Working and valorising Big Data is business-as-usual for companies that have built their business model on it. For companies that don’t compete on analytics, that is for whom analytics is not a core element of their strategy, it’s a huge challenge.
But Big Data is the talk of town nowadays. I think that a part of the growing management interest is due to two factors:
In a recent article in SAS magazine, John Knowles from Allianz insurance gives his view on the challenges to effectively embrace Big Data in existing business models:
I especially agree with the first two arguments. Dashboards, metrics, segmentations, propensity scores,.. - no matter how attractive or good - require a deep understanding of the business process in which they are supposed to play a role. Data doesn’t speak for itself. Data doesn’t propose a decision. Data itself doesn’t bring profit.
Instead, considering the right (amount and type of) data for supporting the right decision at the right time is essential to make any investment in a (Big) Data initiative profitable. Said otherwise, relevant Big Data as well as analytical outcomes from it should be fed back into daily business decisions (do I contact this customer, do we increase our sales target in region x,..).
First the data, then the decision, then the profit.