Keeping Up with the Quants: Your Guide to Understanding and Using Analytics

244 Pages·2013·1.82 MB·English

by  Thomas H. Davenport

Copyright

Copyright 2013 Harvard Business School Publishing Corporation
All rights reserved
No part of this publication may be reproduced, stored in or introduced into a
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First eBook Edition: Jun 2013
ISBN: 978-1-4221-8725-8
Contents

Copyright
Acknowledgments
1. Why Everyone Needs Analytical Skills
2. Framing the Problem
3. Solving the Problem
4. Communicating and Acting on Results
5. Quantitative Analysis and Creativity
6. Developing Quantitative Analysis Capabilities
7. Working with Quants

Notes
About the Authors
Acknowledgments

Tom thanks (and dedicates his portion of the book) to his loving wife of more
than thirty years, Joan Powell Davenport. His sons Hayes and Chase have left
the nest now, but they still support Dad’s work remotely. The Harvard Business
School Division of Research was less loving, but did provide useful research
support.
Jinho thanks John and Bonnie Ries for their enthusiastic support and
encouragement, including their proofreading of the original manuscript.
Particular thanks go to Jung-Hwa Shin for motivating and consistently
supporting Jinho in writing this book. Jinho also thanks his elder daughter Nuri,
his son-in-law Dong-Wook, and his younger daughter Youngri for their love and
inspiration. Jinho dedicates his share of this book to his mother, Tae-Hwa Cho,
for her love and wisdom.
Tom and Jinho both thank Melinda Merino for great editing at Harvard
Business Review Press, as well as all the other talented staff who make the Press
the best business publisher in the business. Thanks, too, to Mark Allen for
providing the charming illustrations. We’d also like to thank all the
groundbreaking quants and quantitatively oriented executives, living or dead,
who provided us with the examples in this book. We are merely the chroniclers
of their heroic exploits.
Why Everyone Needs Analytical Skills
We live in a world awash with data. Data is proliferating at an astonishing rate—
we have more and more data all the time, and much of it was collected in order
to improve decisions about some aspect of business, government, or society. If
we can’t turn that data into better decision making through quantitative analysis,
we are both wasting data and probably creating suboptimal performance.
Therefore, our goal in this book is to show you how quantitative analysis works
—even if you do not have a quantitative background—and how you can use it to
make better decisions.
The Rise of Analytics and Big Data

The rise of data is taking place in virtually every domain of society. If you’re
into sports, you undoubtedly know about moneyball, the transformation of
professional baseball—and by now virtually every major sport—by use of player
performance data and analytics. If you’re into online gaming, you probably
realize that every aspect of your game behavior is being collected and analyzed
by such companies as Zynga and Electronic Arts. Like movies? If so, you
probably know about the algorithms Netflix uses to predict what movies you will
like. You may not have heard that a few producers of Hollywood movies,
including Relativity Media, employ algorithms to decide which movies to back
financially.
Of course, there are different types of data. Some is collected and managed for
transactional purposes—for example, your company or organization keeps track
of the date you started work or your vacation balance. But after a while,
organizations have accumulated a lot of data and they want to make sense of it
and make decisions on the basis of it. Data on human resources transactions can
also be explored with analytics. Organizations might ask questions like, “How
many employees are likely to retire next year?” or “Is there a relationship
between using all your vacation days and your annual performance rating?”
But data and analytics don’t just improve internal decision making. Many
Internet-based organizations—Google, Facebook, Amazon, eBay, and others—
are using so-called big data from online transactions not only to support
decisions but to create new product offerings and features for customers.
Whether you’re seeking better internal decisions or more value for customers,
that’s where analytics come in—they summarize data, find the meaning in it, and
find patterns in it. Making sense of and extracting value from data can only be
done through mathematical or statistical analysis of data—in a word, analytics
(see “What Are Analytics?”)
What Are Analytics?

By analytics, we mean the extensive use of data, statistical and quantitative
analysis, explanatory and predictive models, and fact-based management to
drive decisions and add value.a
Analytics can be classified as descriptive, predictive, or prescriptive according
to their methods and purpose. Descriptive analytics involve gathering,
organizing, tabulating, and depicting data and then describing the characteristics
about what is being studied. This type of analytics was historically called
reporting. It can be very useful, but doesn’t tell you anything about why the
results happened or about what might happen in the future.
Predictive analytics go beyond merely describing the characteristics of the
data and the relationships among the variables (factors that can assume a range
of different values); they use data from the past to predict the future. They first
identify the associations among the variables and then predict the likelihood of a
phenomenon—say, that a customer will respond to a particular product
advertisement by purchasing it—on the basis of the identified relationships.
Although the associations of variables are used for predictive purposes, we are
not assuming any explicit cause-and-effect relationship in predictive analytics. In
fact, the presence of causal relationships is not always necessary to make
accurate predictions.
Prescriptive analytics, including methods such as experimental design and
optimization, go even further. Like a prescription from a doctor, they suggest a
course of action. Experimental design tries to answer the questions of why
something happened by conducting experiments. To make causal inferences with
confidence in causal research, researchers must manipulate one or more
independent variables and effectively control other extraneous variables. If the
test group—the one with the experimental condition present—performs
substantially better than the control group—then the decision maker should
apply that condition broadly.
Optimization, another prescriptive technique, attempts to identify the ideal
level of a particular variable in its relationship to another. For example, we might
be interested in identifying the price of a product that is most likely to lead to
high profitability for a product. Similarly, optimization approaches could identify
the level of inventory in a warehouse that is most likely to avoid stock-outs (no
product to sell) in a retail organization.
Analytics can be classified as qualitative or quantitative according to the
process employed and the type of data that are collected and analyzed.
Qualitative analysis aims to gather an in-depth understanding of the underlying
reasons and motivations for a phenomenon. Usually unstructured data is
collected from a small number of nonrepresentative cases and analyzed
nonstatistically. Qualitative analytics are often useful tools for exploratory
research—the earliest stage of analytics. Quantitative analytics refers to the
systematic empirical investigation of phenomena via statistical, mathematical, or
computational techniques. Structured data is collected from a large number of
representative cases and analyzed statistically.
There are various types of analytics that serve different purposes for
researchers:

Statistics: The science of collection, organization, analysis, interpretation,
and presentation of data
Forecasting: The estimation of some variable of interest at some specified
future point in time as a function of past data
Data mining: The automatic or semiautomatic extraction of previously
unknown, interesting patterns in large quantities of data through the use of
computational algorithmic and statistical techniques
Text mining: The process of deriving patterns and trends from text in a
manner similar to data mining
Optimization: The use of mathematical techniques to find optimal
solutions with regard to some criteria while satisfying constraints
Experimental design: The use of test and control groups, with random
assignment of subjects or cases to each group, to elicit the cause and effect
relationships in a particular outcome

Although the list presents a range of analytics approaches in common use, it is
unavoidable that considerable overlaps exist in the use of techniques across the
types. For example, regression analysis, perhaps the most common technique in
predictive analytics, is a popularly used technique in statistics, forecasting, and
data mining. Also, time series analysis, a specific statistical technique for
analyzing data that varies over time, is common to both statistics and
forecasting.
a. Thomas Davenport and Jeanne G. Harris, Competing on Analytics (Boston:
Harvard Business School Press, 2007), 7.