The Metaknowledge Guide offers a concise, practical guide to knowledge management for both newcomers and experienced practitioners.
Part I provides an intuitive interpretation of "knowledge", and how it compares to information and data. This helps you position knowledge management in relation to information management and data management in your organisation.
If we want to have effective knowledge management, we of course have to understand exactly what we’re managing. We intuitively know what knowledge is, but it can be quite difficult to define. It means different things to different people, in a different context. Some will interpret it as books and documentation, while others see it more as competences and skills. It doesn’t help that many IT tools are (incorrectly) marketed as “knowledge management tools”, which only adds to the confusion. This can, paradoxically, make knowledge seem very unknowable.
However, you don’t need a universal definition of knowledge. You only need a good-enough understanding of what knowledge means for your organisation, that you can act on. Something that helps you understand and communicate what knowledge management is about, and why it’s worth investing in. And to understand what knowledge is, we should understand what it is not. For this, we will consider the relation of knowledge, information and data. Though many understand there is a difference – we talk about information overload, but not knowledge overload – they are often discussed in the same context, and used interchangeably. Knowing where one ends and the other begins helps us to scope the activities that deal with each of them.
Knowledge vs Data vs Information
Let’s start with looking at the relation between data and information. These are concepts that are more tangible, so they’re a good place to start. Again, we’re not looking for a universal definition, but to establish an intuitive understanding that applies to our modern organisations.
Data is a list of observations, measurements, or records. It only exists in technology. Today that usually means IT systems, but it could also be paper or other forms. In itself, data usually doesn’t say much until it is combined and put into context, which turns it into information. Usually these are graphs, tables, and other structured formats. In this way, data is the “raw material” of information. Note that the same data can provide different information – and different conclusions – depending on how you interpret it.
Knowledge exists only in the heads of people. It is a complex web of know-what, know-who, know-when, know-where, know-how, and know-why that was built through experience. And experience is the key word here – what we’ve learned by doing. Experience is the feedback to our actions that shapes our assumptions (what we think), which in turn shapes our actions. That’s the cycle of how we learn and build our understanding of the world. When a piece of knowledge is made explicit as text, audio, images, or video, it becomes information.
Note: information is sometimes called "explicit knowledge", while knowledge in the head of people is called "implicit knowledge". This term especially refers to the knowledge we are not aware of, and is reflected in the "four knows".
- That which we know we know.
- That which we know we don't know.
- That which we don't know we know.
- That which we don't know we don't know.
Identifying what we do and don't know, and managing the associated risks, is the base of knowledge risk management.
In the context of our companies, thinking of knowledge as experience is a useful approach. It sets knowledge apart from books and documentation. And it makes it clear that knowledge exists in and is shaped by people. So, no matter if you’re doing knowledge management in industry, pharma, legal, engineering, or other sectors, it all boils down to helping the people in your organisation leverage collective experience to their benefit.
This is not only a huge benefit to their day-to-day work, it’s also a strategic opportunity for company. In today’s global, information-rich world, the only thing that differentiates you is your experience. That’s what sets you apart, and that’s what allows you to deliver real value, and solve real problems. And it can’t be replicated even by your closest competitors, as they will inevitably have experienced different things. Plus, experience is an asset your company is constantly producing regardless of how well things are going financially. As many companies have yet to adopt the strategic aspects of knowledge management to manage their knowledge capital, there is a huge early-mover advantage to be had.
So, at a glance, it would appear that data and knowledge management have a similar purpose: to facilitate and optimize the creation of relevant, actionable information. And that companies should invest in knowledge management for the same reason they invest in data: to create actionable insights.
However! This is not the full picture. Regardless of whether it starts as data or knowledge, information is not the final product – knowledge is. People don’t act on the information they receive, they act on the knowledge they have internalized. Data practices (incl. data management, data governance, data science and business intelligence) aim to ensure data is collected, stored, accurate, and complete, and that this data is translated into useful information. Information management aims to ensure information is stored in the right place, up-to-date, and secure but accessible to the right people. Knowledge management is a collection of practices to facilitate the effective capture, sharing, and reuse of knowledge. And that emphasis on reuse is what sets knowledge management aside from information management and data practices.
While data and information management aim to create high-value information assets, knowledge management aims to create high-value communication. It aims to help people leverage the experience and expertise of others, to learn collectively and work as efficiently and effectively as possible, and prevent avoidable mistakes. While data and information management are technology-oriented - and good information management is a crucial part of good knowledge management - knowledge management begins and ends with people.