To become a taxi driver in London, you have to pass “The Knowledge” - a series of written and oral examinations that test your knowledge of London and how to get from any A to any B by the shortest and least congested route. Even today, in the age of satellite navigation, The Knowledge is a requirement for the job.
Given that the next great leap forward in the motoring world is expected to be the driverless car, are all taxi drivers, including the legendary London cabbies, in danger of extinction? For many, advances in technology mean that their livelihoods are becoming more vulnerable to automation. Similarly, globalized organizations are transferring work to lower cost parts of the operation. In this environment, is it any surprise that knowledge management is greeted with suspicion?
But is there more to knowledge management than just an effort to transfer hard won knowledge and experience from people’s minds into computer systems and robots? To find out what really motivates the knowledge management enthusiast, we need to start at the bottom of the knowledge hierarchy.
The most basic level involves raw data. Consider the following spreadsheet fragment:
It doesn’t tell us much, does it? The problem here is that the data lack any context. What do these figures mean? Are they related somehow? Similarly, the string of characters “56656C6F7069” doesn’t suggest much to the casual observer.
However, if we move up the hierarchy and consider information, the raw data make sense. Simply add row and column headers and a bit of emphasis and suddenly we are looking at something much more interesting:
By giving context to the data, we now have access to some useful information. As far the string of characters given above, all I have to say is that this is a hexadecimal representation of an ASCII encoded string and a certain type of person will realize that this is what “Velopi” looks like to a computer.
While information helps us answer “who”, “what”, “where” and “when” type questions, we need to move up the ladder to get at the answers to “how” questions. At this level, we are considering knowledge itself and this comes in two flavours: The first is “know that” knowledge. Here we have facts and figures relating to the task in hand – for instance: “I know that, to work on a Morris Minor, I need imperial-sized spanners". In other words, the sort of knowledge that the London cabbies have to amass. The second type of knowledge is “know how” – “I know how to ride a bicycle” – and this is a lot trickier to convert into something a machine can understand. The only way you can learn to ride a bike is to practice (and fall off a lot) because you need to develop a sense of balance.
Efforts to capture know how have been greeted with varying degrees of hostility. In some cases, organizations want to protect themselves in the event of skilled staff members leaving the company or being incapacitated in some way (run over by the proverbial bus). In recent times, collecting know how has been associated with transferring operations to lower cost parts of the organization and has, naturally, been resisted.
However, if we go another step up the knowledge hierarchy, we can start to see a less sinister motivation for this exercise. With knowledge, we know how to do something, but we do not necessarily need to know why. Anyone who uses the roads will clearly see that a sizeable number of drivers have no idea why they do certain things. As an example, visit any busy roundabout and reflect on the variety of indicating strategies used. Clearly there are people who know how to use the indicators, but have no idea why they are doing so.
To take a more business-related example, suppose the board of a trans-national corporation reflects on the end-of-quarter sales figures. Suppose further that the figures from South-East Asia are way in excess of those from any other region. There might be a temptation for the board to find out what sales initiatives are being used in South-East Asia (know that knowledge) and learn how (know how knowledge) these work, before rolling them out worldwide.
But a wiser group of directors will want to know why these initiatives were successful. It might turn out that they appeal specifically to South-East Asians. In other words, what works in Vietnam might not work in Vienna!
This application of both knowledge and understanding leads to the final level of the knowledge hierarchy: wisdom. By knowing how your organization works and by understanding why your work has been successful, you now have the wisdom to make informed decisions about future directions.
So if you are considering a knowledge management exercise for your organization, make sure that you understand why you are doing it – what benefits will such an effort accrue? Also, be aware that this is not a one-shot deal. As your staff do their work, they are increasing their skills and building up new knowledge. If you charter new projects, you will also revise the knowledge base of the organization. New knowledge needs to be fed back into the knowledge base, so that our understanding evolves as our knowledge does. Only then will we have the wisdom to react appropriately to what the situation is now, as opposed to what it was when we did this exercise years ago.
If you are still not sure what the different levels mean, this thought might help: knowledge is knowing that a tomato is a fruit; wisdom is knowing not to put one in a fruit salad!