The Analytics Revolution E-Book

Bill Franks

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 510

Veröffentlichungsjahr: 2014

Beschreibung

Lead your organization into the industrial revolution of analytics with The Analytics Revolution

The topics of big data and analytics continue to be among the most discussed and pursued in the business world today. While a decade ago many people still questioned whether or not data and analytics would help improve their businesses, today virtually no one questions the value that analytics brings to the table. The Analytics Revolution focuses on how this evolution has come to pass and explores the next wave of evolution that is underway. Making analytics operational involves automating and embedding analytics directly into business processes and allowing the analytics to prescribe and make decisions. It is already occurring all around us whether we know it or not.

The Analytics Revolution delves into the requirements for laying a solid technical and organizational foundation that is capable of supporting operational analytics at scale, and covers factors to consider if an organization is to succeed in making analytics operational. Along the way, you'll learn how changes in technology and the business environment have led to the necessity of both incorporating big data into analytic processes and making them operational. The book cuts straight through the considerable marketplace hype and focuses on what is really important. The book includes:

• An overview of what operational analytics are and what trends lead us to them
• Tips on structuring technology infrastructure and analytics organizations to succeed
• A discussion of how to change corporate culture to enable both faster discovery of important new analytics and quicker implementation cycles of what is discovered
• Guidance on how to justify, implement, and govern operational analytics

The Analytics Revolution gives you everything you need to implement operational analytic processes with big data.

Leseprobe

Additional praise for The Analytics Revolution

“I have known Bill for many years and I admire him for his very pragmatic and straight forward approach to operationalizing analytics. Two decades of real-life, hands-on experience set Bill apart and define him as one of the top leaders in the analytics space!”

Elpida Ormanidou, Vice President, Global People Analytics, Walmart

“Franks has created another masterpiece of pragmatic insight and direction, taking the standard of practice and leaping it forward. While data scientists and data managers will appreciate the business value Franks offers, anyone who wants to advance data-driven decisioning and operational analytics needs to read this guide to reaching the next level of the analytics-based business.”

Jeff Tanner, author of Analytics and Dynamic Customer Strategyand Director, Baylor's Innovative Business Collaboratory

“As recently as a few years ago, many organizations, departments, and people remained dubious about Big Data and questioned whether analytics mattered at all. Today, those who haven't crossed the chasm are squandering massive opportunities. They appear outdated and hidebound. But where to begin? While no one book can possibly answer every question about making Big Data happen, The Analytics Revolution provides an excellent framework. I heartily recommend it.”

Phil Simon, keynote speaker and award-winning author of The Visual Organization and Too Big to Ignore

“This is a comprehensive and much-needed guidebook to successfully implementing operational analytics, automating decisions, and driving data analysis deep into business processes. There is no better guide than Bill Franks to this timely subject, fast becoming a critical strategic differentiator in the era of big data.”

Gil Press, contributor to Forbes.com

“The book offers an excellent perspective on what a business leader must do and consider to be successful with analytics. The way decisions are made at firms, by operational processes and even by customers is changing - all driven by analytics! This revolutionary change in decision-making will be a new norm in business. I highly recommend this book as a great guide on what to do and expect with operationalizing analytics!”

Russell Walker, Clinical Associate Professor, Managerial Economics and Decision Sciences, Northwestern University Kellogg School of Management

“If you're in the thick of the Big Data movement at your organization (and who isn't?), then you must read this book. Through his unique storytelling ability, Bill Franks delivers entertaining and insightful examples of how firms around the globe capitalize on their data stores through operational analytics. In particular, there is a keen focus on how to assign value to smart use of data, something that has been missing in many conversations involving Big Data. Franks follows up his succinct analysis presented in Taming The Big Data Tidal Wave by providing a surfboard for those who want to optimize their ride on the wave, and provides his vision for the future of a data driven world.”

Linda Burtch, Managing Director, Burtch Works Executive Recruiting

“One our key learnings at Kaggle is that big data is about more than building advanced algorithms. Bill has written an important book about what's involved in putting analytics into practice.”

Anthony Goldbloom, Founder & CEO, Kaggle

The Analytics Revolution

HOW TO IMPROVE YOUR BUSINESS BY MAKING ANALYTICS OPERATIONAL IN THE BIG DATA ERA

Bill Franks

Cover image: Wiley Cover design: © istock.com/jerry_only

Copyright © 2014 by Bill Franks. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, [phone], fax [phone], or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, [phone], fax [phone], or online at http://www.wiley.com/go/permissions.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at [phone], outside the United States at [phone] or fax [phone].

Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com.

Library of Congress Cataloging-in-Publication Data:

Franks, Bill, 1968- The analytics revolution : how to improve your business by making analytics operational in the big data era/Bill Franks. pages cm Includes index. ISBN 978-1-118-87367-0 (cloth); 978-1-118-97675-3 (ebk); 978-1-118-97676-0 (ebk) 1. Business intelligence. 2. Big data. I. Title. HD38.7.F733 2014 658.4′013–dc23

2014022308

This book is dedicated to Stacie, Jesse, and Danielle.

CONTENTS

Foreword

Preface

Who Should Read This Book?

Who Should

Not

Read This Book?

What's in This Book?

Acknowledgments

Part I: The Revolution Has Begun

Chapter 1: Understanding Operational Analytics

Defining Operational Analytics

Welcome to Analytics 3.0

How Analytics Are Changing Business

Putting Operational Analytics in Perspective

Wrap-Up

Notes

Chapter 2: More Data . . . More Data . . . Big Data!

Cutting through the Hype

Preparing for Big Data

Putting Big Data in Context

Wrap-Up

Notes

Chapter 3: Operational Analytics in Action

Improving Customer Experiences

Time Is of the Essence

Making Us Safer

Increasing Operational Efficiency

Improving Our Lives in the Future

Finding Unexpected Value in Data

Wrap-Up

Notes

Part II: Laying the Foundation

Chapter 4: Want Budget? Build the Business Case!

Setting the Priorities

Choosing the Right Decision Criteria

Business Case Framework to Consider

Tips for Creating a Winning Business Case

Wrap-Up

Notes

Chapter 5: Creating an Analytics Platform

Planning

Building

Using

Wrap-Up

Notes

Chapter 6: Governance and Privacy

Setting the Stage for Governance

Deciding Where Analytics Happen

Governing Operational Analytics

Privacy

Wrap-Up

Notes

Part III: Making Analytics Operational

Chapter 7: The Analytics

Creating Operational Analytics Processes

Expanding into New Analytics Disciplines

Focusing Analytics Efforts

Comparing Analytics Approaches

Lessons from the Past

Wrap-Up

Notes

Chapter 8: The Analytics Organization

A Major Shift Has Occurred

Staffing

Organizing

Succeeding

Wrap-Up

Notes

Chapter 9: The Analytics Culture

Instilling the Proper Mind-Set

Implementing Effective Policies

Facilitating Success

Enabling and Handling the Right Failures

Wrap-Up

Notes

Conclusion: Join the Revolution!

About the Author

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1

Chapter 2

Table 2.1

Chapter 4

Table 4.1

Chapter 6

Figure 6.1

Table 6.1

Figure 6.2

Chapter 7

Table 7.1

Chapter 8

Table 8.1

Chapter 9

Table 9.1

List of Illustrations

Chapter 1

Figure 1.1 Analytics 1.0: Traditional Analytics

Figure 1.2 Analytics 2.0: The Big Data Era

Figure 1.3 Analytics 3.0: Fast Business Impact for the Data Economy

Chapter 2

Figure 2.1 Start from the Right Perspective

Figure 2.2 Three Ways to Drive Value with Big Data

Figure 2.3 Scaling Big Data: Typical Focus Dimensions

Figure 2.4 Scaling Big Data: Necessary Focus Dimensions

Figure 2.5 Big Data as Distinct Silo

Figure 2.6 Integrated Big Data

Figure 2.7 Challenges with Any New Data Source

Chapter 4

Figure 4.1 Paint a Bigger Picture

Figure 4.2 Time to Insight Components

Figure 4.3 Typical Total Value Decomposition

Figure 4.4 Cost Components of an Analytics Investment

Figure 4.5 Labor Costs that Must Be Accounted For

Chapter 5

Figure 5.1 Traditional versus Fabric-Based Computing

Figure 5.2 Unified Analytics Environment

Figure 5.3 Sample of Semistructured JSON Data

Figure 5.4 How a Discovery Platform Streamlines Analytics

Figure 5.5 Incorrect Node-Level Mean Computation

Figure 5.6 Correct System-Level Mean Computation

Chapter 7

Figure 7.1 Generic Analytics Process Flow

Figure 7.2 A Multidiscipline Discovery Platform

Figure 7.3 Sample Decay Rates

Chapter 8

Figure 8.1 Analytics Degrees versus Regular Degrees

Figure 8.2 Covering All the Bases on an Analytics Team

Figure 8.3 Hybrid Organizational Structure

Guide

Cover

Table of Contents

Part I

Pages

xi

xii

xiii

xv

xvi

xvii

xviii

xix

xx

xxi

xxii

xxiii

xxiv

xxv

1

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

87

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

267

269

270

271

272

273

274

275

276

277

278

Foreword

If you have followed the topics of business intelligence, analytics, and big data over the last decade or two, you may have wondered what is coming next. After all, the initial flurry of excitement about big data is beginning to subside, and analytics of all kinds have become an important part of business, but a familiar one by now.

What's next is in this book. Bill Franks refers to it as “operational analytics,” but it could also be called such terms as “production analytics,” “real-time analytics,” or “decision automation.” As these terms suggest, the nature of how analytics are performed is changing rapidly. It's not the analytics themselves that are changing so much. As Franks notes, operational analytics are mostly the same analytics we've done for decades, even centuries. What has changed is the context in which they are carried out.

You can read the details in the book, and you should. I will say here that instead of the back-office, slow, batch analytics of the past, operational analytics are being done much more rapidly and continuously. They are being integrated with business processes and systems, rather than being done separately. I've called this trend “Analytics 3.0,” as you will read in his first chapter, but Bill's term “operational analytics” is certainly more descriptive. And he gives a lot more detail about how this world works than I ever did.

This movement is long overdue, after 50 years of separation between analytics and the operations of businesses. The separation created a number of problems. Decision-makers often requested analytics and data to support their decisions, but didn't actually use them. They probably wanted to appear more rational and analytical than they actually were. Quantitative analysts, who should have been at the front and center of business decisions and actions, were generally at significant remove from them (as Franks notes from his own experience in Chapter 8). Everything with analytics took far longer than it needed to. Analytics were still useful in this context, but not nearly as useful as they might have been.

Given all these problems with traditional analytics, it is perhaps testimony to the power of the field that organizations still plan to embed and institutionalize them in their business activities, rather than leaving them optional and tacked-on. The work on operational analytics suggests that analytics can no longer be marginalized because of the way they are undertaken. Analytics need to inform decisions both strategic and tactical, and they need to be done at the pace, time, and location of business operations. As the pace of data flow has quickened within companies, so must the pace of analytics and decision-making be accelerated.

If you weren't wondering what's coming next, you're probably wondering whether this book is yet another one on big data. The answer is no—in part because Franks already wrote an excellent one on that topic, Taming the Big Data Tidal Wave. It's not a big data book in another sense, because it addresses the use of all sizes and types of data. In fact, this book might be described as the first post-big data book. Franks takes for granted that organizations will use their small, structured data assets as well as their large, less-structured data assets. Why would anyone do otherwise? It seems obvious that data can be useful no matter what its size or structure. Unfortunately, since small data came before big data, few if any other books have had “all data” as their focus, and have few have counseled that your technology environment and analytical activities should be tailored to the various types of data you will be managing and analyzing.

This is also one of the first books that focuses on the “analytics of things” topic. There are many books now on the “Internet of Things” (IoT); a quick search on Amazon today yielded more than a dozen, even though that term is relatively new. But much less has been said about the way to produce value from sensor data, which is to analyze it and mine it for insights and anomalies. Many of Franks' examples of operational analytics involve the IoT, and he discusses how analytics can be used to deal with the vast streams of data those sensors produce.

Despite the fact that Bill is the Chief Analytics Officer for Teradata, he is quite neutral about technologies and vendors. Chapter 5 in this book, for example, includes a very even-handed discussion about the relative merits of Hadoop and enterprise data warehouses based on relational technology. I think Bill is correct in that the vast majority of organizations will employ a variety of technologies to store and analyze data. Nothing ever seems to go away; new technologies augment the old ones, and the amount of data grows at a sufficient pace to require them all.

The book addresses a wide range of topics, from technology to privacy to people topics. It's all here in highly useful and digestible form. It's not Franks' style to make wild-eyed predictions or pronouncements; instead you get calm, straightforward discourse about the way things are with operational analytics in 2014.

The word “revolution” in the title is apt. This move to operational analytics is revolutionary in a variety of ways that are covered in the book, but there is at least one revolutionary issue that Franks does not delve into substantially. Embedded, real-time analytics raise a lot of questions about how organizations will work in the future. When computers are making most of the decisions, what happens to the people who were previously making them? How can humans monitor and improve the approach to decision-making when it is essentially invisible? Franks does point out that when decisions are made in real time with little or no human intervention, it has to be a really good set of analytics and decision rules, or you can lose a lot of money very quickly. He doesn't say a lot beyond that about the new roles for humans in all this, however. I must say that I was glad to see that, because I am working on a book myself about this topic!

So jump into this book and into a previously unknown world where many important decisions are made through operational analytics. You have nothing to lose but your indecision and your office in the back!

Thomas H. Davenport, President's Distinguished Professor of IT and Management, Babson College; Co-Founder and Research Director, The International Institute for Analytics

Preface

Like manufacturing in the 1800s, the field of analytics needs to go through its own industrial revolution. Analytics processes today are usually created in an artisanal fashion with a lot of care and customization. That's okay in many cases, and the artisanal approach often still is appropriate. However, we must also push analytics forward to another level of scale and impact. The industrial revolution took manufacturing processes from an artisanal practice to a modern technological marvel that is able to manufacture quality items at massive scale. The same type of revolution must happen with analytics.

Centuries ago, if a bowl was needed, then a visit to a potter was necessary. A potter can make a custom bowl to fit any need. The problem is that such an approach isn't scalable. The limited pool of potters can create only so many bowls in a day. Today most bowls are created on a large scale in manufacturing plants. Although it is still possible to purchase a custom bowl from a potter, it isn't cost effective to use that approach except for special situations. Besides cost considerations, people today also often prefer the consistency of a mass-manufactured product. However, even in today's world, bowls don't magically appear. Someone still has to come up with a design, build initial prototypes, create a mold, and validate that the mold will produce the right bowl time and time again. Only then is an assembly line turned on to manufacture the bowl at scale.