TUTORIAL: PRESCRIPTIVE ANALYTICS
Kathryn
Blackmond Laskey
Paulo
C. G. da Costa
Edward
Huang
Rajesh
Ganesan
George Mason University
Department of Systems Engineering and Operations Research
May 4, 2013
Data analytics
(i.e., the process of acquiring, extracting, integrating, transforming,
and modeling data with the goal of deriving useful information) is
increasingly important across a wide variety of applications. The need
for data analytics is driven by the massive accumulation of “Big Data”
in a variety of industries such as healthcare, finance, government
(federal, state, and local), and cyber defense. The ultimate goal of
data analytics is to derive value by suggesting effective actions for
the future. Prescriptive analytics focuses on methods for deciding on
the best course of action, while taking into account constraints and
risks. This tutorial in prescriptive analytics will introduce methods
to drive effective decision making and to identify and select optimal
courses of action. Techniques are discussed to analyze both structured
and unstructured data to derive meaningful knowledge, which will be
useful for developing effective strategies and making optimal
decisions. The use of prescriptive analytic methods in computerized
decision support systems is also discussed. The tutorial emphasizes
both analytical and practical aspects of prescriptive analytics.
Hands-on exercises stress the practical aspects of modeling,
optimization, and risk analysis. Students are also expected to
demonstrate proficiency in decision making, design of decision support
systems, and risk analysis.
