer’ is an entity type and ‘Jane Smith’ is an instance of that; ‘product’ is an entity type and ‘litre bottle of semi-skimmed milk’ is an instance of that. In a table of customer-entities, each row will represents a different customer, and columns may associate that customer with attributes such as her address or loyalty-card number.

One of the attribute columns is used as the Primary Key to quickly access that row of the table; in a classroom, the child’s name could be used as a ‘natural’ primary key, but most often a unique and never re-used or altered artificial numerical ID code is generated (which gets around the problem of having two Jane Smiths).

Possible/permitted relationships can then be stated between all the different entity types; a list of ‘Transactions’ brings a ‘Customer’ into relationship with a particular ‘Product’, which has an ‘EAN’ code retrieved at the point of sale by scanning the barcode, and this retrieves the ‘Price’. The RDBMS can create temporary and supplementary tables to mediate these relationships efficiently.

Limitations of RDBMs, benefits of graphs

However, there are some kinds of data which RDBMSs are not good at representing, said Dion. And many of these are the sorts of thing that currently interest those who want to make good use of the ‘big data’ in their organisations. Dion noted:

  • situations in which changes in one piece of data mean that another piece of data has changed as well;
  • representation of activities and flows.

Suppose, said Dion, we take the example of money transfers between companies. Company A transfers a sum of money to Company B on a particular date; Company B later transfers parts of that money to other companies on a variety of dates. And later, Company A may transfer monies to all these entities, and some of them may later transfer funds in the other direction… (or to somewhere in the British Virgin Islands?)

Graph databases represent these dynamics with circles for entities and lines between them, to represent connections between the entities. Sometimes the lines are drawn with arrows to indicate directionality, sometimes there is none. (This use of the word ‘graph’ is not be confused with the diagrams we drew at school with x and y axes, e.g. to represent value changes over time.)

This money-transfer example goes some way towards describing why companies have been prepared to spend money on graph data technologies since about 2006 – it’s about money laundering and compliance with (or evasion of?) regulation. And it is easier to represent and explore such transfers and flows in graph technology.

Dion had recently watched a YouTube video in which an expert on such situations said that it is technically possible to represent such relationships within an RDBMS, but it is cumbersome.


Most NetIKX meetings incorporate one or two table-group
sessions to help people make sense of what they have learned. Here, people
are drawing graph data diagrams to Dion Lindsay’s suggestions.


To get people used to thinking along graph database lines, Dion distributed a sheet of flip chart paper to each table, and big pens were found, and he asked each table group to start by drawing one circle for each person around the table, and label them.

The next part of the exercise was to create a circle for NetIKX, to which we all have a relationship (as a paid-up member or paying visitor), and also circles representing entities to which only some have a relation (such as employers or other organisations). People should then draw lines to link their own circle-entity to these others.

Dion’s previous examples had been about money-flows, and now he was asking us to draw lines to represent money-flows (i.e. if you paid to be here yourself, draw a line from you to NetIKX; but if your organisation paid, that line should go from your organisation-entity to NetIKX). I noted that aspect of the exercise engendered some confusion about the breadth of meaning that lines can carry in such a graph diagram. In fact they can represent any kind of relationship, so long as you have defined it that way, as Dion later clarified.

Dion had further possible tasks up his sleeve for us, but as time was short he drew out some interim conclusions. In graph databases, he summarised, you have connections instead of tables. These systems can manage many more complexities of relationships that either a RDBMS could cope with, or that we could cope with cognitively (and you can keep on adding complexity!). The graph database system can then show you what comes out of those complexities of relationship, which you had not been able to intuit for yourself, and this makes it a valuable discovery tool.

HOMEWORK: Dion suggested that as ‘homework’ we should take a look at an online tool and downloadable app which BP have produced to explore statistics of world energy use. The back end of this tool, Dion said, is based on a graph database.

Building Rich Search and Discovery: User Experiences with Linked Open Data – David Clarke


DAVE CLARKE is the co-founder, with Trish Yancey, of Synaptica LLC, which since 1995 has developed
enterprise-level software for building and maintaining many different types of knowledge organisation systems. Dave announced that he would talk about Linked Data applications, with some very practical illustrations of
what can be done with this approach.

The first thing to say is that Linked Data is based on an ‘RDF Graph’ — that is, a tightly-defined data structure, following norms set out in the Resource Description Framework (RDF) standards described by the World Wide Web Consortium (W3C).

In RDF, statements are made about resources, in expressions that take the form: subject – predicate – object. For example: ‘daffodil’ – ‘has the colour’ – ‘yellow’. (Also, ‘daffodil’ – ‘is a member of’ – ‘genus Narcissus’; and ‘Narcissus pseudonarcissus’ – ‘is a type of’ – ‘daffodil’.)

Such three-part statements are called ‘RDF triples’ and so the kind of database that manages them is often called an ‘RDF triple store’. The triples can also be represented graphically, in the manner that Dion had introduced us to, and can build up into a rich mass of entities and concepts linked up to each other.

Describing Linked Data and Linked Open Data

Dion had got us to do an exercise at our tables, but each table’s graph didn’t communicate with any other’s, like separate fortresses. This is the old database model, in which systems are designed not to share data. There are exceptions of course, such as when a pathology lab sends your blood test results to your GP, but those acts of sharing follow strict protocols.

Linked Data, and the resolve to be Open, are tearing down those walls. Each entity, as represented by the circles on our graphs, now gets its own ‘HTTP URI’, that is, its own unique Universal Resource Identifier, expressed with the methods of the Web’s Hypertext Transfer Protocol — in effect, it gets a ‘Web address’ and becomes discoverable on the Internet, which in turn means that connections between entities are both possible and technically fairly easy and fast to implement.

And there are readily accessible collections of these URIs. Examples include:

We are all familiar with clickable hyperlinks on Web pages – those links are what weaves the ‘classic’ Web. However, they are simple pointers from one page to another; they are one-way, and they carry no meaning other than ‘take me there!’

In contrast, Linked Data links are semantic (expressive of meaning) and they express directionality too. As noted above, the links are known in RDF-speak as ‘predicates’, and they assert factual statements about why and how two entities are related. Furthermore, the links themselves have ‘thinginess’ – they are entities too, and those are also given their own URIs, and are thus also discoverable.

People often confuse Open Data and Linked Data, but they are not the same thing. Data can be described as being Open if it is available to everyone via the Web, and has been published under a liberal open licence that allows people to re-use it. For example, if you are trying to write an article about wind power in the UK, there is text and there are tables about that on Wikipedia, and the publishing licence allows you to re-use those facts.

Stairway through the stars

Tim Berners-Lee, who invented the Web, has more recently become an advocate of the Semantic Web, writing about the idea in detail in 2005, and has argued for how it can be implemented through Linked Data. He proposes a ‘5-star’ deployment scheme for Open Data, with Linked Open Data being the starriest and best of all. Dave in his slide-set showed a graphic shaped like a five-step staircase, often used to explain this five-star system:


The ‘five-step staircase’ diagram often used to explain the hierarchy of Open Data types

  • One Star: this is when you publish your data to the Web under open license conditions, in whatever format (hopefully one like PDF or HTML for which there is free of charge reading software). It’s publishable with minimal effort, and the reader can look at it, print it, download and store it, and share it with others. Example: a data table that has been published as PDF.
  • Two stars: this is where the data is structured and published in a format that the reader can process with software that accesses and works with those structures. The example given was a Microsoft Excel spreadsheet. If you have Excel you can perform calculations on the data and export it to other structured formats. Other two-star examples could be distributing a presentation slide set as PowerPoint, or a document as Word (though when it comes to presentational forms, there are font and other dependencies that can trip us up).
  • Three stars: this is where the structure of a data document has been preserved, but in a non-proprietary format. The example given was of an Excel spreadsheet exported as a CSV file (comma-separated values format, a text file where certain characters are given the role of indicating field boundaries, as in Dion’s example above). [Perhaps the edges of this category have been abraded by software suites such as OpenOffice and LibreOffice, which themselves use non-proprietary formats, but can open Microsoft-format files.]
  • Four stars: this is perhaps the most difficult step to explain, and is when you put the data online in a graph database format, using open standards such as Resource Description Framework (RDF), as described above. For the publisher, this is no longer such a simple process and requires thinking about structures, and new conversion and authoring processes. The advantage to the users is that the links between the entities can now be explored as a kind of extended web of facts, with semantic relationships constructed between them.
  • Five stars: this is when Linked Data graph databases, structured to RDF standards, ‘open up’ beyond the enterprise, and establish semantic links to other such open databases, of which there are increasingly many. This is Linked Open Data! (Note that a Linked Data collection held by an enterprise could be part-open and part-closed. There are often good commercial and security reasons for not going fully open.)

This hierarchy is explained in greater detail at

Dave suggested that if we want to understand how many organisations currently participate in the ‘Linked Open Data Cloud’, and how they are linked, we might visit, where there is an interactive and zoomable SVG graphic version showing several hundred linked databases. The circles that represent them are grouped and coloured to indicate their themes and, if you hover your cursor over one circle, you will see an information box, and be able to identify the incoming and outgoing links as they flash into view. (Try it!)

The largest and most densely interlinked ‘galaxy’ in the LOD Cloud is in the Life Sciences; other substantial ones are in publishing and librarianship, linguistics, and government. One of the most central and most widely linked is DBpedia, which extracts structured data created in the process of authoring and maintaining Wikipedia articles (e.g. the structured data in the ‘infoboxes’). DBpedia is big: it stores nine and a half billion RDF triples!


Screen shot taken while zooming into the heart of the Linked Open Data Cloud (interactive version). I have positioned the cursor over ‘’ for this demonstration. This brings up an information box, and lines which show links to other LOD sites: red links are ‘incoming’ and green links are ‘outgoing’.

The first case study Dave presented was an experiment conducted by his company Synaptica to enhance discovery of people in the news, and stories about them. A ready-made LOD resource they were able to use was DBpedia’s named graph of people. (Note: the Named Graphs data model is a variant on the RDF data model,: it allows RDF triples to talk about RDF graphs. This creates a level of metadata that assists searches within a graph database using the SPARQL query language).

Many search and retrieval solutions focus on indexing a collection of data and documents within an enterprise – ‘in a box’ if you like – and providing tools to rummage through that index and deliver documents that may meet the user’s needs. But what if we could also search outside the box, connecting the information inside the enterprise with sources of external knowledge?

The second goal of this Synaptica project was about what it could deliver for the user: they wanted search to answer questions, not just return a bunch of relevant electronic documents. Now, if you are setting out to answer a question, the search system has to be able to understand the question…

For the experiment, which preceded the 2016 US presidential elections, they used a reference database of about a million news articles, a subset of a much larger database made available to researchers by Signal Media ( Associated Press loaned Synaptica their taxonomy collection, which covers more than 200,000 concepts covering names, geospatial entities, news topics etc. – a typical and rather good taxonomy scheme.

The Linked Data part was this: Synaptica linked entities in the Associated Press taxonomy out to DBpedia. If a person is famous, DBpedia will have hundreds of data points about that person. Synaptica could then build on that connection to external data.

SHOWING HOW IT WORKS. Dave went online to show a search system built with the news article database, the AP taxonomy, and a link out to the LOD cloud, specifically DBpedia’s ‘persons’ named graph. In the search box he typed ‘Obama meets Russian President’. The results displayed noted the possibility that Barack or Michelle might match ‘Obama’, but unhesitatingly identified the Russian President as ‘Vladimir Putin’ – not from a fact in the AP resource, but by checking with DBpedia.

As a second demo, he launched a query for ‘US tennis players’, then added some selection criteria (‘born in Michigan’). That is a set which includes news stories about Serena Williams, even though the news articles about Serena don’t mention Michigan or her birth-place. Again, the link was made from the LOD external resource. And Dave then narrowed the field by adding the criterion ‘after 1980’, and Serena stood alone.

It may be, noted Dave, that a knowledgeable person searching a knowledgebase, be it on the Web or not, will bring to the task much personal knowledge that they have and that others don’t. What’s exciting here is using a machine connected to the world’s published knowledge to do the same kind of connecting and filtering as a knowledgeable person can do – and across a broad range of fields of knowledge.

NATURAL LANGUAGE UNDERSTANDING. How does this actually work behind the scenes? Dave again focused on the search expressed in text as ‘US tennis players born in Michigan after 1980’. The first stage is to use Natural Language Understanding (NLU), a relative of Natural Language Processing, and long considered as one of the harder problem areas in Artificial Intelligence.

The Synaptica project uses NLU methods to parse extended phrases like this, and break them down into parts of speech and concept clusters (‘tennis players’, ‘after 1980’). Some of the semantics are conceptually inferred: in ‘US tennis players’, ‘US’ is inferred contextually to indicate nationality.

On the basis of these machine understandings, the system can then launch specific sub-queries into the graph database, and the LOD databases out there, before combining them to derive a result. For example, the ontology of DBpedia has specific parameters for birth date, birthplace, death date, place of death… These enhanced definitions can bring back the lists of qualifying entities and, via the AP taxonomy, find them in the news content database.

Use case: understanding symbolism inside art images

Dave’s second case study concerned helping art history students make searches inside images with the aid of a Linked Open Data resource, the Getty Art and Architecture Thesaurus.

A seminal work in Art History is Erwin Panofsky’s Studies in Iconology (1939), and Dave had re-read it in preparation for building this application, which is built on Panofskyan methods. Panofsky describes three levels of analysis of iconographic art images:

  • Natural analysis gives a description of the visual evidence. It operates at the level of methods of representation, and its product is an annotation of the image (as a whole, and its parts).
  • Conventional analysis (Dave prefers the term ‘conceptual analysis’) interprets the conventional meanings of visual components: the symbolism, allusions and ideas that lie behind them. This can result in semantic indexing of the image and its parts.
  • Intrinsic analysis explores the wider cultural and historical context. This can result in the production of ‘knowledge graphs’



Detail from the left panel of Hieronymous Bosch’s painting ‘The Garden of Earthly Delights’, which is riddled with symbolic iconography.


The educational application which Synaptica built is called Linked Canvas (see Their first step was to ingest the art images at high resolution. The second step was to ingest linked data ontologies such as DBpedia, Europeana, Wikidata, Getty AAT, Library of Congress Subject Headings and so on.

The software system then allows users to delineate Points of Interest (POIs), and annotate them at the natural level; the next step is the semantic indexing, which draws on the knowledge of experts and controlled vocabularies.
Finally users get  to benefit from tools
for search and exploration of the
annotated images.

With time running tight, Dave skipped straight to some live demos of examples, starting with the fiendishly complex 15th century triptych painting The Garden of Earthly Delights. At Panofsky’s level of ‘natural analysis’, we can decompose the triptych space into the left, centre and right panels. Within each panel, we can identify ‘scenes’, and analyse further into details, in a hierarchical spatial array, almost the equivalent of a detailed table of contents for a book. For example, near the bottom of the left panel there is a scene in which God introduces Eve to Adam. And within that we can identify other spatial frames and describe what they look like (for example, God’s right-hand gesture of blessing).

To explain semantic indexing, Dave selected an image painted 40 years after the Bosch — Hans Holbein the Younger’s The Ambassadors, which is in the National Gallery in London. This too is full of symbolism, much of it carried by the various objects which litter the scene, such as a lute with a broken string, a hymnal in a translation by Martin Luther, a globe, etc. To this day, the meanings carried in the painting are hotly debated amongst scholars.

If you zoom in and browse around this image in Linked Canvas, as you traverse the various artefacts that have been identified, the word-cloud on the left of the display changes contextually, and what this reveals in how the symbolic and contextual meanings of those objects and visual details have been identified in the semantic annotations.

An odd feature of this painting is the prominent inclusion in the lower foreground of an anamorphically rendered (highly distorted) skull. (It has been suggested that the painting was designed to be hung on the wall of a staircase, so that someone climbing the stairs would see the skull first of all.) The skull is a symbolic device, a reminder of death or memento mori, a common visual trope of the time. That concept of memento mori is an element within the Getty AAT thesaurus, and the concept has its own URI, which makes it connectable to the outside world.

Dave then turned to Titian’s allegorical painting Bacchus and Ariadne, also from the same period and also from the National Gallery collection, and based on a story from Ovid’s Metamorphoses. In this story, Ariadne, who had helped Theseus find his way in and out of the labyrinth where he slew the Minotaur, and who had become his lover, has been abandoned by Theseus on the island of Naxos (and in the background if you look carefully, you can see his ship sneakily making off). And then along comes the God of Wine, Bacchus, at the head of a procession of revellers and, falling in love with Ariadne at first glance, he leaps from the chariot to rescue and defend her.

Following the semantic links (via the LOD database on Iconography) can take us to other images about the tale of Ariadne on Naxos, such as a fresco from Pompeii, which shows Theseus ascending the gang-plank of his ship while Ariadne sleeps. As Dave remarked, we generate knowledge when we connect different data sets.

Another layer built on top of the Linked Canvas application was the ability to create ‘guided tours’ that walk the viewer around an image, with audio commentary. The example Dave played for us was a commentary on the art within a classical Greek drinking-bowl, explaining the conventions of the symposium (Greek drinking party). Indeed, an image can host multiple such audio commentaries, letting a visitor experience multiple interpretations.

In building this image resource, Synaptica made use of a relatively recent standard called the International Image Interoperability Framework (IIIF). This is a set of standardised application programming interfaces (APIs) for websites that aim to do clever things with images and collections of images. For example, it can be used to load images at appropriate resolutions and croppings, which is useful if you want to start with a fast-loading overview image and then zoom in. The IIIF Search API is used for searching the annotation content of images.

Searching within Linked Canvas is what Dave described as ‘Level Three Panofsky’. You might search on an abstract concept such as ‘love’, and be presented us with a range of details within a range of images, plus links to scholarly articles linked to those.

Post-Truth Forum

As a final example, Dave showed us, which is an ontology of concepts around the ideas of ‘fake news’ and the ‘post-truth’ phenomenon, with thematically organised links out to resources on the Web, in books and in journals. Built by Dave using Synaptica Graphite software, it is Dave’s private project born out of a concern about what information professionals can do as a community to stem the appalling degradation of the quality of information in the news media and social media.

For NetIKX members (and for readers of this post), going to Dave’s Post Truth Forum site is also an opportunity to experience a public Linked Open Data application. People may also want to explore Dave’s thoughts as set out on his blog,

Taxonomies vs Graphs

In closing, Dave wanted to show a few example that might feed our traditional post-refreshment round-table discussions. How can we characterise the difference between a taxonomy and a data graph (or ontology)? His first image was an organisation chart, literally a regimented and hierarchical taxonomy (the US Department of Defense and armed forces).

His second image was the ‘tree of life’ diagram, the phylogenetic tree that illustrates how life forms are related to each other, and to common ancestor species. This is also a taxonomy, but with a twist. Here, every intermediate node in the tree not only inherits characteristics from higher up, but also adds new ones. So, mammals have shared characteristics (including suckling young), placental mammals add a few more, and canids such as wolves, jackals and dogs have other extra shared characteristics. (This can get confusing if you rely too much on appearances: hyenas look dog-like, but are actually more closely related to the big cats.)

So the Tree of Life captures systematic differentiation, which a taxonomy typically cannot. However, said Dave, an ontology can. In making an ontology we specify all the classes we need, and can specify the property sets as we go. And, referring back to Dion’s presentation, Dave remarked that while ontologies do not work easily in a relational database structure, they work really well in a graph database. In a graph database you can handle processes as well as things and specify the characteristics of both processes and things.

Dave’s third and final image was of the latest version of the London Underground route diagram. This is a graph, specifically a network diagram, that is characterised not by hierarchy, but by connections. Could this be described in a taxonomy? You’d have to get rid of the Circle line, because taxonomies can’t end up where they started from. With a graph, as with the Underground, you can enter from any direction, and there are all sorts of ways to make connections.

We shouldn’t think of ditching taxonomies; they are excellent for some information management jobs. Ontologies are superior in some applications, but not all. The ideal is to get them working together. It would be a good thought-experiment for the table groups to think about what, in our lives and jobs, are better suited to taxonomic approaches and what would be better served by graphs and ontologies. And, we should think about the vast amounts of data out there in the public domain, and whether our enterprises might benefit from harnessing those resources.


Following NetIKX tradition, after a break for refreshments, people again settled down into small table groups. We asked participants to discuss what they had heard and identify either issues they thought worth raising, or thinks that they would like to know more about.

I was chairing the session, and I pointed out that even if we didn’t have time in subsequent discussion to feed everyone’s curiosity, I would do my best to research supplementary information to add to this account which you are reading.

I ran the audio recorder during the plenary discussion, so even though I was not party to what the table groups had discussed internally, I can report with some accuracy what came out of the session. Because the contributions jumped about a bit from topic to topic, I have resequenced them to make them easier for the reader to follow.

AI vs Linked Data and ontologies?

Steve Dale wondered if these efforts to compile graph databases and ontologies was worth it, as he believed Artificial Intelligence is reaching the point where a computer can be thrown all sorts of data – structured and unstructured – and left to figure it out for itself through machine learning algorithms. Later, Stuart Ward expressed a similar opinion. Speaking as a business person, not a software wizard, he wonders if there is anything that he needs to design?

Conrad, in fielding this question, mentioned that on the table he’d been on (Dave Clarke also), they had looked some more into the use in Dave’s examples of Natural Language Understanding; that is a kind of AI component. But they had also discussed the example of the Hieronymous Bosch painting. Dave himself undertook the background research for this and had to swot up by reading a score of scholarly books. In Conrad’s opinion, we would have to wait another millennium before we’d have an AI able to trace the symbolism in Bosch’s visual world. Someone else wondered how one strikes the right balance between the contributions of AI and human effort.

Later, Dave Clarke returned to the question; in his opinion, AI is heavily hyped – though if you want investment, it’s a good buzz-word to throw about! So-called Artificial Intelligence works very well in certain domains, such as pattern recognition, and even with images (example: face recognition in many cameras). But AI is appalling at semantics. At Synaptica, they believe that if you want to create applications using machine intelligence, you must structure your data. Metadata and ontologies are the enablers for smart applications.

Dion responded to Stuart’s question by saying that it would be logical at least to define what your entities are – or at least, to define what counts as an entity, so that software can identify entities and distinguish them from relationships. Conrad said that the ‘predicates’ (relationships) also need defining, and in the Linked Data model this can be assisted if you link out to publicly-available schemas.

Dave added that, these days, in the Linked Data world, it has become pretty easy to adapt your database structures as you go along. Compared to the pain and disruption of trying to modify a relational database, it is easy to add new types of data and new types of query to a Linked Data model, making the initial design process less traumatic and protracted.

Graph databases vs Linked Open Data?

Conrad asked Dave to clarify a remark he had made at table level about the capabilities of a graph database product like Neo4j, compared with Linked Open Data implementations.

Dave explained that Neo4j is indeed a graph database system, but it is not an RDF database or a Linked Data database. When Synaptica started to move from their prior focus on relational databases towards graphical databases, Dave became excited about Neo4j (at first). They got it in, and found it was a wonderfully easy system to develop with. However, because its method of data modelling is not based on RDF, Neo4j was not going to be a solution for working with Linked Data; and so fervently did Dave believe that the future is about sharing knowledge, he pulled the plug on their Neo4j development.

He added that he has no particular axe to grind about which RDF database they should use, but it has to be RDF-conforming. There are both proprietary systems (from Oracle, IBM DB2, OntoText GraphDB, MarkLogic) and open-source systems (3store, ARC2, Apache Jena, RDFLib). He has found that the open-source systems can get you so far, but for large-scale implementations one generally has to dip into the coffers and buy a licence for something heavyweight.

Even if your organisation has no intention to publish data, designing and building as Linked Data lets you support smart data and machine reasoning, and benefit from data imported from Linked Open Data external resources.

Conrad asked Dion to say more about his experiences with graph databases. He said that he had approached Tableau, who had provided him with sample software and sample datasets. He hadn’t yet had a change to engage with them, but would be very happy to report back on what he learns.

Privacy and data protection

Clare Parry raised issues of privacy and data protection. You may have information in your own dataset that does not give much information about people, and you may be compliant with all the data protection legislation. However, if you pull in data from other datasets, and combine them, you could end up inferring quite a lot more information about an individual.

(I suppose the answer here is to do with controlling which kinds of datasets are allowed to be open. We are on all manner of databases, sometimes without suspecting it. A motor car’s registration details are held by DVLA, and Transport for London; the police and TfL use ANPR technology to tie vehicles to locations; our banks have details of our debit card transactions and, if we use those cards to pay for bus journeys, that also geolocates us. These are examples of datasets that by ‘triangulation’ could identify more about us than we would like.)


Graham Robertson reported that on his table they discussed what the difference is between URLs and URIs…

(If I may attempt an explanation: the wider term is URI, Uniform Resource Identifier. It is ‘uniform’ because everybody is supposed to use it the same way, and it is supposed uniquely and unambiguously to identify anything which might be called a ‘resource’. The Uniform Resource Locator (URL) is the most common sub-type of URI, which says where a resource can be found on the Web.

But there can be other kinds of resource identifiers: the URN (Uniform Resource Name) identifies a resource that can be referenced within a controlled namespace. Wikipedia gives as an example ISBN 0-486-27557-4, which refers to a specific edition of Shakespeare’s Romeo and Juliet. In the MeSH schema of medical subject headings, the code D004617 refers to ‘embolism’.)


Some people had discussed the issue of the trustworthiness of external data sources to which one might link – Wikipedia (and WikiData and DBpedia) among them, and Conrad later asked Mandy  to say more about this. She wondered about the wisdom of relying on data which you can’t verify, and which may have been crowdsourced. But Dave has pointed out that you might have alternative authorities that you can point to. Conrad thought that for some serious applications one would want to consult experts, which is how the Getty AAT has been built up. Knowing provenance, added David Penfold, is very important.

The librarians ask: ontologies vs taxonomies?

Rob Rosset’s table was awash with librarians, who tend to have an understanding about what is a taxonomy and what an ontology. How did Dave Clarke see this, he asked?

Dave referred back to his closing three slides. The organisational chart he had shown is a strict hierarchy, and that is how taxonomies are structured. The diagram of the Tree of Life is an interesting hybrid, because it is both taxonomic and ontological in nature. There are things that mammals have in common, related characteristics, which are different from what other groupings such as reptiles would have.

But we shouldn’t think about abandoning taxonomy in favour of ontology. There will be times where you want to explore things top-down (taxonomically), and other cases where you might want to explore things from different directions.

What is nice about Linked Data is that it is built on standards that support these things. In the W3C world, there is the SKOS standard, Simple Knowledge Organization Systems, very light and simple, and there to help you build a taxonomy. And then there is OWL, the Web Ontology Language, which will help you ascend to another level of specificity. And in fact, SKOS itself is an ontology.

Closing thoughts and resources

This afternoon was a useful and lively introduction to the overlapping concepts of Graph Databases and Linked Data, and I hope that the above account helps refresh the memories of those who attended, and engage the minds of those who didn’t. Please note that in writing this I have ‘smuggled in’ additionally-researched explanations and examples, to help clarify matters.

Later in the year, NetIKX is planning a meeting all about Ontologies, which will be a way to look at these information and knowledge management approaches from a different direction. Readers may also like to read my illustrated account of a lecture on Ontologies and the Semantic Web, which was given by Professor Ian Horrocks to a British Computer Society audience in 2005. That is still available as a PDF from

Ontologies, taxonomies and knowledge organisation systems are meat and drink to the UK Chapter of the International Society for Knowledge Organization (ISKO UK), and in September 2010 ISKO UK held a full day conference on Linked Data: the future of knowledge organization on the Web. There were nine speakers and a closing panel session, and the audio recordings are all available on the ISKO UK Web site, at

Recently, the Neo4j team produced a book by Ian Robinson, Jim Webber and Emil Eifrem called ‘Graph Databases’, and it is available for free (PDF, Kindle etc) from Or you can get it published in dead-tree form from O’Reilly Books. See

The Future of Work for Information and Knowledge Professionals

To celebrate the tenth anniversary of NetIKX, the November 2017 meeting was opened free of charge to people from related knowledge and information management organisations. It featured two speakers, Peter Thomson and Stuart Ward, and an extended panel session with questions from the audience.

Peter Thomson on the changing world of work

Peter Thomson is a consultant who advises clients about how to create a corporate culture supporting new and better working practices. He is the author of ‘Future Work’, a visiting Executive Fellow at Henley Business College, and the Research Director of the Telework Association. Amongst his current engagements, he is helping the Health Foundation to launch a community of 1,000 health improvement practitioners, and working with Medécins sans Frontières on developing a knowledge-based evaluation process for their humanitarian response missions.

Thirty years ago he worked for Digital Equipment Corporation (DEC), known for its PDP and Vax lines of multi-user minicomputers. DEC experimented with new ways of networking, and with long-distance working. Surely, they thought, nobody in the 21st century would come to work getting stuck in traffic jams or packed into suburban trains – they would be sitting comfortably at home and ‘teleworking’ – it was a big buzzword at that time, but is now pretty much extinct.

With the benefit of hindsight, Peter notes that technology has changed but people haven’t. Human behaviour is full of ingrained habits, especially so amongst leaders of organisations. So we have the absurdity of people being forced to commute to sit at a desk, and send emails to the person a few metres away.

The younger generation is beginning to question why we continue with these outmoded working practices. The absurdity persists because business leaders want their team around them, under their eye, in a ‘command and control’ notion of how to run a business.

New tech, old habits

He asked: most of the audience have a smartphone, yes? How many had used it that day to actually telephone somebody – compared to sending or reading email, or other text based message? A show of hands showed that the latter was more prevalent than the former.

Although mobile devices and related technologies are now part of our everyday lives, and the world has become more complex, many of our practices in the world of work are still trying to catch up. Businesses may boast of being Agile, but many of the actual processes are Fragile, he said.

Business communication is spread across a spectrum of behaviours. People still like to get together physically in groups to discuss things. In that setting they employ not only words, but also nuances of expression, gesture, body language and so on. At the other end of the spectrum is email: asynchronous, and text-based (with quite a narrow medium of expression). Ranged in the middle we have such things are videoconferencing, audio conference calls, Skype etc.

Daily business communication is conducted mostly by typing emails – probably slowly – then checking for typing mistakes. Wouldn’t it be quicker to use new technology to send a quick video message? It’s technically possible these days. Look at how people have adopted WhatsApp in their personal lives. But the corporate default is face-to-face physical meetings, email at the other end, and nothing in between. Indeed, the social media tools by which people communicate daily in ordinary life are banned from many workplaces. And then people complain of having too many emails and too many meetings.

Tyranny of 24/7 and the overwork culture

Many people today are the victims of ‘presenteeism’. If you are not already at your desk and working when your managers show up, or if you leave your desk before they do, they won’t be impressed. They can’t sack you for sticking to the hours you are contracted for, but you’ll probably be passed over for promotion. Even if you’re the one who comes up with the best creative ideas, that’s regarded as incidental, secondary to the quantitative measure of how many hours you work.

This has now extended into, or been replaced by, a form of virtual presenteeism. Knowledge and administration work can now be done anywhere. So now we have digital presenteeism, 24/7. ‘Please take your laptop on holiday with you, in case there’s an emergency – and check in every day.’ Or, ‘I tend to send emails over the weekend, and while I don’t insist that you reply to them immediately, those who do are the ones I’ll favour.’ These leadership behaviours force people to be in work mode round the clock. It all builds stress, which the World Health Organization says is a 21st century epidemic.

But many people now won’t work under these conditions. They’d rather quit and set up their own business, or join the ‘gig economy’. They want to own their own time. If you got used to budgeting your time how you see fit, for example at university, you don’t want to be treated like a child by an employer, and be told when to do what.

The typical contract of employment is not about what you achieve – it’s about your hours of work, and you are paid according to the time you spend at work. For example, consider a mother who returns from maternity leave and agrees to work a four-day week rather than five. She benefits from having the extra time; the employer may also benefit, because in trying to get the same work done in four rather than five days a week, she probably skips unproductive meetings and spends less time chatting. But after a while, she finds that however productive she is, she’s being paid four-fifths of what her colleagues are.

At a national level, Peter commented, Britain is quite low on the productivity scale, and yet our working hours are so long.

Challenges to permanent employment

There has been a trend towards outsourcing and subcontracting: consider call centres in India or companies having products made in China. Will there now be a second wave of this, at the management and administration level, in which inefficient layers are taken out of corporate organisations and the organisation gets the professional inputs it needs from subcontractors?

We’re seeing the collapse of the pension paradigm. The conventional model is predicated on the idea of ‘superannuation’, and a few years of retirement before you die. But with today’s longer lifespans, thinking of seniors as being too old to contribute knowledge and skills is increasingly untenable — and anyway, it’s proving impossible to fund a long retirement from the proceeds of 40 years of employment. Nor can the State pension scheme fund extended pensions from the taxes paid by (a declining proportion of) younger people in work. Is retirement then an antiquated idea?

Peter closed by wondering what people’s ideal workplace might be — where they are at their most creative. Within the audience, people mentioned a variety of domestic settings, plus walking and driving. Peter imagines the organisation of the future as a network of people, working in such conducive conditions, and connected by all the liberating means that technology can bring us. Are we ready for this new world?

Stuart Ward on KIM and adding value to organisations

Stuart was the first chair of NetIKX and has been involved with our community throughout. The first meeting of NetIKX was addressed by David Skyrme, who spoke about the value of knowledge and information management (KIM for short, hereafter); that would also be the main focus of his presentation. He believes that it can be challenging for KIM professionals (KIPs) to prove their value to the organisations in which they work.

Knowledge and information are the life-blood of organisations; those who use them well will prosper, those who don’t will wither. From that, one might expect the KIPs to be highly valued, but often it is not the case.

Stuart identifies four things he thinks are important if KIPs are to survive and flourish in an organisation. They are: to focus on creating value; to link KIM activities to the organisation’s goals and objectives; to be clear about everyone’s responsibilities in relation to KIM (and there are various such roles, whether in creating and disseminating information products, or managing data and information resources). Finally, the organisation must have the right structures, skills and culture to make best use of what KIM can provide.

‘Value’ means different things in different enterprises. Commerce will focus on value for shareholders and other stakeholders, and customer service. In the public sector, and for non-profits, value could mean packaging information so that citizens and other service users can make best use of it.

A six-part model

Stuart has long promoted a model that is structured around six mechanisms through which information and knowledge can be used to deliver value to an organisation. They are:

  • developing information and knowledge products which can be marketed;
  • helping to drive forward the business strategy;
  • enabling organisational flexibility and change;
  • improving corporate decision making;
  • enabling and improving all business processes, especially the key ones; and
  • enhancing the organisation’s ‘intellectual capital’.

Looking at these in turn…

Information and knowledge products: Some businesses (publishers, media companies, law firms etc) create products for sale to the public or to other businesses. Others, such as local government or non-profits, produce reports and studies: though not for sale, these are crucial in their work of informing the public, influencing government or what have you.

Driving business strategy and renewal: Organisations often must change to survive, and here KIM can deliver value by enabling innovation. Apple Computer almost hit the rocks a couple of times, but through KIM and innovative product and service design became highly profitable. It’s important to sense the direction the market is headed: Blackberry and Nokia are examples of companies which failed to do that.

Enabling organisational change and flexibility: Good KIM helps an organisation to be sensitive to changing business opportunities and risks, to improve efficiency and cut costs. Here, the last thing one needs is to have knowledge trapped within silos. Efficient sharing of knowledge and information across the organisation is key.

Improving decision making: The mantra is, ‘Get the right information at the right time, to the right people.’ Good decision making requires an understanding of options available, and the consequences of making those choices – including the risks. Bad decisions are often made because of the prejudices of the decision-makers, who have the power to prevail in the face of evidence, so it’s important that the organisation has the right cultural attitudes towards decision-making, knowledge and information.

Continuous improvement: Almost always, business processes could be done better, and proper curation of information and knowledge is the key to this. Good ideas need not be limited to a discrete business process, and can inspire changes in other activities.

Enhancing intellectual capital: One of the most important realisations in KIM is that, just as money and equipment and premises and people are assets to a business, so are information and knowledge, and they should be managed properly. Yet many organisations don’t have an overview of what those intellectual assets are. As engineer Lewis Platt, 1990s CEO of Hewlett-Packard once said, ‘If we only knew what we know, we’d be three times more profitable.’ (Platt was also famous for his practice of ‘management by walking around’, immersing himself in the operational side of the business rather than staying aloof in his office.)

Linking KIM to key goals: the benefits matrix

Stuart then proposed a methodology for fitting IM and KM to an organisation’s key goals and objectives. As a tool, he recommends a ‘benefits matrix’ diagram, somewhat like a spreadsheet, in which the row headings define the organisation’s goals, its aims and objectives, while column headings define existing or possible future KIM services and capabilities. This creates an array of cells, each one of which represents how one particular KIM service or capability maps to one of the organisation’s goals. In these cells, you can then list the benefits, which may be quantifiable (e.g. increased income, reduced cost) or unquantifiable.

Stuart gave the example of an organisation having a Document Management System (represented as a column on the matrix). How might that map across to the company’s goal of reducing overhead costs? Well, a quantifiable result might be the saving of £160K a year, while unquantifiable benefits could include faster access to information, and a reduced risk of losing critical information.

Stuart likes this kind of exercise, because it stimulates thinking about the way in which information and knowledge management initiatives can generate benefits for the organisation’s self-identified objectives. It isn’t narrow-minded with a focus only on quantifiable benefits, though it does stimulate thinking about how one might define metrics, valuable for monitoring results. Finally, it is strongly visual and easy to assimilate, and as such is good for engaging with senior management.

Responsibilities, capabilities

It would be a mistake to assume that KIM responsibilities attach only to people explicitly employed for a KIM role. Business leaders also have strategic responsibilities for KIM, and every ‘ordinary’ worker has KIM responsibilities too. There are special defined responsibilities for those with ‘steward’ roles, for those who create and manage information and knowledge resources as their main job, and also those who manage IT and other kinds of infrastructure and services which help to manage and deliver the resources to where they are needed.

Stuart’s slide set included several detailed bullet-point slides for the KIM responsibilities that might attach to these various roles, but we skipped over a detailed examination these due to pressure of time. [The slide set is available at to NetIKX members and to those who attended the meeting.]

A cyclical process

Stuart’s final diagram suggested that there is a cyclical process between two spheres: the first sphere represents the organisation’s data and information, and what it knows. Through good management and use of these resources, the organisation hopefully performs well and succeeds in the second sphere, that of action. By monitoring performance, the organisation can learn from experience, and that feeds back to the first sphere.

Learning from the Hawley Committee

In 1995 the Hawley Committee, chaired by Sir Robert Hawley, under the auspices of the KPMG IMPACT Programme, published the results of an investigation into information management, and the value assigned to information, in several large UK businesses. Entitled ‘Information as an Asset – The Board Agenda’, the report set out ten agenda points, of which three have to do with responsibilities of the Board of Management. CILIP have recently shown a renewed interest in the Hawley Report, and may soon republish it, with updates to take account of changes between then and now.

Panel Q&A session

The panel discussion session had something of a BBC ‘Any Questions’ flavour. Before the session, people sat in table groups and came up with written questions, which Panel chair David Penfold then collected and collated during our tea break. David then called for questions which were similar to be put to the panel, which consisted of Noeleen Schenk of Metataxis, David Gurteen, David Smith (Government KIM Head of Profession), Karen McFarlane (Chair of CILIP Board), David Haynes (chair of ISKO UK) and Steve Dale.

Will KIM professionals become redundant?

Stuart Ward asked for the panel’s opinion about whether knowledge and information management professionals might soon be redundant, as their skills are diffused to a wider group of people through their exposure to technology in school and university. Joanna asked how we can create knowledge from the exponentially growing amount of information; and Alison wondered if the information available to all on Wikipedia is good enough; or are we looking for a perfect solution which doesn’t exist?

Steve Dale responded mostly to Stuart’s question. He has observed how in most of the organisations with which he works, KIM functions are being spread around the organisation. Organisations like Cadbury’s, the British Council and PWC no longer have a KIM department per se. Knowledge has become embedded in the organisation. But Steve still sees a future role for KIM professionals (or their equivalent – they may be called something else) as organisations turn to machine-augmented decision-making, ‘big data’, and machine learning.

Consider machine learning, in which computer systems are fed with truckloads of data, and process that to discover patterns and connection. If there is bias in the data, there will be bias in the outcomes – who is checking for that? This is where wise and knowledgeable humans can and should intervene to manage the quality of the data, and to ensure that any ‘training set’ is truly representative.

Karen McFarlane also responded to Stuart’s question. With her GCHQ and National Cybersecurity Centre background, she sees a continued and deepening need for skills in data and information governance, information assurance and cyber-security; also in information risk management, and data quality. KIM professionals have those kinds of skills. As for Stuart’s assertion that exposure to technology at university is enough to impart those skills – she thinks that is definitely not the case. Such people often don’t know how to manage the information on their desktops [let alone at an enterprise level].

Noeleen Schenk in contrast replied that she didn’t think it should be necessary to teach people how to manage the information they work with, so long as there were rule-based technical systems to do information governance automatically (for example, though auto-categorisation). But who will write the rules? That’s where the future of KIM work may lie.

David Haynes offered the perspective of someone teaching the next generation of KIM professionals (at Dundee and at City University of London). He is impressed by the diversity of backgrounds among people drawn to take courses in Library and Information Studies, and Archives and Records Management: it shows how relevant these skills are to many fields of human activity. He would like to see in future a larger emphasis on information governance skills, because many LIS-trained people go on to take up roles as data protection officers, or working on compliance matters.

David Smith thinks KIM professionals do risk extinction if they don’t change. He remembers that when he joined the Civil Service, doing online search was an arcane craft for specialists – that’s gone! He agrees that information governance is a key skill. Could anyone do that? Yes… Would they make mistakes? Certainly! This is where KIM professionals should be asserting their skills and gaining a reputation for being the go-to person for help in such matters.

David Gurteen doesn’t think the need to manage information and knowledge will go away – quite the reverse. One recently-arising topic has been that of ‘fake news’ and bias, which for him highlights the need for information and media literacy skills and judgement to be taught and learnt.

Training the robots?

Claire Parry referred to the greater availability today of information which has been structured to be ‘understandable’ by machines as well as by humans. What did the panel think might be KIM professionals’ roles in training the machines, and dealing with ‘bots’.

Steve Dale said that artificial intelligence has been a big interest of his in recent years. A lot of young people are out there coding algorithms, and some machines are even crafting their own through machine learning. That’s fine for game-playing, but in matters of more importance, affecting the lives of citizens, we must be concerned when machines evolve their own algorithms that we don’t understand. The House of Commons Science and Technology Committee is requesting information from organisations creating these kinds of tools, so they can consider the implications. Steve said that, when some algorithm is being used to augment decision-making in a way which affects him, he wants to know about it, and about what data is being used to inform it.

Rob Rosset wondered whether it is possible to create an algorithm that does not have some form of bias within it. David Gurteen thought ‘bias’ was inevitable, given that programming always proceeds from assumptions.  Noeleen Schenk thought that good data governance could at least reveal to us the provenance and quality of the data being used to inform decisions. David Haynes agreed, and referred to the ‘ethics of information’, noting that CILIP’s model of the Wheel of Knowledge places ethics at the very centre of that diagram.

Steve Dale mentioned he had just been at an event about whether AI will lead to job losses, and people there discussed the algorithms that Facebook uses. Facebook now realises that they can’t detect online abuse algorithmically, so are in the process of recruiting 10,000 humans to moderate the algorithms! So the adoption of AI maybe bringing up job opportunities?

The gig economy; face-to-face vs virtual working

David Penfold brought forward four questions which he thought might be related to each other.

Kathy Jacob asked, what impact will ‘gig economy’ workforce arrangements have on knowledge and information work in the future? Particularly in the aspects of knowledge creation and use. Valerie Petruk asked, is the gig economy a necessary evil? Sarah Culpin wondered how to get the right balance between face-to-face interactions and virtual working spaces; and Jordana Moser similarly wondered how we organise to meet human needs as well as the demands of efficiency and productivity. For example, a face-to-face meeting may not be the most efficient way of getting work done, but has value on other levels.

David Smith thought that the ‘gig economy’ probably is a necessary evil. Records management for government has become increasing commoditised: when a task emerges, you buy in people to do it, then they go. It’s a balancing act, because some work is more appropriately done by people on the payroll, and some doesn’t have to be. Procurement skills have therefore become more important – deciding what work you keep in-house, and what you farm out, or get people in for on a temporary basis.

David Haynes noted the loss of rights that comes along with the ‘gig economy’ – being employed has benefits for people. He himself has been both employed and self-employed – it’s worked out just fine for him, but people engaged for more routine tasks can be easily exploited; when they are ill, they aren’t paid; they don’t get holiday pay, etc. Peter Thompson in his talk had proposed being ‘paid by the piece’ rather than for time on the job, but David thinks that going down this path not only imposes on individuals, but brings a cost to the whole of society too.

Noeleen Schenk finds that a ‘gig economy’ approach suits her, because she likes a portfolio lifestyle. If you combine it with the Internet’s opportunities for long-distance working, it’s brilliant that an enterprise can find someone with just the skills they want, who can provide that service from the other side of the world.

Moving to address Kathy Jacobs’s question directly, Noeleen thinks that knowledge capture will move from writing things down towards voice capture, plus voice-to-text conversion, such that there will be fewer low-grade tasks to be assigned to temporary workers. However, what gig work methods do risk losing is the organisational knowledge that comes with continuity of shared experience in the enterprise.

Karen McFarlane said that we need both face-to-face and distant working. We are humans; we work best in a human way. We can blend in virtual meetings, virtual communities; but these virtualised relationships always work best if you have met the other person(s) in real life.

David Gurteen is definitely in favour of face to face conversation. He has been experimenting holding his Knowledge Café meetings using Zoom technology, but he thinks, if you can meet face to face, it’s better. Doing it remotely is something you do if you have to. Nancy Dixon talks about the ‘oscillation principle’ – if you have a geographically dispersed team, every so often you have to bring them together (see her blog post at – she talks about ‘blend[ing] sophisticated virtual tools with periodic, in-depth, face-to-face collective sensemaking.’)

Recruitment criteria, and the robots (again)

Judith Stuart, who lectures at the University of the West of England, asked what skills and knowledge the panel look for in new recruits and appointments to knowledge management roles in organisations.

David Haynes replied in one word: ‘flexibility’, and other panelists agreed, David Gurteen would add ‘curiosity’. Noeleen’s answer was similar – adaptability, and the ability to cope with uncertainty.

Karen McFarlane said that when she used to recruit people to roles in records, information or knowledge management, she looked out for people who had a love of information. Yes, flexibility was also amongst her criteria, but also the inter-personal skills to be able to work in a team.

David Penfold thought it was interesting that no-one had mentioned professional skills! Karen replied that of course those were required, but her response to the question was about what would make a candidate ‘stand out’ in her eyes. Noeleen added that professional skills can be learned, but the softer skills can’t so easily.

Steve Dale referred to a company he would shortly be meeting, called HeadStart, which is using artificial intelligence and machine learning working on data (such as exam results, social media interventions) to identify candidates for organisations. They claim to shorten the time and lower the cost of getting the right people into the right jobs. He has been wondering how they would know what ‘a good result’ or ‘a bad result’ looks like…

David Haynes noted that the new data protection regulation will give people the right to challenge how decisions are made by automated systems, and to insist on human intervention if they don’t like the basis on which decisions are made.

Is it good to be averse to risk?

Anna Stothard asked for top tips or recommendations for changing a risk-averse culture, and getting more buy-in to new ideas from senior management.

David Smith remarked that government is keen on risk-aversion! Indeed the best way to get civil service management attention is to say, ‘If you want to avoid risk, do this.’ If he tells them about various benefits that a new approach could bring, he’ll be politely ignored. If he describes all sorts of bad things that could be avoided – then they are all ears (though one shouldn’t overdo it).

It all depends on your organisational culture; you need to assess management’s appetite for risk, and to make sure people understand the nature of the risks. He gave the example of a local government organisation that had turned down a Freedom of Information request on the grounds that it was ‘impertinent’, when what was underlying the response was a risk-averse culture.

Steve Dale said that in his consulting role he has had to go try to convince senior management that a change would be beneficial. His rule of thumb is to pay attention to Return on Investment (ROI); if the investment can be kept modest, the proposal is more likely to find favour.

Noeleen Schenk generally prefers to argue for change because of the benefits it will bring, but she had recently worked with a client where the concern was mostly about risk. So the project on which she was working was converted from a ‘value adding’ one to a ‘risk reduction’ one instead.

The role of knowledge organisation?

Keri Harrowven asked what role knowledge organisation plays in promoting knowledge and information management in the workplace.

Noeleen Schenk replied that, for Metataxis, knowledge organisation has a central role. But many people regard KO as an overhead, and an unnecessary expense. It takes time and effort to get KO right, but Noeleen will ask – ‘If you can’t find it, why have you got it?’ She recalled a client with about 110,000 boxes of documents in offsite storage, with next to no indexing, but they insisted they wanted to keep it all – at huge cost. She asked them, could they find anything? (No.)

Just because it is hard to do knowledge organisation, doesn’t mean that you shouldn’t. She’d say – start with some architecture, then build out. In a recent project, she has started by putting in place a set of requirements about how newly generated information is handled, first.

David Haynes noted that the UK Chapter of the International Society for Knowledge Organization often visits these topics. Like Noeleen, he thinks that there is no point in hoarding information if you can’t retrieve it. That leads to such KO questions as how you categorise information, how it is described, what metadata can be captured and attached, and what search and discovery tools you can put in place. It also goes into what the organisation’s needs are, what is the nature of the information you are faced with, and how you make that connection.

Also of increasing importance is how we can exploit information. Linked Data is an approach showing incredible potential, and new applications, such as combining map data with live sensor and update feeds – for example, the data revolution which helps Transport for London passengers know when their next bus is coming and where it is now. But none of these novel forms of exploitation would be possible without robust schemes for classifying and managing the information sources.

Finally, knowledge organisation is key to information governance.

Silos or outstations?

Someone asked: ‘Does having KM roles in an organisation create silos? How can move towards a more embedded approach?’

Karen MacFarlane described a hybrid approach in which her organisation had a central KIM team, which might be considered a silo; but she funded the placing of KIM professionals into teams of analysts for a year, helping them to develop their own information management skills. In every case, the teams that had had the benefit of working with the KIM professional wanted to find the funds to continue that work from within their own budgets.

Information governance directions?

Martin Newman wanted to know where panellists thought information governance was going, as the two initial speakers seemed to be predict new ways of working in which information roles would be decentralised.

David Haynes replied that KIM professionals are increasingly being tasked with data protection and information governance framework development. But he doesn’t think that they can work on their own. They have to work with the people on the legal side, and the people delivering the technology. It doesn’t really matter who is ‘in charge’, so long as there is that sort of coalition, and that it is embedded within the organisation.

Noeleen Schenk recounted noticing enormous variability in where information governance tasks are run from – sometimes from legal, sometimes from the executive, sometimes IT. Arguably, all is well if how people collaborate matters more to them than where people are sitting. She has been noticing a trend of information governance roles moving from the centre, along ‘spokes’, towards decentralised clusters of people; but it is even better if the way people work at every level supports good governance rather than it having to be done for them.

David Smith says that the culture of the civil service is already imbued with instinct to take good care of information. Yes, silos are there – he gave us a picture of ‘fifteen hundred silos in a single department, flying in close formation’. Teams have got smaller – to do with cuts, as much as anything else. Not every information asset is equally treated, depending upon risks attached. It’s a question of expedience, and balancing risk against cost.

His own department manages information about the European Regional Development Fund. If the European Court of Auditors asks to see information about any ERDF projects in the UK, his department has 72 hours to find it; or else there is a fine up to 10% of the value of the ERDF loan that financed that project. Imagine the prospect of a fine of £100,000 if you can’t find a file in 72 hours! You can bet the department has got that information rigorously indexed; whereas other areas are managed with a lighter touch, as they don’t carry the same risks.

There is also variability across government as to whether the work is done at the ‘hub’ or along the ‘spokes’.

Steve Dale pointed out that silos can exist for a reason – an example would be to maintain security in investment banking.

Globalism and process alignment

Emma Bahar had a question: ‘How can processes be managed in global organisations in which alignment is likely impossible?’

Steve Dale used to work for Reuters, with offices in every country. They managed very well in aligning their processes. Indeed, their whole business model relied on good interchange of quality information. He thought most global organisations would wither and die if there wasn’t good interchange and standardisation of processes. Yes, there will be cultural differences, and in Reuters they encountered these and learned to work with them.

Wikipedia again

David Penfold suggested returning to the question about the quality of information available on Wikipedia; are we asking too much looking for a perfect solution which doesn’t exist? Universities typically talk down Wikipedia, and students are not allowed to quote it as a reference. Is that realistic?

David Haynes pointed out that Wikipedia editing is moderated. A study some years ago compared the accuracy of Wikipedia articles against Encyclopaedia Brittanica online, and Wikipedia was found to be superior. He advises students that Wikipedia is a fantastic resource and they should use it – but not quote it! If Wikipedia gives a reference to a source document [according the Wikipedia ‘no original research’ rules, every assertion should be backed up by a citation], then go to that source and quote that. Wikipedia should be regarded as a secondary source, a good entry point into many subjects. Indeed, David uses it that way in his own research.

Noeleen Schenck hinted at possible double standards. In the analogue world we never relied on Encyclopaedia Brittanica for everything. She thought that some of the discomfiture was about how Wikipedia is authored by tens of thousands of volunteers. We should remember that enthusiastic amateurs helped to expand the boundaries of science; they are not necessarily ignorant or inept.

The panel agreed that Wikipedia should be regarded as one source amongst many. Noeleen compared this to reading several newspapers to get an angle on something in politics. How you assess sources brings us back to the topic of Information Literacy – not, perhaps, the best term for it, but as David Haynes confirmed, critical assessment of information sources is actually being taught (to KIM students, anyway).

Generational attitudes

Graham Robertson noted that Peter Thomson had talked about ‘millennials’ and their attitudes, and Graham wondered what the panel thought about the role the younger generations would play in changing attitudes, cultures and practices around KIM in organisations. Do younger people use and process information in a different way?

David Smith said he has been doing a review, where it was interesting to compare how older and younger people were sharing information within their cohorts. In the case of older members of staff, one could track discussions via email. But the younger staff members appeared to be absent. Why? It turned that they communicated with work colleagues using WhatsApp. Because it was a medium with which they were familiar, it was a quick way for them to ‘spin up’ a conversation. Of course, this poses new challenges for organisations. Discussions and information sharing are absent from the network (and apparently WhatsApp security isn’t up to much).

Noeleen Schenk thought it was a fool’s errand to try to force people to work in a way which they don’t find natural. She doesn’t know what the solution is, but we need to think afresh at important information and knowledge is kept track of – the current crop of tools seem inadequate.

Facing down ‘alternative facts’

Conrad Taylor asked: What is, or could be, the roles and responsibilities of all who work with knowledge and information – including teachers and journalists – in helping people to learn how to weigh evidence, distinguish fact from falsehood and propaganda, both in ‘big media’ and in social media?

David Haynes noted that this was increasingly a focus in meetings of KIM professionals [it was the subject of a panel session at the 2017 ISKO UK conference]. How can people be sure they are receiving unbiased information? Or if, like Steve Dale, we think that there cannot be unbiased information, we will have to be open to a range of information sources, as Noeleen had suggested.

David Penfold noted that in recent partisan political debate on social media, bots had been unleashed as disseminators of propaganda. Conrad noted that Dave Clarke of Synaptica has proposed a taxonomy of different sources of misleading information (see resources at The panel noted that the role being played by paid-for posts on, for example. Facebook and the way Facebook’s personalisation algorithms work are coming under closer strategy.

Conrad regretted that the term ‘Information and Knowledge Professional’ is often used to mean only people who curate information, excluding people whose job it is to create information – as writers, and designers and illustrators too. It is all too common to see data graphics that have been created in support of an editorial line, and which are misleading. (Indeed Steve Dale addressed this at a recent NetIKX meeting.)

Steve Dale remarked that we now have a new weapon to counteract ‘phishing’ attacks where fake online approaches are made in an attempt to defraud us of money, steal our identity, etc. It’s called Re:scam ( and if you forward scammy emails to it, its artificial personalities will engage the scammers in an exchange of emails that will waste their time!

At this point, we ran out of time, but continued discussions over drinks. Face-to-face, naturally!