Proceedings

2. THE PROCESS OF PREDICTIVE MODELING
The process of predictive modeling requires great skill and is so important that it must
be integrated in a clear methodology ( in fact, in 1996 was created the first industry
standard methodology called CRISP – Cross Industry Standard Process for Data
Mining). So most analytic modelers adhere to a methodology to create predictive
models. But, whatever which methodology, explicit or implicit, is followed, the
processes of creating predictive models integrate the following steps:
� Project definition – define the business objectives and desired outcomes and
translate them into predictive analytic objectives and tasks. That requires a
close interaction between the business and analytic modeler.
� Exploring the data – analyze source data to determine the most appropriate
data and model building approach.
� Data preparation – select, extract and transform data upon which to create
models.
� Model building – create, test and validate models and evaluate whether they
will meet project goals.
� Deployment – apply model results to business decisions or processes.
Deploying analytical models can be achieved in more than one way : share the
model (share insights with business users via a presentation), score the model (
transform the model in a SQL statement or programming code and then apply
it to every record in the database ), embed the model in a business intelligence
report or in an application (so it drives business actions automatically).
� Model management – manage models to improve performance, control access,
promote reuse and minimize overhead. Even if now few organizations are
concerned about model management, the demand for that particular activity
will increase to comply with new compliance regulations.
Figure 8. The process of predictive modeling
Data cleaning and organizing
Data
Mining
Predictive
model
development
Predictive model
Operational data
External data
Deployment : SHARE / SCORE /EMBED IN APPLICATIONS
Model Management
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