Business Analytics: Considering the Spectrum of Analysis
by David Loshin
Originally published July 28, 2011
Over the past few months we have essentially looked at organizational preparedness for introducing a business analytics capability. At some point, with management approval, allocated budget, and engaged business consumers, it is time to start considering how to map the business usersí expectations into a realistic plan for source data selection, evaluation, preparation, and analysis.
A prelude to planning the data selection is considering the different types of analysis that can be performed, and what is expected to be achieved as a result. In our customer engagements, we typically see a transition in the level of maturity in the types of analyses and techniques that correspond to the levels of expected value. As the organization begins to produce actionable knowledge, the consumers become engaged in finding new ways to analyze data for increasingly better outcomes. Another way of describing this looks at a sequence of sophistication of the business questions, such as these:
To reflect back to the sales example, you may find that in many cases a certain sequence of events may lead to closing a sale, but at some point the probability decreases. Knowing where the process breaks down and how to change the process can advise the salesperson in real time of whether to continue to try to close the deal, alternative approaches to assist in closing, or whether to drop the prospect and concentrate on another with a higher probability of closure.
Of course, we are using sales as an example, and we can refer to any of our previously identified success measures to drive the process. But letís reflect on these levels of analysis sophistication: It is much easier to create an application that delivers predefined reports answering existing questions about organizational operations than it is to optimize specific areas of the business. In fact, these levels present a spectrum of analysis, and each progressive level described here builds on the previous levels. In essence, you canít improve what you canít measure, as good olí Lord Kelvin used to say.
This suggests two things to keep in mind. The first is that as designers and implementers, we need to gauge user expectations and help ratchet those expectations to match the level of sophistication of the analytics capability. Your users might have been sold on the idea that instituting an analytics program was going to improve the business, but they have to be aware that the value is evolutionary, not revolutionary (that is, sales donít increase when you crack the shrink wrap).
The second is that this evolution has to be incorporated into the roadmap and the program plan. The deliverables of the projects should be mapped to the expected capabilities; at the same time, the user communities can be trained as to how they can make best use of the analytics capabilities as they are deployed.
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