I have been challenged by several people as follows: If you think that users are not spending as
much as they should on Big Data because they don't see it as a process, but rather
as a series of one-shot "big value-add insights", what then is the
process they should create? I don't
pretend to have all the answers, but here, off the cuff, are my thoughts.
My first reflex in answering these questions is to recommend
something "agile" (my definition, not the usual marketing hype). However, in today's un-agile enterprise, and
particularly dealing with un-agile senior executives, that won't work. Btw, there's a wonderful phrase for this kind
of problem, that I credit to Jim Ewel of Agile Manifesto fame: HIPPO.
It stands for the HIghest-Paid Person in the rOom, and it refers to the
tendency of decision-making to be made according to the market beliefs of the
HIPPO rather than customer data. Still, I believe something can be salvaged
from agile marketing practices in answering the question -- the idea of being
customer-data-driven.
Next, I assert that the process should have a Big Data information
architecture aimed at supporting it. If we are to use Big Data in gaining
customer insights, then our architecture should allow access to and support
integration of the three types of Big Data sources: Loosely, (1) sensor-driven (real-time streams
of data from Web sensors such as GPS tracking and smartphone video), (2)
social-media (the usual Facebook/Pinterest sources of customer
interest/interaction unstructured data), and (3) the traditional inhaled
in-house data that tends to show up in the data warehouse or data marts.
The process itself would be one of iterative deeper
understanding of the customer, equal parts understanding the customer as he/she
is now (buying procedures/patterns plus how to chunk the present and potential
parts of the market) and where he/she is going (changes in buying behavior, new
technologies/customer interests, evolution of present changes via predictive
analytics carefully applied -- because agile marketing tells us there's a
danger in uncritical over-application of predictive analytics). The process would be one of rapid iteration
of customer-focused, Big-Data using insights, typically by data scientists,
often feeding the CMO and marketing first, as befits the increased importance
in today's large enterprise of the CMO.
What I suggest you have in this process is a Big-Data-focused,
customer-insight-driven enterprise-driving analytical process. Or, for short, the "customer-driven
enterprise." As in, Big Data for
the customer-driven enterprise.
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