How CIOs can provide the insight decision making requires

In the late 1990s knowledge management was one of the hottest areas in information technology. The internet was creating a new ‘information age', the knowledge economy was in full swing and HP CEO Lew Platt provided a financial rationale for investments in this area with his famous quote: "If only HP knew what HP knows, we would be three times more productive."

Ten years on and a knowledge-driven leap in productivity has not transpired with businesses spending less as a consequence. In part this may be due to a grossly inflated expectation as to the benefits systematic knowledge management would bring, but in part it may also have sprung from the idea that knowledge was an asset that businesses already owned but were failing to leverage. As a result the starting point was data that the business already had - not the data that the business needed to have - with the knowledge management process aiming to transform that data into information which provided insight to shape the actions that would create value for the business.

While the idea of using existing asset better makes such a flow logical from a financial perspective, it is completely the reverse to what a strategic approach would be. Given the importance of high quality insight to good decision making, a strategic approach to insight collection is merited, arguably essential. Such an approach would both prioritise the insights to be collected and put processes and systems in place for the acquisition, storage and sharing of the component data sets.
Achieving this requires inverting the traditional knowledge management flow of data to information to insight to action to value.

The starting point is the desired value the business wishes to create, both for customers and itself. This identifies the possible actions the business can take. The need to select between different options determines the insights required. These insights will be provided by information built up from raw data. Rather than working from what data the business has, the focus is on identifying of what data the business should collect given how it plans to create value.

Deciding what data needs collection requires clear specification of the richness - or maturity - of insight required in each area. In broad terms there are three maturity levels.

The first level is simply measuring - just monitoring what is happening currently, whether that be existing performance to help a business understand how well it is doing or tracking external variables, perhaps using data supplied by an industry association or market research. The Measure maturity level will help a business understand which areas require attention (e.g. performance is declining or below target) or potential opportunities (e.g. reported growth in a particular segment). In both cases more information is required before deciding how to proceed.

This additional information is furnished at the second maturity level which augments measurement with explanation to enhance understanding. The Understand level helps a business clarify what it should do differently.

The third level incorporates both the breadth and depth of insight needed to support decisions where significant investment is involved and a number of options exist. The Select maturity level enables a business to choose between the different alternatives that it has.

The higher the financial and strategic impact, the greater the insight maturity needed. As the maturity level increases, so do the number of required data sources, not least because the necessary information is increasingly external to the business. And whereas the Measure level of maturity requires just quantitative data, the higher levels require additional qualitative insights - depth and breadth increasing with the transition from Understand to Select.

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