ANALY TIC S AC ROS S T H E E N T ERP RISE
explored innovative ways to determine
development expense at a detailed level,
thereby addressing a problem that many
thought was impossible to solve. IBM
analytic teams haven’t waited for perfect
data to get started; rather, they have refined and improved their data along the
way. …
The key is to put a stake in the ground
with a commitment that analytics will be
woven into your strategy. That’s how IBM
does it. This approach is also effective
with big data. Rather than postpone the
leveraging of big data, you should embrace it, establish a link between your
business priorities and your information
agenda, and apply analytics to become a
smarter enterprise. …
with expertise in the data in that particular area of the business.
A joint study by MIT Sloan and the
IBM Institute for Business Value developed several recommendations. The first
is that you start with your biggest and
highest-value business challenge. The
next recommendation is to ask a lot of
questions about that challenge in order to
understand what’s going on or what could
be going on. Then you go out and look for
what data you might have that’s relevant
to that challenge. Finally, you determine
which analytic technique can be used to
analyze the data and solve the problem.
Because most companies have constraints on the amount of money and
skills available for projects, estimating
the ROI can provide a better differentiator
PROVEN APPROACHES
for selecting the project with the highest
Staying focused on solving business potential impact than relying on instincts.
problems was the pragmatic start, and Estimating an analytics project’s ROI inthe other crucial element was having very volves both capturing the project costs
high-level executive support from the be- and measuring the value. …
ginning. From a governance perspective,
those are two key levers to drive value: EMERGING THEMES
focus on actions and decisions that will
generate value and have high-level executive sponsorship.
The ideal team to do analytics is a
collaboration between an experienced
data scientist, a person steeped in the
area of the business where the challenge
needs to be solved, and an IT person
66
|
A N A LY T I C S - M A G A Z I N E . O R G
Relationships inferred from data
today may not be present in data collected tomorrow. The relationships that
you infer from data about the past do
not necessarily hold in data that you collect tomorrow. You cannot analyze data
once and then make decisions forever
based on old analysis. It’s important to
W W W. I N F O R M S . O R G