g oal - d rive n a n a ly t i c s
established along with target performance and its impact on operations?
Without the baseline and target, how will
success of the analytic initiative be defined, measured and interpreted?
• Ability to affect: Does the organization have the willingness and wherewithal to carry out potential model
recommendations? If not, we haven’t
passed the “so what” test. It is far less
expensive to determine in advance of a
modeling objective that the organization
“can’t handle the truth.”
• Decision culture: Does your company drive more from general leadership
experience and feel, or evidence-based
decisioning? If the former, is leadership
open to letting go of one handlebar and
allow a pilot to compete in a series of A/B
tests?
• Cost of status quo: Referencing
back to “Buy-in” at the top of the list, considering the ultimate cost of doing nothing is often what gets leadership off the
fence. Leadership need not be analytically literate to appreciate that supporting costly big data initiatives does not
make sense unless a more capable and
purposeful analytic practice is prepared
to leverage it.
The information amassed from these
and many other strategic and tactical
considerations is used to prepare a highly
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tailored analytic project design. The resulting process supports agile model development by functional managers and
business practitioners. This is the engine
required to generate measurable benefit
from big data.
Goal-Driven Analytics Will
Justify Big Data
Until leadership grants analytic teams
the six to eight weeks to assess and
design tailored analytic processes that
will rapidly produce analytic models to
support specific business targets, data
analysis will continue to be a theoretical
practice that produces little more than interesting insights and isolated low-value
remedies. The vast majority of companies will remain analytically immature and
dysfunctional. This creates a significant
competitive opportunity for those who invest in formal strategic assessment and
design.
Here are the primary
takeaways:
1. Don’t wait for big data to stand
up. It’s a journey and not a destination. Analytics can start bringing value
at any stage of a big data implementation and even help justify further big data
investment.
2. Get trained. Seek a vendor-neutral
trainer that not only provides methods
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