Database Reference
In-Depth Information
2
Introducing Data Science
to the Enterprise
“[Data Science] infers an evolution beyond the traditional rigid output of aggregated
data: business intelligence. It is a use-case-driven, iterative, and agile exploration of gran-
ular data, with the intent to derive insights and operationalize these insights into down-
stream applications.”
- Annika Jimenez,
Greenplum
“There go my people. I must run and follow them. For I am their leader.”
- Mohandas Gandhi
Introducing Data Science to the enterprise often requires fundamental change in the cul-
ture of the enterprise, particularly vis-a-vis the process of decision-making and the hierarchy
of decision-makers.
In the traditional corporate model with an absence of precise data, it has always made
sense for firms to ultimately rely on the knowledge and intuition of the people at the “top”
- the bosses - when it came time to make key, strategic decisions. This has not only been
a question of rank and power, but also a reflection of the fact that the people in the upper
strata of the firm have generally tended to be those with the greatest experience, the longest
tenure, and therefore the most informed and viable “intuition.”
Pre Big Data and pre Data Science, this was most certainly the truth. Given highly lim-
ited amounts of data (as compared to what we have available today), the top executives of a
firm were indeed the people best positioned to digest that relatively small core of data and
make informed assumptions. However, in the new environment where vast data is leveraged
primarily for the purpose of upheaving old assumptions, the experiential (aka, “old”) know-
ledge of senior executives is often an asset of declining (if not useless) value.
The days of the HiPPO [Highest Paid Person's Opinion] are pretty much over. Intro-
ducing this fundamental idea into the culture of an enterprise is often the biggest hurdle in
