42 ISE Magazine | www.iise.org/ISEmagazine
April 2021 | ISE Magazine 43
Getting the models right starts with a comprehen-
sive understanding of the process. A successful ap-
plication of analytics requires a study of the existing
process model and related systems. In most cases,
the existing process model needs to be updated to
consider additional variables and relationships rec-
ognized in big data sources. The validation of the causal re-
lationship of a potential new variable and data is best accom-
plished as the fit in the process model is guided by the process
owners and operators.
Creating or updating the model to accurately reflect the
process is the critical step in applying analytics. An accurate
data model then can be developed and used to describe the
results of the process, predict future outcomes and prescribe
actions to produce desired outcomes. Generally, the model of
a process and the subsequent data model necessarily becomes
more comprehensive and complex as it is applied to predict an
outcome, and even more so when used to determine an action
to cause a desired outcome.
This can be illustrated by considering the sales data for a
marketing process. For the data model to describe (report or
present) sales results over periods of time simply requires a
model or template to aggregate the sales in total and mean-
ingful distribution categories. To predict future sales, both
the process and data model need to be expanded to integrate
additional information such as demand history and industry
forecasts, competitor strategy and customer behavior and de-
sired features. To prescribe actions to cause an increase in sales
requires a process model with inclusion of potentially bene-
cial changes to the process and even more relationships and
information such as current product development, projected
competitor performance and scenario processing. Then the
data model can incorporate related data to prescribe an action.
Descriptive data models are generally well-established by
business use such as standard financial reporting formats and
quality charts. Predictive data models are derived from the
analysis of data from potentially related variables identied,
validated and included in the process model. The relationship
of some potentially predictive variables to the process may not
be obvious or not previously recognized by the process ex-
perts. These candidate variables may be external to the process
flow and need to be evaluated for logical inclusion in the mod-
el. Predictive models provide the greatest benefit from the ap-
plication analytics and can generate a competitive advantage.
Currently, few companies are committed to the development
of prescriptive models due to the challenge involved in mak-
ing them reliable.
The focus of this article is getting the predictive models
right to answer business questions, make better decisions and
improve results.
Getting the models right
Appropriate advice is provided in a blog, “7 Fundamental
Steps to Complete a Data Analytics Project,” by Alivia Smith.
She wrote: “Understanding the business or activity that your
data project is part of is key to ensuring its success and the first
phase of any sound data analytics project ... before you even
think about the data, go out and talk to the people in your
organization whose processes or whose business you aim to
improve with data.” (Data Iku, blog.dataiku.com, July 2019).
Getting the data model right for predicting outcomes is the
goal of most organizations using analytics. This requires estab-
lishing the purpose of the data model and understanding the
business process on which it is based. Creating the appropriate
process model and data model is both art and science. Identify-
ing the operative relationships of variables in the process and
relevant data can be accomplished by observation and guid-
ance from the process owners and operators. Development of
the process model is iterative and involves testing and rene-
ment. The same is true for the data model.
Getting the models right is key
to successful analytics
Predictive data model must be enhanced to respond to change, disruptions
By William E. Hammer Jr.
Look for M&S track at Annual
The IISE Modeling & Simulation Division is sponsoring a
Modeling & Simulation Track at the IISE Annual Conference &
Expo 2021, to be held virtually May 22-25. To register and save
on fees before May 3, visit iise.org/Annual/register. Full details
on the conference schedule and program will be updated at
44 ISE Magazine | www.iise.org/ISEmagazine
Getting the models right is key to successful analytics
The process model documents the
purpose of the business process and all
that is known about it; purposes, map-
ping of work, event and data flow, op-
erational variables, relationship of vari-
ables, strategies, people involved and
supporting systems and database(s).
New and potentially useful variables
and related data from big data sources
should be considered for logical and
causal relationship, and included in the
process model and database if they fit
into the process mechanism. Scanning
big data (transactions, social media, sur-
veys, etc.) for new variables and aspects
of variables already included in the process model is a critical
step in getting the process model right.
Once the process model and supporting database are es-
tablished, the work shifts to the construction of an inclusive
data model. The data model is the basis for the application
of appropriate statistical and visual tools, and algorithms, to
generate an accurate and reliable predictive model. There is
an abundance of excellent tools and methodologies avail-
able for this task. The book, Advanced Analytics Methodologies:
Driving Business Value with Analytics, by Michele Chambers
and Thomas Dinsmore, provides a clear and useful explana-
tion for these techniques.
Training and testing with carefully selected data sets are
critical to validating the data model and generating accurate
and reliable predictions. The testing and refinement cycle
produces confidence in predictive models, as recently dem-
onstrated by the COVID-19 models.
While many excellent statistical and visual tools are avail-
able for facilitating the creation of analytics models, there
is a tendency to rely on them too much and not focus on
understanding the business process and carefully identifying
the inherent predictive variables. The guidance and insight
of process owners and operators are key to the development
of an appropriate process model, data model and predictive
It is important to note that an unrelated variable and data
can appear to be predictive when the positive correlation is
coincidental. A famous example of this known as the Miers-
cheid Law, is the correlation of the Social Democratic Party
of Germany’s share of the popular vote with the size of crude
steel production in Western Germany.
The business payoff for getting the models right clearly
justifies the significant effort needed. Predictions from sub-
optimal models can produce counterproductive results or
less beneficial results. Right models produce optimal predic-
tions and better business decisions.
Getting the models right requires:
Clearly dening the purpose of the model, e.g., the
question(s) to be answered.
Understanding the process and identifying the intrinsic pre-
dictive variables.
Creating or updating the process model first.
Careful evaluation of data for potential predictive variables
to conrm causal relationship.
Discarding potential predictive variables with high correla-
tion value that a causal relationship cannot be established.
Guidance from the process experts (process owners and op-
erators) during all stages in development of models.
Avoiding overreliance on modeling tools.
Keeping the models right
Most beneficial predictive models need enhancement as the
business environment changes, process changes occur and
when additional and previously inaccessible data become
available. The disruptive effect of the COVID-19 pandemic
on retail business in particular, and the vast amount of ad-
ditional customer data made available due to the massive in-
crease in online transactions, warrant review and update of
process, data and predictive models as a regular practice.
Big data is even bigger now and provides more potential
data, in terms of variables, relationships and volume.
William E. Hammer Jr. is principal consultant of Hammer Manage-
ment Consulting. He previously was director of Corporate Information
Services/Information Systems for the Duriron Co. (now Flowserve
Corp.). He is a Fellow and past senior vice president of IISE and
member of the Dayton-Cincinnati Chapter. He is a Distinguished
Alumnus of the University of Dayton, where he is currently teach-
ing Information Systems and Business Decisions in the online MBA
program. He is a Distinguished Alumnus of the College of Engineer-
ing of The Ohio State University. He received a BIE degree from the
University of Dayton and an MSIE from Ohio State. Contact him at