A model for KM technology selection

An example from Schlumberger shows us how selecting KM technology should be done.

image from wikimedia commons

At the KMUK conference a few years ago, Alan Boulter introduced us to the Schlumberger approach to selecting Knowledge Management technology. This is a very straightforward contracts to the common “gadget-store pick and mix” approach, and worth repeating.

Firstly, Schlumberger defined exactly what the business needed from their Knowledge Management technology. They divided these needs into 4 groups;

  • Connecting people to solutions
  • Connecting people to information
  • Connecting people to communities of practice
  • Connecting people to people
Secondly, they bought technology which does each required job, and only that job, and does it well.  If no technology was available that did the job well enough, they built it in-house.
Thirdly, they stuck with that technology over time, provided it still did the job well. People were familiar with it, so they stuck with it.
Finally (and this seems so rare nowadays, that I want to emphasise it), if they bought new technology which had optional functionality that duplicated an existing tool, they disabled that functionality. As an example, they brought in SharePoint as an ECM tool, and SharePoint comes with the “MySite” functionality, which can be used to build a people-finder system. Schlumberger had a people-finder system already, and to introduce a second one would be crazy (if you have two systems, how do you know which one to look in?). So they disabled MySite.
Schlumberger have ended up with a suite of ten tools, each perfect for the job, and with no duplicates. Staff know how to find what they need, and which tool to use. Schlumberger are long-term winners of the MAKE awards, and deliver hundreds of million dollars annually through KM.  Their technology selection forms part of their success.

View Original Source (nickmilton.com) Here.

Do you agree with these two KM assumptions?

A recent paper from the Gartner group seems to contain two basic assumptions about knowledge management which I think are worth addressing. See what you think.

The Gartner paper is entitled Automate Knowledge Management With Data Science to Enable the Learning Organization, and contains the following blocks of text:

“The capture of expertise and experiential knowledge diverts experts and skilled professionals away from productive work. Project managers, software engineers, product developers, hiring managers or customer support agents may be asked to document their work; to participate in peer-to-peer communities to capture and share expertise; to work in more open and transparent ways to encourage serendipitous connections and information flows across an organization; or to shadow peers and learn from observation. But for every minute they do this, it is a minute taken away from doing what they are supposed to be doing”.

“Although still relatively immature, and requiring much manual fine-tuning with domain as well as technical expertise, the “body of knowledge” that powers smart machine movers, sages and doers is extracted automatically by analyzing, classifying, labelling and correlating volumes of structured and unstructured data, including free-form text”.

Now these two chunks of text seem to me to be based on two assumptions. Let’s look at these one by one.

The first assumption is that “knowledge management is not real work”. Note how they say “diverts away from productive work – taken away from what they are supposed be doing”.  So they do not see KM as productive, nor do they see it as something a knowledge worker should be doing.

But KM is productive – it may not produce a tangible object which can be sold to a customer, but it produces knowledge which can be used to improve processes or innovate new products in the future, and so adds value to the business. In some cases, such as R&D, knowledge is the only value. In Pharma, for example, the success rate of R&D projects in delivering a successful product is only 2% or 3%, and in every other case knowledge is the only product. And as the development manager at Toyota said (and I paraphrase); “In Toyota NPD our job is not to produce cars but to produce knowledge, and from that knowledge great cars will emerge”. Producing knowledge is an investment in the future; producing products gives value in the immediate term while producing knowledge gives value in the longer term. KM is productive work.

And maybe KM is something that everyone should be doing, or at least contributing to. Who else should contribute if not the knowledge workers? And you cannot say that the job of the engineer is only engineering, the job of the salesperson is only to sell, or the job of the IT coder is only to write code. All of these people have other things to consider – they need to bear finances in mind, and safety, and quality, to name just three. They can’t say “I don’t want anything to be involved in quality or in safety – just let me do my real job”. The real job needs to be done within a number of contexts, and knowledge management is one of them.  Imagine an airline pilot saying “I am not going to take part in this lessons meeting about my recent near miss, because this is a day taken away from what I am supposed to be doing”. That airline pilot would not keep their job for very long, because the aviation industry knows very well the value of knowledge and of knowledge work, and knows that KM is something pilots need to contribute to.

But if there is pressure to balance the demands on the knowledge worker, between their short-term delivery of product and their long-term delivery of knowledge, can smart machines take up the slack?  The answer to this depends very much as to how much knowledge you think lives in the “volumes of structured and unstructured data, including free-form text”.

Personally I don’t think there is much knowledge in there at all.

The vast majority of structured and unstructured data and documents are work products – the outcome of knowledge work, but not containing the knowledge itself. For example:

  • A CAD drawing may show you a design for a product, but does not help you know understand the process of design, nor how best to design the next product;
  • A bid document tells you how a bid was constructed, but does not contain knowledge of why the bid was won or how to improve bid success;
  • A project plan tells you how a project was planned, but contains nothing about project best practices.

Knowledge is not created through work, it is created through reflection on work, and it is captured not in work products, but in knowledge products such as lessons learned, best practices, guidelines and checklists. If those knowledge products are not created by the knowledge workers, because “that would be a minute taken away from doing what they are supposed to be doing” then the machines will have no knowledge to find.

So I don’t think either assumption is valid. I think KM should be part of the job, and part of the expectation for any knowledge worker, and I do not think the machines will find knowledge where no knowledge exists. I think the machines will help greatly, and will enhance the work of the knowledge workers, but not as a replacement for KM activities. Gartner seem to acknowledge this when they say “This is not in order to replace conventional KM techniques but to augment them where automated techniques may be more effective or economically viable”.
Let’s look at where automation helps, let’s embrace that, and let’s not assume this means people can stop doing KM and can leave it all to the machines,

View Original Source (nickmilton.com) Here.

Will AI replace KM?

My answer is No, for the following reasons.

image from wikipedia

I have been working in Knowledge Management for a long time now, and the history of KM includes examples of one technology after another claiming that it will replace KM or make it obsolete.

Yet KM is still here.

  • In the 1990s, it was Expert Systems that would make KM obsolete
  • Then in the late 90s, it was Groupware that would replace KM
  • Then Enterprise Search would be the saviour of KM
  • In the mid 200s, Social networking became the new trend that would supercede KM (“Social is the new KM”)
  • And of course SharePoint – “all you need for KM”
  • Then came Enterprise 2.0, and Enterprise Social. They would become the new KM
  • In 2015 I met a purveyor of Semantic Search wearing a T-shirt reading “John Snow may not be dead, but knowledge management is”. Made obsolete by his technology, obviously.
  • And now Big Data and AI and Chatbots and IBM Watson are set to “make KM obsolete”.
Yet KM is still here.
All of these technologies have found their place within the KM toolbox over the years, and they have certainly made certain elements of KM work much faster and much more easily, while making little difference to other elements. 
Yet KM as a discipline is still needed.
Enterprise search, for example, makes it far easier to find documented knowledge, but you still need KM to ensure the knowledge is documented in the first place. Enterprise Social Media makes it far easier to set up conversations within a community of practice, but you still need the community of practice in the first place, with its roles, processes, culture, and stores of shared knowledge. Semantic search makes it far easier to retrieve content in context, but content is only half of the content/conversation duo, and retrieval is only half of the supply/demand duo, and technology is only one of the four legs on the KM table, so there is far more to KM than just search.
All of these technologies make KM faster and easier, but none of them replace KM.
Even AI will not replace KM.

AI is a game-changer, for sure. It makes it possible to make new and rapid correlations from within massive datasets, but someone has to create the datasets, and clean them, and then train the AI, and then interpret the correlations and draw knowledge from what they observe (because we all know correlation is not causation). As I posted here, in the context of Big Medical Data at the European Bioinformatics Institute,

Big Data does not become Knowledge because of it’s size – people have to add Knowledge to the data to make sense of it. The huge data resources of the EBI have to be combined with the specialist knowledge of the staff, and the application of the knowledge is the sense-making step

Also AI and Big Data still only work in the realm of documents, information and data, and in the processes of analysing and retrieving; they don’t help with the transfer and creation of knowledge through conversation, or with tacit knowledge. So AI will be a massively powerful tool in the KM toolbox, but it won’t replace the toolbox. We will need the roles and the processes and the governance to interplay with the technology. KM shifts up a gear, but still will be needed.

So call me an old grouch, but to date none of the new technologies touted as “the killer of KM” have made KM obsolete, and history suggests that neither will AI. And neither will the new technology that comes along in 5 years time.  They will simplify, disrupt, and accelerate KM, but not replace it.

To the extent that people need to use knowledge to make decisions and judgments, then Knowledge Management will be augmented by technology, but not replaced.

View Original Source (nickmilton.com) Here.

Knowledge management and technology – but what sort of technology?

When we think of KM and technology, we usually think of IT. But is this the wrong sort of technology to concentrate on?

image from wikimedia commons

Knowledge Management as a discipline was born in the 1980s as a combination of Organisational Learning and the technological revolution sweeping through organisations. In BP, where I worked at the time, Knowledge Management went hand in hand with, and was enabled by, the development of a common operating system, personal desktop computers for all, email and video conferencing. In fact, the KM program was a direct successor to the Video Telecommunications project.

Technology has always been part of KM, right from the beginning, and is still one of the four legs on the KM table.  So why do we so often get the Technology part wrong, and end up going down database rat holes, creating mega-systems people don’t like to use.
I think it is partly because we focus on IT, and not on ICT. 
There is only one letter different between IT and ICT, but its a crucial letter. The additional C stands for Communication.  As wikipedia says; 

Information and communication technology (ICT) is another/extensional term for information technology which stresses the role of unified communications and the integration of telecommunications (telephone lines and wireless signals), computers as well as necessary enterprise software, middleware, storage, and audio-visual systems, which enable users to access, store, transmit, and manipulate information.

It was not so much the availability of information processing power or the ability to store information that sparked the birth of KM, so much as the networking of computer systems and the ability to communicate far more widely. Suddenly, through ICT, we were connected with so many more people, so many more knowledgeable people. 

I clearly remember one day in the mid 90s, working in BP Norway, when someone came to me with a seismic plot from the North Sea with some very strange features on it. Neither of us could work out what these were, but we realised that now we had networked computers, linked to geologists all round the world, we did not have to solve this problem ourselves. We could send out an email to all our colleagues, and tap into a much broader knowledge base.

It was primarily the communication technology that enabled KM, and its worth remembering this as we look at out technology tools.

Let’s focus not so much on IT and more on ICT, because that C makes all the difference.

View Original Source (nickmilton.com) Here.

Why you can’t have AI without KM

The rise of AI in the form of intellegent agents requires the rise of KM to support it. 

Image from wikimedia commons

This is the conclusion of Gartner research, quoted in this Computer Weekly post entitled “IT staff will need to retrain when automation deskills their jobs”.

According to the post – 

Before automation and intelligent agents can really take off in the enterprise, IT operations teams will need to build knowledge management systems. 

Gartner said knowledge management is essential for a chatbot or virtual support agent (VSA) to provide answers to business consumers, but the response can only repeat scripted answers when based on existing data from a static knowledge base. It warned that intelligent agents without access to this rich source of knowledge cannot provide intelligent responses. 

As such, Gartner suggested that infrastructure and operations managers will need to establish or improve knowledge management initiatives. Gartner predicted that, by 2020, 99% of AI initiatives in IT service management will fail because of the lack of an established knowledge management foundation.

That’s quite a prediction, but really it makes sense. AI in the form of intelligent agents like IBMs Watson is really a delivery vehicle for knowledge, allowing contextual answers to be provided quickly and effectively, and it requires a robust source of knowledge in order to work. Without KM, AI will fail. 

View Original Source (nickmilton.com) Here.

The NATO lesson learned portal

The video below is a neat introduction to the concept behind the new Lesson Learned Portal at NATO

The video is publically available on the Youtube channel of JALLC, the joint Analysis and Lessons Learned Centre at NATO

The Youtube description is as follows:

The NATO Lessons Learned Portal is the Alliance’s centralized hub for all things related to Lessons learned. It is managed and maintained by the JALLC, acting as NATO’s leading agent for Lessons Learned. 

Observations and Best Practices that may lead to Lessons to be Learned can be submitted to the Portal, and the JALC will ensure that these Observations find their way through the NATO Lessons Learned Process. 

The information shared on the NATO Lessons Learned Portal can help saving lives. The little piece of information you have, may be the fragment missing to understand the bigger problem/solution – make sure you share it.

View Original Source (nickmilton.com) Here.

The role of the Knowledge Manager in an AI world

How will the development of Artificial Intelligence affect the role of the Knowledge Manager?

There  is a lot of discussion on Artificial Intelligence as part of Knowledge Management, and the use of powerful computing to replace the reliance on experts. As discussed here, the expert, in a rule-based scenario, is seldom better than a smart computer, and the computers are closing the gap that remains. Is there still a role for KM and the Knowledge manager as the computers get smarter?

Take the vision below, from an IBM Watson TV commercial.

Here the company expert, Jack, is on holiday and is replaced by Watson, who can give advice just as good as Jack’s, and in some cases, in Jack’s own words.  Engineers using Watson can “access 30 years of experience in seconds” according to the commercial, which is exactly what we are seeking for in KM. Knowledge which used to live only in the expert engineer’s head is now available to all at the point of need. Knowledge, through the application of AI, has become common property, easily accessed.

The benefits of this use of AI are considerable (please note that I am not, in this post, addressing the use of AI to search for correlations and patterns in big datasets; I am looking more at the retrieval of actionable advice).

What AI is doing here is automating the supply chain for knowledge, and removing the bottleneck which the expert previous represented.  It represents some of the automated augmentation of knowledge work that will help increase the productivity of the knowledge worker, and help up meet Drucker‘s “50-fold productivity increase” challenge.

As a result, the knowledge workers get quicker access to better knowledge, the organisation is protected against the loss of experts and the risk of problems onsite, and more can be done with fewer people.

It is probably inevitable that the number of knowledge workers will decrease as this sort of AI-related augmentation is used more and more.  Think for example of the reduction in support-centre staff as the use of AI chatbots and technology such as Watson is used to answer customer queries, rather relying on human staff drawing on a Knowledge Base platform.

But what about the knowledge managers? Will they still have a job in the new world?

Yes, they definitely will.

AI like Watson in the video above interrogates structured and unstructured information, and rapidly retrieves an answer to a question, providingthat answer in the context of the enquirer. But someone has to make sure the knowledge is in the system already, and much of it is not.  Even if it is in the system, the AI needs the language to be able to understand the question and to retrieve possible answers, and to have algorithms tuned well enough to judge the right answer. So the Knowledge Manager may have some or all of the following jobs to do:

  • Capture the tacit knowledge in the first place. In many organisations, much or most of the crucial knowledge is still in people’s heads, and therefore completely inaccessible to AI. Some of it may never be captured. In the video above, how do you think Jack’s knowledge became accessible to Watson? Not through trawling Jack’s emails, but through a well-planned knowledge elicitation exercise, involving the skills of knowledge management.
  • Add the events which have not yet happened. This is another class of “knowledge not yet captured”. In the example above, the records will be full of data and information related to the normal running of the plant, but will rarely have much on operations “outside the envelope” – when something has gone wrong. I remember a knowledge capture session I did once with a senior engineer, and he told me his favourite way to train new guys was to give them out-of-the-box examples to work with. “Imagine pump 3 has stopped, pipe 7 is running at 500 degrees, and there’s smoke coming from the turbines. What do you do? Watson would not know what to do unless the knowledge manager puts examples like this into the dataset.
  • Clean the knowledge base. The biggest problem with AI is poor data, and AI starts with clean data. AI retriving knowledge starts with clean knowledge, and thats a job for the knowledge manager, as most knowledge bases I have seen are decidely unclean. For example a Watson-like AI acting as a chatbot answering customer queries needs a clean, reliable and constantly updated knowledge base just as much as contact centre agents do, and the knowledge manager makes sure that the knowledge base is managed well.
  • Build the ontology. Semantic search such as Watson’s relies on a really good ontology, so Watson can make sense of the question, and can identify classes of answers from the existing documentation. Who will write the ontology? The knowledge manager will, with the help of the experts.
  • Oversee the training. AIs need to be trained, and this can be a big job. As this KM World article points out, “it is reported that it took a core team of 20 researchers to build Watson’s Jeopardy-beating machine (along with a strong support team to aid in those efforts). Likewise, the AlphaGo team spent 18 months researching the very complex game of Go (with 20 core researchers publishing their paper in Nature)”.
  • Tune the algorithm. Not every AI has got the algorithm right, and a wrong algorithm can be a disaster. Microsoft shut down a bot called Tay after pranksters pushed it to make racist, sexist and pornographic remarks, for example.  At the time of writing, AI needs a lot of guidance before it can work on its own.
  • Continually improve the knowledge supply chain. New knowledge comes in all the time. This needs to be added to the knowledge base. The performance of the company needs to be tracked, and you need to look at the lessons. Sometimes the AI needs tweaking. All of these things are jobs for the knowledge manager. 

There are also circumstances where AI doesn’t yet work well.

“Machine learning works best in an environment with rules and huge numbers of data points. That might work with cars driving through heavy traffic governed by laws, or with achieving the best price for selling a big block of shares. It might not work well in deciding where to invest a hedge fund’s money, for example, or recommending products to customers without much previous data to go on. The minute things get fuzzy—either due to a lack of rules, an unclear evaluation of success or a lack of data—artificial intelligence performs poorly”.

In a fuzzy and complex world, you are out of the realm where experts, expert systems and AI function well. Here you need the knowledge networks and the communities of practice, who can draw on their collective tacit experience. And helping build and sustain these networks is part of the role of the knowledge manager. AI can do nothing with tacit knowledge.

The number of knowledge managers in the AI world may well increase, not decrease.

Knowledge managers, and allied disciplines such as content managers and data scientists, will quite possibly be in greater demand if the use of AI increases as some commentators predict. For example, according to the bloomberg article quoted above 

“These limitations (of AI) mean it’s not yet clear that the cost of automation will be offset by savings in human capital. Hiring a data scientist can cost more than $200,000, according to Bloomberg News. Flight-bookings company Amadeus has 40 of them. Siemens says it has more than 200 A.I. specialists running various projects. And even Silicon Valley has its grunt workers: Facebook is hiring 3,000 content moderators, on top of 4,500 existing ones. A.I. cheerleader Amazon has 341,000 employees—three times the number it had in 2012”.

AI does not mean that knowledge management is dead, and the knowledge manager is out of a job. It adds a new and powerful technology to the knowledge managers arsenal, and changes the nature of some of the knowledge manager’s tasks and adds new ones, while other tasks remain just as they were.

It looks like AI is not going to replace the knowledge managers, content managers and data scientists, at least not in the short term!

View Original Source (nickmilton.com) Here.

Can you deliver Knowledge Management with no technology?

There are a few cases – very few – where you can build a Knowledge Management progam without the help of technology.

  Low-tech Twitter
We often talk about a Knowledge Management Framework as a mix of Roles and Accountabilities, Processes, Technologies, and Governance – the four legs on the Knowledge Management table.

But can you get along with any of these missing?

Can you do Knowledge Management with no technology, for example?

The purpose of KM technology is to allow people to talk when they can’t get into the same room at the same time, and to allow knowledge to be stored and developed beyond the constraints of human memory.

So it would be possible to have a technology-free application of KM for:

  • a small group, who were all co-located and could talk at any time;
  • meeting frequently to discuss knowledge, and keeping that knowledge tacit and undocumented;
  • debriefing quickly while memories are fresh, ensuring knowledge did not need to be stored for a long time;
  • dealing with rapidly-changing knowledge which would be out of date by the time it is documented;
  • or knowledge they need to internalise quickly, which they do through conversation rather than reading;
  • which they will re-use quickly, before they forget the details. 
This is rather like Action Learning, and you could argue either that Action Learning is a technology-free application of Knowledge Management where the knowledge remains only in tacit form, or that Knowledge Management is a technology-enabled extension of Action Learning using technology to spread the learning process through space and time across multiple teams and locations.

I know of one example like this – where a project was in the middle of intensive negotiations with a hot government, and conducted Knowledge management through a series of After Action reviews in the team “war room” with everyone present. The knowledge could then be internalised and taken into the next negotiation without the need for documentation. However even then, the team used the war-room whiteboard as a form of technology to build up a map of the negotiation process and the negotiating stances of the governmental departments.

So it is possible for a small team to use something like Action Learning as a technology-free form of KM. However as soon as the conversation needs to reach beyond the immediate team, or where knowledge has to be stored for more than a few days, technology needs to play its part.  “Play it’s part” is the key point – technology is one of the four enablers of KM, and cannot deliver KM on its own. What it can do is extend the reach of learning discussions, and provide a place where the content of those discussions can be stored and developed over time. 

While there are a few cases where no technology is needed for KM, in the vast majority of cases Technology is one of the four main enablers, forms one of the four legs on your KM table, and will require one quarter of your focus during KM implementation.

View Original Source (nickmilton.com) Here.

Which Knowledge Management technologies add most value?

Interesting results are coming through from the Knoco 2017 Knowledge Management survey, including this plot of comparative KM technology value.

We asked the survey participants to rate these different types of technology by the value they have added to their KM program, including in the question the option to choose “we do not use this technology” or “it’s too early to tell”.

The chart above shows these technologies in order of value from left to right, as a stacked area chart, with the weighted value shown as a blue line (this line would be at 100% if all the participants that used this technology claimed it had “high value” and at 0 they all claimed it had no value).

The top of the grey area represents the usage percentage for these technologies, as the light grey area above represents people who do not use this technology. The top of the green area represents the percentage of people who said this technology had added “large value”.

288 people answered this question.

The technology types are listed below in order of usage, and in order of value.

Technology type in order of usage 
(most common at the top)
Technology type in order of value delivered  when used (most valuable at the top)
Best practice repository
Document collaboration
eLearning
People and expertise search
Enterprise search
Enterprise content management
Portals (non-wiki)
Video publication
Question and answer forums
Blogs
Lessons Management
Microblogs
Brainstorming/ideation/crowdsourcing
Wikis
Social media other than microblogs
Expert systems
Data mining
Innovation funnel
Semantic search
Enterprise search
Best practice repository
Document collaboration
Enterprise content management
eLearning
Portals (non-wiki)
People and expertise search
Question and answer forums
Lessons Management
Expert systems
Brainstorming/ideation/crowdsourcing
Microblogs
Video publication
Social media other than microblogs
Wikis
Semantic search
Data mining
Innovation funnel
Blogs

Comparison of usage and value

There is a strong correlation between usage and value. This could represent a tendency for the more valuable technologies to get the greatest use. This is a perfectly valid interpretation.  An alternative argument would be to say that technologies deliver more value the more they are used. Technologies at the top of the list are mainstream technologies, used frequently, and delivering high value. Technologies at the bottom of the list are less mainstream, and deliver less value to the companies that use them, because those companies make less use of these technologies. This is also a plausible interpretation.

Even with this interpretation, we could still look for “Good performing” technologies which deliver more value than their popularity would imply, and “Poor performing technologies” which deliver less value than their popularity would imply.

Under this interpretation, the best performing technologies are Enterprise Search and Expert Systems (both of them 6 places higher in the Value list than the Usage list) and the worst performing technologies in terms of value per use are Blogs.

This does not necessarily mean Blogs are a bad technology; it probably means they are not being used in ways that add KM value.

Changes since the 2014 survey

We saw very similar results in the 2014 survey, again with Blogs being the poorest performing technology given their usage figures, and again with the best performing technologies in terms of value vs use being Enterprise Search and Expert Systems.

Those technologies which have most increased in use between 2014 and 2017 are Microblogs and video publication, and not surprisingly these have also seen the greatest increase in value delivery as well. The technology which has decreased in use the most over the last 3 years is the innovation funnel technology.

View Original Source Here.

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