g oal - d rive n a n a ly t i c s
and tactics, but has a specific focus on
project-level strategic implementation.
3. Comprehensive assessment. It
is infinitely more effective to select the
most viable and valuable modeling project after having surveyed leadership,
team members, resources and the environment than to perform great work on
a doomed initiative or start sifting for insights without a performance target.
4. Conduct an underground pilot. If
the initial results fall short, that’s part of
the overall discovery process. Shift and
cycle again. If they exceed, then market
to leadership and expand.
5. Ongoing strategic oversight.
Seek the guidance of a seasoned mentor. This consultant will have the experience to anticipate hurdles, overcome
elusive pitfalls and provide a low-risk/
high-reward roadmap to greater returns
in a shorter time frame.
Without a formal and comprehensive assessment performed by a senior
strategic analytic consultant, organizations will continue to perform analysis for
the sake of analysis. The results of this
practice will uncover some discoveries
of interest that rarely align with business
objectives or translate to impact.
Instead, goal-directed analysis driven
by a methodical assessment and tailored
project design lifts a specific business
38
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a n a ly t i c s - m a g a z i n e . o r g
objective by a measurable margin. Of
course, this is what translates well for
leadership and puts data productively to
work, whether big or small. ❙
Eric A. King is the president and founder of
The Modeling Agency, LLC, an advanced analytics
training and consulting company providing
strategic guidance and impactful results for the
data-rich yet information-poor. King is a copresenter in a monthly live, interactive analytics
webinar entitled “Data Mining: Failure to Launch.”
He may be reached at (281) 667-4200 x210 or
[email protected].
Acknowledgements
The author thanks Carla Gentry of Analytical
Solution for granting permission to use a slight
variation of a fantastic blog phrase as the title of
this article. Also, cheers to Professor Dan Ariely
of Duke University for permission to quote his
hilariously accurate teenage sex analogy for big
data. Gratitude is extended to the International
Institute for Analytics (IIA) and Tom Davenport to
reference Analytics 3.0 and related IIA material in
this article and TMA courseware with attribution.
And finally, a gracious nod to Sandra Hendren,
senior consultant at The Modeling Agency, for her
review and brilliant edits.
REFERENCES
1. Gartner, Inc., “Hype Cycles 2013 Research
Report,” Gartner Technology Research, 2013
(https://www.gartner.com/doc/2571624).
2. Thomas H. Davenport. “Analytics 3.0,”
Harvard Business Review (http://hbr.org/2013/12/
analytics-30), December 2013.
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