After spending what are often unprecedented amounts of time and money to build a world-class business intelligence and information delivery system, one of the hardest things to do is to get people to ignore the haystack of business intelligence they are able to create in order to focus on the needle of actionable intelligence that they need to drive better decisions, improved effectiveness and enhanced efficiency.
Given advances in data warehouse technology, business intelligence query engines and user- friendly interfaces, humans can now get answers to questions faster than the questions themselves could be asked just a few years ago. The ease with which high-quality business intelligence can now be delivered to the desktop and handtop can lead to information and BI overload.
Later in this publication we will explore the concept of Data Quality including a detailed discussion around the characteristics that differentiate high-quality data from low-quality data. It is important, at this time, however that I introduce one of those characteristics – that of Relevance.
As you will read later in this publication, data is relevant if it has bearing upon or is related to the question, issue, concept, operation or strategy at hand. Relevant data is pertinent and has a material relationship to a topic of discussion, decision to be made or strategy to be defined and executed. While one of the three fundamental characteristics that drive data quality, Relevance is the single measure that differentiates business intelligence from actionable intelligence.
Having been introduced earlier to the Data Maturity Lifecycle, we now know that Business Intelligence is the technologically based understanding that drives strategy. Consider then that you are attempting to define and implement a strategy to improve market penetration and net sales results in the 28 – 36 year old demographic in the Northeast section of your sales territory. While high-quality business intelligence around the relationship between the company’s increased investment in continuing education investment and reduced level of employee turnover is certainly important intelligence, it is hardly relevant to the strategy we are trying to define, the future state we are trying to create and the results we are trying to achieve.
Given this, let us now add one more metaphysical state to the Data Maturity Lifecycle; that of Actionable Intelligence. Let us then define Actionable Intelligence as Business Intelligence that has a high degree of relevance to the strategy we are trying to define and execute.
Finally, let’s agree that while Business Intelligence will be used to measure the results of a strategy, it is actionable intelligence that will be best used to help us define the strategy itself.