In the interest of full disclosure, this post is not about algebra or calculus, nor is it about financial instruments. It’s about various kinds of business research ‘raw materials’ and how to discern their quality if you are a producer or user of such research.
Whether you are navigating the waters off Tuscany in a cruise ship, or analyzing a new business opportunity, the quality of the data you use is integral to the outcome you will deliver. While the availability and use of high-quality data does not guarantee a desired outcome, the absence of it renders the desired outcome more a result of chance than of strategic intention.
Manufacturing re-thought quality
When I originally developed the KVC model, it was partly a test to see how well best practices in manufacturing could be applied to organizational intelligence. In so doing I borrowed heavily from earlier work I had done in TQM (Total Quality Management). One of TQM’s most basic principles is that in any manufacturing process, it is no longer acceptable to just get to the end of the process — the finished goods — and weed out the ones that do not meet quality standards. Instead, it is much more efficient to build quality checks into each stage of the process, reducing the number of adverse quality-related surprises at the end of the process.
Intelligence can, too
So it is with intelligence — the ‘manufacture’ of a knowledge-based product. One of the implications is that each step up the value chain tends to replicate (and in my experience, amplify) any quality shortfalls embedded in earlier stages. ‘Garbage in, garbage out’, as the expression goes — though we prefer its converse, ‘Quality in, quality out.’
Another implication is that wherever possible, in developing your source data, you go to the lowest possible level on the chain at which it’s available. If information and intelligence are the branches and leaves of the ‘knowledge tree’, then data points are the roots. You want to get to the ‘root-most’ level in order to start your analysis.
To the extent you accept at face value someone else’s processing or analysis of the data, you may save yourself some analytic time and effort — but also you leave yourself open to inheriting and building on their errors.