Having the information at your disposal becomes a lot more valuable when you can use contextual information from other related content to analyse exactly what you have. It's just like Simon was saying about Pandora - real value comes from analysing multiple classification axes - not just one. As always, somewhere there's an academic at work on the problem - the article on Faceted Classification has some good starting points...
It reminded me of an article that I wrote for KMWorld, a publication that deals extensively with knowledge management, about robots, and how you need to empower decision makers with information.Funnily enough, when I tried to find my paper, there were a bunch of third party vendors who were charging money for me to read it. So I thought that I'd just post it here for free:
What a Robot Really wants…Knowledge at the point of decision
By Gordon Taylor, TOWER Software
A small, two legged robot stands atop a glass topped coffee table. On its two dimensional world, it has to contend with a pot plant, an old TV guide, and several coffee cups, along with the ever present danger of plummeting over the side towards the carpet below. As the robot navigates its way around, a constant evaluation process occurs inside its software ‘brain’. First, information is collected through its sensors. Secondly, that information is analyzed, using a decision tree to determine the optimal course of action.
What do the adventures of this robot have to do with knowledge management? It’s more important than you might think. You see, right before the robot takes its next step — once this simple two stage process is completed, the robot could be said to ‘know’ something. It’s collected all available information, and analyzed it. Knowledge is created through analysis of information.
So the effectiveness of our robot friend — or if you like, how ‘smart’ it is, depends directly on two things — the accuracy and relevance of the information supplied and the effectiveness of the evaluation process. Poor information, through faulty sensors or too few sensors, will result in an inaccurate picture being fed to the decision making processes. Poor analysis will lead to bad decisions, regardless of the quality of information supplied.
Now I’m sure you saw this analogy coming, but face it — your enterprise is exactly the same. To create a smart enterprise, you need to have a stable, reliable information base, and the analysis tools that allow you to create valuable knowledge — knowledge that fosters good decisions.
Information management has been refined over the years, to the point where most enterprise architects are including a central structured repository as part of their information architecture. ECM systems, built on solid data storage solutions, are the platforms that facilitate these sound information management policies.
At the heart of these information systems, is metadata — data stored about the data you store. By monitoring, storing, and indexing specific information about your business content, ECM vendors allow their customers the ability to easily find any piece of information, and its relevant business context, quickly and efficiently. These systems are built on information management policy and principles that have been around for a long time.
So, if your organization has a sound ECM policy and system in place, it’s not likely to fall off the coffee table because of poor quality information. The next generation of Enterprise systems will focus on how to manage the analysis of that information base to support your decision making process.
The DIKW Model is an information hierarchy that’s frequently cited when trying to address this problem. The model was originally recorded in a 1932 poem called The Rock from TS Eliot:
Where is the life we have lost in living?
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?
In the modern, slightly less poignant implementation of the DIKW model, we find four layers:
Nowadays, thanks to advances in data storage, the science of information management, and the implementation of these systems in ECM products, the transition from data to information is largely a solved problem.
Getting from information to knowledge is much more difficult. Knowledge includes the ‘how’ aspect of a problem. Returning to our robot, it’s the analysis of the information that tells it ‘how’ to proceed.
Current efforts at solving this problem are varied, and you’ll probably recognize them as the more modern features provided by ECM vendors. Collaboration — allowing people to discuss and share information in order to facilitate progress. Workflow — prescriptive, best practice knowledge defined by a business process analyst is another attempt to provide ‘how’ information. Content Management tools, like Blogs and Wikis all provide additional published content around a topic — more published analysis to help people decide which step to take next.
Tools like these are striving to bridge the conceptual void between information and knowledge. While the jury is still out on how effective they are, the challenge is considerable. The next time you need to evaluate a system for inclusion in your Enterprise Architecture, consider how well it bridges this gap. Think like a robot. Do I have the right information available? Will this system enable me to make better decisions? Without a careful approach to both aspects, you could end up on the carpet.