Big Data can help in mapping and understanding customer behaviors, and in developing one-to-one marketing programs or innovative services. However, Big Data is too often presented as a technological capability subsequently requiring armies of data scientists to mine and analyse data.
Yes, managing and exploiting the growing amount of internal and external data is a necessary condition to steer business performance. But it is far from a sufficient condition.
In a recent meeting around a business case for a new investment, one manager formulated as it follows “we don’t need to ask [the analytical department] more numbers, we need to figure out first what we want to measure”. Data science should support decision making. For this to work properly, I think a good decision process could look as follows:
(1) Ask the right questions – what do we want to learn from data, i.e. how much uncertainty do we want reduced ?
(2) Provide a visual framework – one that describes the bigger picture as well as the connections between the questions – a value chain, a process diagram, a sales funnel,.. This can keep a group or audience in sync with respect to the scope of what we want to measure.
(3) Analyse data – should be about the data as much as about the analysis. I mean linking results back to the business problem and communicating/presenting conclusions is crucial.
(4) Refine progressively – questions provoque more questions. This is OK, as long if we don’t deviate from the goal, exceed timings or boile the ocean.
(5) Decide – and don't forget to communicate and followup underlying assumptions or uncertainties.
A book that treats the role of measurements in taking business decisions very well is Douglas Hubbard’s “How to measure Anything – Finding the Value of Intangibles in Business”.