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how many times it happened. In the initial level, most of the analysis is reactive in nature
and looks backward into historical data. The analysis performed at this level does not
have repeatability and in most cases is ad-hoc in nature; the data management platforms
and analyst teams are set up on an as-needed basis. The next level of maturity is
“Repeatable and Defined:” at this level, you start looking into unique drivers, root causes,
cause-effect analysis as well as performing simulation scenarios like “What-If.” At this
level, the data management platforms are in place and analysts' teams have a pre-defined
role and objectives to support. The next level is “Optimized and Predictive”: at this level,
you are doing deeper data analysis, performing business modeling and simulations with
a goal to predict what will happen.
Figure 1-6.
Analytics process maturity
While the analytics process maturity levels help organizations to identify where
they are at present and then gives them a road map to get to the desired higher levels of
maturity, another critical component in the transformational journey is the organization
model. You can have the best tools installed and the best people in your team, but if you
do not have a rightly aligned organizational model, your journey becomes tougher.
There are three types of organization models (“decentralized,” “shared services,” and
“independent”), and each one of these models has its pros and cons (see Figure
1-7
).
