Big Data is the latest technology wave impacting C-Level executives across all areas of business, but amid the hype, there remains confusion about what it all means. The name emphasizes the exponential growth of data volumes worldwide (collectively, 2.5 Exabytes/day in the latest estimate I saw from IDC), but more nuanced definitions of Big Data incorporate the following key tenets: diversification, low latency and ubiquity. In the current developmental-phase of Big Data, CIOs are investing in platforms to “manage” Big Data.
But there is an emerging realization across public and private sectors that there must be more to “Big Data” than just data and platform. CIOs must transform these Big Data platforms and the data they house from cost-centers to data-monetization engines. Forrester likens this very transformation to “refining oil” and Pivotal believes data science is at the heart of the new oil rush.
Big Data emphasizes volume, diversification, low latencies and ubiquity, whereas data science introduces new terms including, predictive modeling, machine learning, parallelized and in-database algorithms, Map Reduce and model operationalization. Don’t worry — I am not going to get bogged down here by the debate on the definition of a data scientist.
Instead, I want to emphasize a more important point regarding this new vernacular: It infers an evolution beyond the traditional rigid output of aggregated data: business intelligence. It is a use-case-driven, iterative and agile exploration of granular data, with the intent to derive insights and operationalize these insights into down-stream applications.
Considering the growing number of use cases relevant to your enterprise, what has changed is that these are no longer confined to legacy data-driven functions such as marketing or finance. Instead, they can and should involve nearly every functional organization in the enterprise. Given the numerous opportunities data science offers for virtually every functional organization in every sector, it is now no longer enough to be a “data-driven enterprise.” Instead, you must build a data science-driven enterprise, a.k.a. the predictive enterprise.