Monetizing Your Data

A Guide to Turning Data into Profit-Driving Strategies and Solutions

Andrew Roman Wells, Kathy Williams Chiang

• Verlag: Wiley
• Jahr: 2017
• ISBN:
  9781119356257
• Auflage: 1
• Thema: Betriebswirtschaft
• Thema: Informationsmanagement

Transforming data into revenue generating strategies and actions

Organizations are swamped with data—collected from web traffic, point of sale systems, enterprise resource planning systems, and more, but what to do with it? Monetizing your Data provides a framework and path for business managers to convert ever-increasing volumes of data into revenue generating actions through three disciplines: decision architecture, data science, and guided analytics. There are large gaps between understanding a business problem and knowing which data is relevant to the problem and how to leverage that data to drive significant financial performance. Using a proven methodology developed in the field through delivering meaningful solutions to Fortune 500 companies, this book gives you the analytical tools, methods, and techniques to transform data you already have into information into insights that drive winning decisions. Beginning with an explanation of the analytical cycle, this book guides you through the process of developing value generating strategies that can translate into big returns. The companion website, www.monetizingyourdata.com, provides templates, checklists, and examples to help you apply the methodology in your environment, and the expert author team provides authoritative guidance every step of the way.

This book shows you how to use your data to:

• Monetize your data to drive revenue and cut costs
• Connect your data to decisions that drive action and deliver value
• Develop analytic tools to guide managers up and down the ladder to better decisions

Turning data into action is key; data can be a valuable competitive advantage, but only if you understand how to organize it, structure it, and uncover the actionable information hidden within it through decision architecture and guided analytics. From multinational corporations to single-owner small businesses, companies of every size and structure stand to benefit from these tools, methods, and techniques; Monetizing your Data walks you through the translation and transformation to help you leverage your data into value creating strategies.

SECTION IV

AGILE ANALYTICS

CHAPTER 8
Decision Theory: Making It Rational

While Data Science helps you turn information into actionable insights, Decision Theory helps you structure the decision process to guide a person to the correct decision. Decision Theory, along with Behavioral Economics, is focused on understanding the components of the decision process to explain why we make the choices we do. It provides a systematic way to consider tradeoffs among attributes that helps us make better decisions.

According to Martin Peterson, Decision Theory is “more concerned with rational decisions, rather than the right ones…it seems impossible to foresee, even in principle, which act is right until the decision has already been made. It seems much more reasonable to claim that it is always possible to foresee whether a decision is rational.”

As we go through the concepts in this chapter you will see how everyone utilizes some degree of decision theory and behavioral economics in their everyday lives as we make tradeoffs around decisions, most of which have elements of uncertainty.

A real-world example of applying decision theory comes from the security technology world. Often when evaluating security threats, there is a great deal of uncertainty about the type of threat and the right type of countermeasures to deploy. Normally a software engineer codes what they believe to be the best course of action when writing the code. However, this approach is changing. By leveraging decision theory, we can develop probable and reasonable options to help guide the decision to choose countermeasures to deploy that have the highest likelihood of success given the range of uncertainty at the time of the incident, thereby greatly increasing the chances for success.

In Decision Theory, there are two kinds of analysis, descriptive and normative. Descriptive analysis is what people actually do, how they actually make decisions. Normative theories seek to answer what people ought to do. Since most of the decisions we make are based on what people ought to do, we will focus our efforts here on the normative techniques and principles.

We layer in techniques from behavioral economics, which apply psychological insights into human behavior with economic analysis to explain decision making. Examples of behavioral economics include cognitive biases and choice architecture.

We are going to cover several practical techniques you can consider as additional Lego pieces when you build your analytical solution to monetize data. These techniques include: Decision Matrix, Probability, Prospect Theory, Choice Architecture, and Cognitive Bias.

Decision Matrix

One of the most important tools we use throughout the Analytical Cycle is the Decision Matrix. A decision matrix reflects the outcome and values of various decision scenarios in a grid format. The decision matrix is a great tool to use when looking at a large group of decision factors to assess each factor's significance. This format enables the manager to quickly analyze relationships between the decision factors to determine the optimal choice.

Acts, events, outcomes, and payoffs are the four building blocks of decision theory. Acts are the actions or decisions that a person may take. Events are the occurrences taking place, usually with a level of uncertainty. Outcomes are the results of the occurrences, and payoffs are the benefits the decision maker receives from the occurrences. The benefits within the matrix can be numerical or descriptive in nature.

For example, we are trying to decide if we should drive for no cost or take the car service Uber for $11 to a show. At this particular venue, the parking is free but there are often delays that may make us late for our show. The two decisions we have to choose between are to drive ourselves or take Uber; these would be the acts. The parking delays are the events that cause a level of uncertainty. The payoffs in this scenario are the $11 spent on Uber versus driving ourselves for $0. An additional payoff is to be on time for the show. Here is our decision matrix:

	Parking Delays	No Parking Delays
Uber car service	On-time arrival/$11	On-time arrival/$11
Drive self	Late for show/$0	On-time arrival/$0

From this decision matrix, we can see that paying the $11 would reduce our uncertainty of making the show on time. The question is whether $11 is worth reducing the uncertainty.

To make a good decision matrix, there are a few principles that we would recommend:

• Actionable—Among the core components of a decision matrix are the acts or decisions that we outline for the user. You need to make sure that decisions you are considering are actionable for the organization, particularly if you are in a large matrix organization. If the actions you outline are too difficult to implement, the analytical solution will probably not get off the ground.
• Rationalize Attributes—How many attributes or metrics do you need to show in your decision matrix to help someone make a decision? This may be one of the toughest questions to answer. While a natural tendency is to consider as much information as possible, we need to ask ourselves which metrics really drive the decision and focus on these.
• Payoff—You must determine a monetary value or some other utility derived from making the decision, like an on-time arrival to the show. This is the incentive to make the decision. In most of our work, we find adding a monetary value to the decision helps the analyst weigh the economic benefits versus the risks, leadi...

Inhaltsverzeichnis

• Cover
• Title Page
• Copyright
• Table of Contents
• Dedication
• Preface
• Acknowledgments
• About the Authors
• Section I: Introduction
• Section II: Decision Analysis
• Section III: Monetization Strategy
• Section IV: Agile Analytics
• Section V: Enablement
• Section VI: Case Study
• Bibliography
• Index
• End User License Agreement