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Information Architecture and KM

May 5, 2016

When developing knowledge management systems (KMS) there exist the need to deliver the right knowledge to the right people at the right time and in the right context. Easier said than done… Right? Well, if you incorporate a sound information architecture (IA) in your design and implementation of your KMS, this will be exactly what you need to correctly facilitate the flow of knowledge.

IA connects people to their content (information & knowledge) that includes the high level rules that govern the manner in which information concepts are defined, related, realized and managed by the enterprise. The major IA components are the Content Model, Metadata Schema and Taxonomy.

A Content Model provides the framework for organizing your content so that it can be delivered and reused in a variety of innovative ways.

A Taxonomy is a hierarchical classification or framework for information retrieval. Taxonomies represent an agreed vocabulary of topics arranged around a particular theme. A taxonomy can have either a hierarchical or non-hierarchical structure. However, typically taxonomies are presented in a hierarchical fashion

Metadata is an important aspect of the IA and in particular the Content Model. Metadata is primarily used for labeling, tagging or cataloging information or structuring descriptive records. Metadata (fields and attributes) are assigned to a content type to provide a means to describe it and provide the means in which to find content once it becomes part of a KMS.

The marriage between IA and KM is really a one sided affair. A KMS needs IA to be effective, but IA is not dependent on KM. When it comes to effectively labeling, structuring and categorizing explicit knowledge types within an KMS having the right information architecture is essential. The purpose of IA for a KMS is to ensure that the right people have access to the right knowledge at the right time… and in the right context.

The knowledge we are speaking about can be either explicit and/or tacit. Explicit knowledge is formalized, codified and easily expressed in words and symbols. This is often called the know-what. Explicit knowledge can take the form of, but not limited to lessons learned, knowledge articles, FAQs, Standard Operating Procedures (SOPs), Job-Aids, and Best Practices. Explicit knowledge can be represented by any tangible asset that conveys how to do something or describes how a decision was made about something. Tacit knowledge is knowledge found within the minds of the practitioner. This can consist of intuitive know-how, experience, learnings, and practices. This knowledge is difficult to capture and is usually passed on to others through mentoring, storytelling, and other socialization methods.

A Knowledge Management System usually involves just explicit knowledge. IA does a great job in providing the infrastructure in order to capture, catalog, store, retrieve, and find explicit knowledge. However, IA plays a significant role in bringing in tacit knowledge sources within the KMS. This is done primarily through the use of expertise locators and social communities. Expertise Locators leverage IA to provide the infrastructure (metadata schema) to capture attributes about the experts with the organization and associate the expert to social communities and explicit knowledge sources within the KMS. The attributes to describe the experts can include such things as areas of expertise, educational background, projects worked on, years of experience, and research material published. Essentially anything that will help describe the expert in order for a user of the KMS to understand what the expert may know and who can contribute to making a decision or has the ability to solve a problem.

When it comes to knowledge management, IA is essential to the adoption and success of any knowledge management system implementation.

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Digital Transformation & Productivity - Part II

April 20, 2016

Looking at a few keynotes from one of our internal, semi-annual technical conferences, and reflecting on some of the many Microsoft and Customer stories I heard on my recent travels in Europe, it doesn’t take a lot to see how they are all converging around one thing: Digital Transformation.

There are probably as many definitions of (and opinions on) the term “Digital Transformation”, as there are leaders, influencers, bloggers…. out there. And as many definitions as there are, as many are the business problems we are trying to solve with it, and as many (multiple times many more actually) solutions are there out there. So that is exactly where it should start: with asking ourselves not only “WHAT” we need to do but even more importantly “WHY” we need to do it? Are we really solving the right problems? Are we asking the right questions? Are you? Have you really isolated the problem you are trying to solve or are you throwing a solution/process/tool/consulting team at it, to fix the symptoms? The low hanging fruits? Or have you actually asked yourself “why”, as well as asking the “what”/how/who/when/where…?

Over the last few weeks I have had a ton of conversations with Customers and Microsoft teams (who work side by side with our customers every day) and everyone is working on some kind of “Digital Transformation” project or initiative – whether it is an internal project to implement our own New World of Work – concept at our offices in Stockholm or Prague, or sitting down with a Customer who is struggling to make users adopt Skype for Business, when all their work instructions are based on phone books and phone extensions. It is all “Digital Transformation” of some sort.

I like the idea of this transformation as the Fourth Industrial Revolution as e.g. referenced by the World Economic Forum, as the theme for their 2016 Annual Meeting in Davos, Switzerland. I think it puts it in a good perspective and I think, in many cases and for most of us, we have to be prepared to support anything from Industrial Revolution 3.5 (or at the very least 3.75) to 4.25, over the next 3-5 years.

How can we do that effectively, all the while we continue to perform, in a world that transforms so fast around us, on every level, that we can barely find the share button on our screen from one day to the next? We need to Work Smarter! Yup, I know, it is a total cliché and I know more than one of my former colleagues who has smoke coming out his ears reading those words: “WORK SMARTER”.

There is no way that one person can support every type of customer problem, at any point of scale of “Digital Transformation”/Industrial Revolution 3.0/4.0 on his or her own, so the only way to keep performing is by leveraging the Collective Knowledge of our Global Organisation(s)! 

 

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How Will KM Certification Benefit My Career?

April 6, 2016

Hear from KMI students, interviewed after their certification courses in London and Washington, DC.

Click here to view video.

 

 

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Is KM a Science? The Verdict

February 10, 2016

We recently featured a two-part article by Lesley Crane, considering the question of whether knowledge management is a science.  (Part I, Part II)  Alongside the article, we included an open survey asking readers what they think.  This caused quite a stir, and we had 186 responses in total. So, what did the community think on this question? 

Lesley’s Analysis

The headline news is that a clear majority – nearly 57% - consider KM to be a science, more than the naysayers (24%) and those not sure (19%) combined. Even more telling, of the 184 participants who shared their view on how such an accepted scientific status might impact on the practice of KM, a further clear majority (78%) thought that this would be positive. Impacts included increased support from top management (just over half), greater access to funding (more than a third), more credibility (57%), improved understanding and support from the workforce (just under half), and access to better facilities and resources (again, more than a third). That is pretty convincing, and paints a positive and beneficial perspective of scientific practices. But also, in contrast, suggests a professional discipline that is not getting the support it deserves or needs.

So, who were these participants? Almost half of them claimed to have the term ‘knowledge’ in their job title, with over 40% working at Director or Senior Manager level, and the rest at Manager or Team Leader level. Interestingly two-thirds of all 178 respondents to the question of how they got into KM came through formal study / qualification or had received work-based training. The picture that emerges is of a professional practice operating at senior or middle management level, most of who could be very nearly described as vocationally motivated. Moreover, there is the strong suggestion that KM could and would be so much more – if it had the right level of support from management and workforce, for instance. A scientific status might just help to accomplish this.

Survey participants were also given the opportunity to leave a comment, with almost half doing so – quite a large proportion. It is to these comments that I particularly turned my attention and analysis. First, I categorised them into ‘unambiguously positive’ (49%), ‘unambiguously negative’ (26%) or just plain ‘ambiguous’ (25%). Then I looked to see what primary themes were invoked in support of whichever cause the commentators pinned their colors to. The case of those who dispute the scientific status of KM is interesting.

First, those arguing against the scientific status of KM propose that, while KM might well draw on multiple science fields, this does not make it a science in its own right. Now, decades ago, many scientists might have agreed with that: multi-disciplinary was, to the purists, a dirty word, and not to be trusted. So, one observation that can be drawn is that those arguing for the non-scientific status of KM hold a rather traditional – even old-fashioned - view of science. This perspective plays out in the deeper analysis of the commentary: for instance, one commentator expresses the notion that KM may be built on the knowledge sciences with those principles applied in practice, but this does not qualify the practice of KM as scientific. I disagree: it qualifies KM as an applied science (see the original discussion - Part I, Part II). Another perspective suggests that because KM deals with human behaviour, this disqualifies it as a science. I know a whole lot of psychologists who would disagree with that one!

Cherry picking from some of the other negative comments, we find that KM is not a science because: it deals with qualitative not quantitative data.  (Ahem! Quite a substantial part of social sciences, for example, deal with qualitative stuff – my own work included); that behavioural sciences are not proper sciences (that sort of talk would cause a lot of behavioural scientists to pull on their fighting gloves); and KM has no body of knowledge or theory to call its own so it can’t be a science. On the latter point, I would point to the tens of thousands of academic publications in dozens of professional journals devoted to the discipline of KM, and its multitude of theory – so much that I have argued elsewhere that there is simply too much. Other commentators suggest that KM is no more than a theory (sic!), best practice, a methodology, or even just a culture. In contrast, I would suggest that not only is KM all of those things, but it is precisely these attributes amongst others which qualify KM as a science in its own right.

On the other side of the debate, those commentators who support KM as a science can be broadly grouped into three main themes:

  • first, that KM is an art and a science; 
  • second, that it is a complex social science or that it draws on various sciences; 
  • third, that it is KM’s practices and methods which make it a science (e.g., harnessing and synthesising knowledge from diverse sources, measuring performance to inform knowledge of process, the study of structures and behaviours)

In other words, exactly the opposite to the arguments made by the naysayers. What I find revelatory about this is the richness of description of a professional practice which largely puts humans front and center, and which is dedicated to designing, mediating, customising and nurturing environments with the sole purpose of ensuring the best engagement of people and the highest productivity. 

Good analysts should always attend to what is not there, as much as what is. At this point the lightning bolt hit! Bang! A very nearly complete absence of “technology” in any of the comments! To understand why this is so astounding, know that the debate over the role – and culpability in failure – of technology in the context of KM initiatives has been a furious one in the academic literatures for decades.  It has also long been mythologised that the key skill of the knowledge manager is a native fluency in Sharepoint! Not according to the participants in this survey. Or if it is, it is not worth talking about.

I could draw many competing points of conclusion here. But, I think the most important one is of an emerging renaissance in the field of KM as both a field and practice deeply rooted in scientific endeavour, and which is no longer hall-marked by an insistence for technology as its defining characteristic. That, I would argue, is in no small part due to the increase in training and education within the practice itself.

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Learning from Dirt Bikes

January 28, 2016

The ability to learn and repurpose knowledge from a specific circumstance is a key to ingredient to innovation.  There are a number of ways that KM practitioners can leverage knowledge and learning.  Some of the techniques most used by KM practitioners include: (1) knowledge capture, (2) knowledge leveraging, (3) knowledge creation, (4) Lessons learned and (5) Best practices.

Learning always begins with a question, although finding the right question can be very difficult.  As Einstein said:  “If I had an hour to solve a problem and my life depended on it, I would use the first 55 minutes determining the proper question to ask, for once I know the proper question, I could solve the problem in less than five minutes.” 

Try asking these powerful questions to enhance learning and capture broadly applicable meta-knowledge:

  • Can we derive or abstract a higher lesson from this?
  • Can our work be captured visually?
  • What are we doing here at a higher level – can we capture higher principles at work?
  • What business are we really in?

The history of the Honda dirt bike shows how corporate learning, combined with a flexible or “emergent” business development strategy can lead to dramatic innovations.  As the story goes, when Honda first entered the US motorcycle market after WWII, the company planned to compete head-to-head with the iconic Harley Davidson.  With a shoe string budget and no track-record, the small team of Japanese sent to preside over the Honda motorcycle introduction soon found their product failing.

Strapped for cash, the team began to ride around on the smaller-sized motorcycles that had also taken to the US for personal transportation.  One of the Honda team members took to riding in the hills of California on weekends and noticed that many of the locals were admired his rugged, small Honda motorcycle.

The spark of innovation happened when Honda’s team was able to abstract from these learning experience that there might be an emerging American market for off-road motorcycles. The failing Honda team decided to take action on the new knowledge and try selling the small bikes. Rather than selling through the usual outlets – motorcycle dealers, they chose instead to sell the bikes through sporting goods stores. Honda’s off-road motorcycles quickly became a best seller and the rest is history.

The audacious Honda motorcycle team’s ability to abstract the Meta-Knowledge of a potential market from a few comments and observations is the essence of corporate learning. Their ability to flexibly to redefine the business to incorporate the new learning was ultimately a key ingredient to Honda’s innovative and successful entry into the US motorcycle market.

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