Time, like the term knowledge, is an elusive concept. Like knowledge, a definition of time that is satisfactory to everyone has defied the greatest minds from Antiphon to Newton, and on to Einstein and Planck. Yet time is pervasive across cultures, and at least in the Western world, drives much of what we do. Time is also intrinsically linked to knowledge management, and provides some insights as to why knowledge management is so difficult.

Take for example the common platitude –“just in time knowledge management”. I take this to mean that the right information (I have chosen my words carefully) should be available to decision-makers at the right time and in the right place, and not before or after the time it is actually required! Now this begs all sorts of questions, like:

• How do we determine what is the right information?
• How do we know who should receive that information?
• What is the right format for the information, so it can actually be used?
• How do we know what the right time is?

I could go on but you should get the idea of just how difficult, if not impossible this is. This brings me to my second idea – knowledge management is about the effective use of data or information at some future point in time. Our knowledge management activities collect data, or information, or even anecdotes, from the past and try to make sense of them so we can do something in the future. We use these collections and apply previous experience, and perhaps some context, to predict an outcome. In other words we expect a better outcome as a result of our efforts.

Again this begs all sorts of questions, like:

• How do we know what to collect?
• How do we know that what we collect will be useful in the future?
• What is the right format for the data and information we collect?
• How do we know what we collect won’t decay with time, or if it does decay with time how do we know when it will no longer be useful?

It seems to me that knowledge is both descriptive and predictive. (I’ve blogged about this idea previously at - The Knowledge Conduit – so I won’t expand on it). On the other hand knowledge management is predictive but uncertain. It is uncertain because ahead of time we cannot know what information we require, and more importantly how or for what purpose that information will be used. We can make some informed guesses (predictions), but we just don’t know. How often are we asked to provide statistics, which were never collected in the first place, or if they were collected are incomplete? How often do we collect data for one purpose only to find it used for something completely different?

So where does this leave us? Well I think we need to consciously consider the time dimension in any knowledge management initiative. We need to recognise information can and does decay with time, and will be used for unforseen purposes. We need to constantly re-evaluate our data repositories, always with an eye to the future. We need to recognise that information is descriptive, but knowledge is both descriptive and predictive. It is the predictive element that is most useful for decision-makers, and predictions reside in the future. Sometimes we get the predictions right and sometimes they are wrong. Knowledge management is about reducing the error rate in our predictions. We do this by ensuring our descriptions are as accurate as possible, and rich in context. We do this by collecting facts, noting that facts can be falsified over time as new data becomes available.

For me the time dimension matters. It isn’t given enough consideration in knowledge management circles. Perhaps the knowledge management system of the future will include the means to travel backwards and forwards in time – just like Dr Who’s TARDIS . Alas at the moment this seems to be an impossible dream because of the Grandfather Paradox , but I live in hope!

Regards, Graham