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.
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. 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.
For more on the Predictive Enterprise, read my full post on Greenplum’s Datastream Blog.