Big Data, Big Innovation E-Book

Evan Stubbs

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: SAS Institute Inc
• Sprache: Englisch

Seitenzahl: 352

Veröffentlichungsjahr: 2014

Beschreibung

A practical guide to leveraging your data to spur innovation and growth Your business generates reams of data, but what do you do with it? Reporting is only the beginning. Your data holds the key to innovation and growth - you just need the proper analytics. In Big Data, Big Innovation: Enabling Competitive Differentiation Through Business Analytics, author Evan Stubbs explores the potential gold hiding in your un-mined data. As Chief Analytics Officer for SAS Australia/New Zealand, Stubbs brings an industry insider's perspective to guide you through pattern recognition, analysis, and implementation. Big Data, Big Innovation: Enabling Competitive Differentiation Through Business Analytics details a groundbreaking approach to ensuring your company's upward trajectory. Use this guide to leverage your customer information, financial reports, performance metrics, and more to build a rock-solid foundation for future growth. * Build an effective analytics team, and empower them with the right tools * Learn how big data drives both evolutionary and revolutionary innovation, and who should be responsible * Identify data collection and analysis opportunities and implement action plans * Design the platform that suits your company's current and future needs * Quantify performance with statistics, programming, and research for a more complete picture of operations Effective management means combining data, people, and analytics to create a synergistic force for innovation and growth. If you want your company to move forward with confidence, Big Data, Big Innovation: Enabling Competitive Differentiation Through Business Analytics can show you how to use what you already have and acquire what you need to succeed.

Leseprobe

Contents

Preface

Acknowledgments

Part One: May You Live in Interesting Times

Chapter 1: Lead or Get Out of the Way

The Future Is Now

The Secret Is Leadership

Notes

Chapter 2: Disruption as a Way of Life

The Age of Uncertainty

The Emergence of Big Data

Rise of the Rōnin

The Knowledge Rush

Systematized Chaos

Notes

Part Two: Understanding Culture and Capability

Chapter 3: The Cultural Imperative

Intuitive Action

Truth Seeking

Value Creation

Functional Innovation

Revolutionary Disruption

Notes

Chapter 4: The Intelligent Enterprise

Level 1: Unstructured Chaos

Level 2: Structured Chaos

Levels 3–5: The Intelligent Enterprise

Notes

Part Three: Making It Real

Chapter 5: Organizational Design

What Should It Look Like?

What Should It Focus On?

What Services Can It Offer?

What Data Does It Need?

Note

Chapter 6: Operating Models

What’s the Goal?

What’s the Enabler?

How Does It Create Value?

Notes

Chapter 7: Human Capital

What Capabilities Do I Need?

How Do I Get the Right People?

How Do I Keep Them?

Notes

Part Four: Making It Happen

Chapter 8: Innovating with Dynamic Value

The Innovation Cycle

The Innovation Paradox

The Secret to Success: Dynamic Value

The Innovation Engine

Reinventing the Rōnin

Notes

Chapter 9: Creating a Plan

Starting the Conversation

Defining the Vision

Identifying Opportunities

Mapping Responsibilities

Taking It to the Next Level

Note

Conclusion: The Final Chapter Is Up to You

Glossary

About the Author

Index

End User License Agreement

List of Illustrations

Figure P2.1 Culture and Capability

Figure 3.1 The Cultural Imperative

Figure 4.1 Intelligent Enterprise

Figure 5.1 Structural Choices

Figure 5.2 Service Design

Figure 6.1 The Value of Business Analytics

Figure 6.2 The Wheel of Value

Figure 6.3 The Return Cycle

Figure 7.1 The Path to Profitability

Figure 7.2 Data Science vs. Value Architecture

Figure 7.3 Data Science Combined with Value Architecture

Figure 7.4 The SMART Model

Figure 8.1 The Innovation Cycle

Figure 8.2 Dynamic Value

Figure 8.3 The Innovation Engine

Figure 9.1 The Cover Story

Figure 9.2 Affinity Map

Figure 9.3 The Stakeholder Matrix

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Guide

Cover

Table of Contents

Begin Reading

Wiley & SAS Business Series

The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions.

Titles in the Wiley & SAS Business Series include:

Activity-Based Management for Financial Institutions: Driving Bottom-Line Results

by Brent Bahnub

Analytics in a Big Data World: The Essential Guide to Data Science and Its Applications

by Bart Baesens

Bank Fraud: Using Technology to Combat Losses

by Revathi Subramanian

Big Data Analytics: Turning Big Data into Big Money

by Frank Ohlhorst

Branded! How Retailers Engage Consumers with Social Media and Mobility

by Bernie Brennan and Lori Schafer

Business Analytics for Customer Intelligence

by Gert Laursen

Business Analytics for Managers: Taking Business Intelligence beyond Reporting

by Gert Laursen and Jesper Thorlund

The Business Forecasting Deal: Exposing Bad Practices and Providing Practical Solutions

by Michael Gilliland

Business Intelligence Applied: Implementing an Effective Information and Communications Technology Infrastructure

by Michael S. Gendron

Business Intelligence and the Cloud: Strategic Implementation Guide

by Michael S. Gendron

Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy

by Olivia Parr Rud

Business Transformation: A Roadmap for Maximizing Organizational Insights

by Aiman Zeid

CIO Best Practices: Enabling Strategic Value with Information Technology

, Second Edition by Joe Stenzel

Connecting Organizational Silos: Taking Knowledge Flow Management to the Next Level with Social Media

by Frank Leistner

Credit Risk Assessment: The New Lending System for Borrowers, Lenders, and Investors

by Clark Abrahams and Mingyuan Zhang

Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring

by Naeem Siddiqi

The Data Asset: How Smart Companies Govern Their Data for Business Success

by Tony Fisher

Delivering Business Analytics: Practical Guidelines for Best Practice

by Evan Stubbs

Demand-Driven Forecasting: A Structured Approach to Forecasting, Second Edition

by Charles Chase

Demand-Driven Inventory Optimization and Replenishment: Creating a More Efficient Supply Chain

by Robert A. Davis

Developing Human Capital: Using Analytics to Plan and Optimize Your Learning and Development Investments

by Gene Pease, Barbara Beresford, and Lew Walker

The Executive’s Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business

by David Thomas and Mike Barlow

Economic and Business Forecasting: Analyzing and Interpreting Econometric Results

by John Silvia, Azhar Iqbal, Kaylyn Swankoski, Sarah Watt, and Sam Bullard

Executive’s Guide to Solvency II

by David Buckham, Jason Wahl, and Stuart Rose

Fair Lending Compliance: Intelligence and Implications for Credit Risk Management

by Clark R. Abrahams and Mingyuan Zhang

Foreign Currency Financial Reporting from Euros to Yen to Yuan: A Guide to Fundamental Concepts and Practical Applications

by Robert Rowan

Harness Oil and Gas Big Data with Analytics: Optimize Exploration and Production with Data-Driven Models

by Keith Holdaway

Health Analytics: Gaining the Insights to Transform Health Care

by Jason Burke

Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World

by Carlos Andre Reis Pinheiro and Fiona McNeill

Human Capital Analytics: How to Harness the Potential of Your Organization’s Greatest Asset

by Gene Pease, Boyce Byerly, and Jac Fitz-enz

Implement, Improve and Expand Your Statewide Longitudinal Data System: Creating a Culture of Data in Education

by Jamie McQuiggan and Armistead Sapp

Information Revolution: Using the Information Evolution Model to Grow Your Business

by Jim Davis, Gloria J. Miller, and Allan Russell

Killer Analytics: Top 20 Metrics Missing from your Balance Sheet

by Mark Brown

Manufacturing Best Practices: Optimizing Productivity and Product Quality

by Bobby Hull

Marketing Automation: Practical Steps to More Effective Direct Marketing

by Jeff LeSueur

Mastering Organizational Knowledge Flow: How to Make Knowledge Sharing Work

by Frank Leistner

The New Know: Innovation Powered by Analytics

by Thornton May

Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics

by Gary Cokins

Predictive Business Analytics: Forward-Looking Capabilities to Improve Business Performance

by Lawrence Maisel and Gary Cokins

Retail Analytics: The Secret Weapon

by Emmett Cox

Social Network Analysis in Telecommunications

by Carlos Andre Reis Pinheiro

Statistical Thinking: Improving Business Performance, Second Edition

by Roger W. Hoerl and Ronald D. Snee

Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics

by Bill Franks

Too Big to Ignore: The Business Case for Big Data

by Phil Simon

The Value of Business Analytics: Identifying the Path to Profitability

by Evan Stubbs

The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions

by Phil Simon

Using Big Data Analytics: Turning Big Data into Big Money

by Jared Dean

Visual Six Sigma: Making Data Analysis Lean

by Ian Cox, Marie A. Gaudard, Philip J. Ramsey, Mia L. Stephens, and Leo Wright

Win with Advanced Business Analytics: Creating Business Value from Your Data

by Jean Paul Isson and Jesse Harriott

For more information on any of the above titles, please visit www.wiley.com.

Big Data, Big Innovation

Enabling Competitive Differentiation through Business Analytics

Evan Stubbs

Cover image: ©iStockphoto.com/nadla

Cover design: Wiley

Copyright © 2014 by SAS Institute Inc. 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:

Stubbs, Evan.

Big data, big innovation : enabling competitive differentiation through business analytics / Evan Stubbs.

pages cm. — (Wiley & SAS business series)

ISBN 978-1-118-72464-4 (hardback) — ISBN 978-1-118-92553-9 (epdf) — ISBN 978-1-118-92552-2 (epub) — ISBN 978-1-118-91498-4 (obook)

1. Business planning. 2. Strategic planning. 3. Big data.

4. Decision making—Statistical methods. 5. Industrial management—Statistical methods. I. Title.

HD30.28.S784 2014

658.4'013—dc23

2014007690

Preface

Writing is an interesting pursuit; where you start is rarely where you end up. This is my third book and while not originally intended to be a trilogy, things seemed to have panned out that way.

My first book, The Value of Business Analytics, was written for the “doers,” the people responsible for making things happen. It tried to answer the fundamental question people kept asking me: “Why don’t people get this?”

My second book, Delivering Business Analytics, was written for the “designers,” the people responsible for working out how things should happen. It opened the kimono, provided solutions to 24 common organizational problems, and laid the framework to identify and replicate best practices. It tried to answer the next question people kept asking me: “I know what I need to do, but how do I do it?”

This book is written for the “decision makers” and aims to answer the final question: “How do I innovate?”

There are countless models out there. Many are useful, including the ones presented in this book. Most try to make everyone follow the same approach. However, business analytics works best when it’s unique to the organization that leverages it. Differentiation means being different, something that’s all too often overlooked. Rather than just trying to copy, I hope you use the models in this book to create your own source of innovation.

I hope you find as much enjoyment reading this book as I had writing it.

Things move quickly. There’s always more case studies, more disruption, and more examples of how business analytics is fueling innovation. For the latest, keep the conversation going at http://evanstubbs.com/go/blog.

HOW TO READ THIS BOOK

This book introduces eight models:

The

Cultural Imperative

: Covered in Chapter 3, this outlines the five perspectives that support a high-functioning culture.

The

Intelligent Enterprise

: Covered in Chapter 4, this explains how organizations build the capability they need to innovate.

The

Value of Business Analytics

: Covered in Chapter 6, this explains the value that business analytics creates.

The

Wheel of Value

: Covered in Chapter 6, this explains how to get organizations to create value from big data.

The

Path to Profitability

: Covered in Chapter 7, this explains how to blend data science with value creation.

The

SMART Model

: Covered in Chapter 7, this explains how to hire and develop the right people.

The

Value Architect

: Covered in Chapter 7, this explains how to make sure data scientists create value.

The

Innovation Engine

: Covered in Chapter 8, this explains how to support innovation through dynamic value.

Everything else in this book outlines, justifies, and explains the steps necessary to make innovation from big data real. Chapter 8 is written for leaders interested in enabling ability and innovation and is arguably the most important chapter to read.

Due to the nature of the subject matter, this book covers a great deal of ground. To keep the content digestible, much of the detail has been summarized; for those interested in more, I’d strongly recommend reading my prior books, The Value of Business Analytics and Delivering Business Analytics. Where relevant, specific references are provided within the text. Endnotes to further reading are also provided throughout. Rather than a definitive list of reading material, readers should view these as a launching pad from which they can further explore whatever they’re interested in.

This book is divided into four parts. The first highlights a number of current and emerging trends that will continue to dramatically change the face of business. It’s true that things always change; in the famous words of Benjamin Franklin (among others), “In this world nothing can be said to be certain, except death and taxes.” It’s also true, however, that we become so accustomed to change that we run the risk of underestimating the enormous disruption caused by continuous gradual change. If big data is the question, business analytics is the solution. Unfortunately for some, the answer it implies will eventually see entire industries disrupted.

The second part provides a framework through which leaders can understand the challenges they’re likely to face in changing their organization’s culture. It outlines the different perspectives organizations exhibit in moving from unstructured chaos to becoming an intelligent enterprise.

The third part focuses on how to leverage big data to support innovation. This isn’t easy. Innovation is amorphous. Business analytics is complex. Big data is daunting. Together, they can seem insurmountable. Within this part, we review the fundamentals behind success. It spans culture, human capital, organizational structure, technology design, and operating models.

Finally, the fourth part links them all into an integrated operating model that covers ideation, innovation, and commercialization; it gives a starting framework to develop a plan. It highlights the major considerations that need to be made and provides some recommendations to ensure that you “stay the course.”

As with my other books, this one relies heavily on practical examples throughout. Theory is good but where practice and theory contradict, practice grabs theory by the ears and smashes its head into the canvas. While anyone interested in the topic will hopefully find value in the entire book, readers interested in specific topics will benefit from going to specific sections.

Readers interested in understanding the broader impacts of big data along with how organizations tend to cope with disruption are encouraged to read Parts One and Two.

Readers responsible for restructuring organizations to take advantage of business analytics along with hiring and developing the right people are encouraged to read Parts Two and Three.

Finally, readers interested in integrating these building blocks into an operating model that supports innovation will find Part Four especially valuable.

CORE CONCEPTS

This section presents the core vocabulary for everything discussed in this book. It is provided to ensure consistency with my prior two books as well as to provide a quick primer to newcomers. Readers comfortable with the field are encouraged to skip this section.

This book refers repeatedly to a variety of concepts. While the terms and concepts defined in this chapter serve as a useful taxonomy, they should not be read as a comprehensive list of strict definitions. Depending on context and industry, they may go by other names. One of the challenges of a relatively young discipline such as business analytics is that while there’s tremendous potential for innovation, it has yet to develop a standard vocabulary.

Their intent is simply to provide consistency. Terms vary from person to person and while readers may not always agree with the semantics presented here given their own background and context, it’s essential that they understand what is meant within this book by a particular word. Key terms are italicized to try to aid readability.

Business analytics is the use of data-driven insight to generate value. It does so by requiring business relevancy, the use of actionable insight, and performance measurement and value measurement.

This can be contrasted against analytics, the process of generating insight from data. Analytics without business analytics creates no return—it simply answers questions. Within this book, analytics represents a wide spectrum that covers all forms of data-driven insight, including:

Data manipulation

Reporting and business intelligence

Advanced analytics (including data mining, optimization, and forecasting)

Broadly speaking, analytics divides relatively neatly into techniques that help understand what happened and those that help understand:

What will happen

Why it happened

What is the best one could possibly do

Forms of analytics that help provide this greater level of insight are often referred to as advanced analytics.

The final output of business analytics is value of some form, either internal or external. Additionally, this book introduces the concept of dynamic value, the potential of multiple competing points of view to fuel innovation. Internal value is value as seen from the perspective of a team within the organization. Among other things, returns are usually associated with cost reductions, resource efficiencies, or other internally related financial aspects. External value is value as seen from outside the organization. Returns are usually associated with revenue growth, positive outcomes, or other market- and client-related measures.

This value is created through leveraging people, process, data, and technology. Encompassing all of these is culture, the shared values and priorities of an organization. People are the individuals and their skills involved in applying business analytics. Processes are a series of activities linked to achieve an outcome and can be either strongly defined or weakly defined. A strongly defined process has a series of specific steps that is repeatable and can be automated. A weakly defined process, by contrast, is undefined and relies on the ingenuity and skill of the person executing the process to complete it successfully.

Data are quantifiable measures stored and available for analysis. They often include transactional records, customer records, and free-text information such as case notes or reports. Assets are produced as an intermediary step to achieving value. Assets are a general class of items that can be defined, are measurable, and have implicit tangible or intangible value. Among other things, they include documented processes, reports, models, and datamarts. Critically, they are only an asset within this book if they can be automated and can be repeatedly used by individuals other than those who created it.

Assets are developed through having a team apply various competencies. A competency is a particular set of skills that can be applied to solve a variety of different business problems. Examples include the ability to develop predictive models, the ability to create insightful reports, and the ability to operationalize insight through effective use of technology.

Competencies are applied using various tools (often referred to as technology) to generate new assets. Often, tools are consolidated into a common analytical platform, a technology environment that ranges from being spread across multiple desktop PCs right through to a truly enterprise platform.

Analytical platforms, when properly implemented, make a distinction between a discovery environment and an operational environment. The role of the discovery environment is to generate insight. The role of the operational environment, by contrast, is to allow this insight to be applied automatically with strict requirements around reliability, performance, availability, and scalability.

The core concepts of people, process, data, technology, and culture feature heavily in this book; while they are a heavily used and abused framework, they represent the core of systems design. Business analytics is primarily about facilitating change; business analytics is nothing without driving towards better outcomes. And, when it comes to driving change, every roadmap involves having an impact across these four dimensions. While this book isn’t explicitly written to fit with this framework, it relies heavily on it.

Readers interested in knowing more are heavily encouraged to read The Value of Business Analytics and Delivering Business Analytics.

Acknowledgments

There were many who provided valuable input and feedback throughout my writing, far too many to acknowledge exhaustively. Their advice was excellent and any mistakes contained inside these pages are solely mine. I would especially like to thank Philip Reschke, Chami Akmeemana, Vicki Batten, Lynette Clunies-Ross, Dorothy Adams, Greg Wood, and Renée Nocker.

Most important of all, I’d like to thank my family. Without their patience, support, and constant caring this would have been impossible. I promise this is the last one—for now.

Part OneMay You Live in Interesting Times

The Chinese have an idiom. Loosely translated, it says that it’s better to be a dog in a peaceful time than a man in a chaotic time. There’s also a related curse, also often attributed to the Chinese: “May you live in interesting times.”

This, in a snapshot, is our world. Our time is one where drones can assassinate someone half-way around the globe, controlled by people on a TV screen from the safety of their own suburb. This is a time where a tiny failed bank in Greece can potentially bring the entire global financial system to a screeching halt, bankrupting nations. It is a time where one can carry the entire Library of Congress on a chip smaller than one’s fingernail and still have storage to spare. And it is a time where cars drive themselves, glasses contain computers, and 3D printers can create duplicates of themselves.

We live in interesting times. And, interesting times call for interesting leaders.

Chapter 1Lead or Get Out of the Way

The greatest leaders are as much a product of their time as they are a reflection of their skill. Without Hitler, what would we remember of Churchill? Without Xerxes, the legend of the 300 Spartans led by Leonidas would never have happened. Without the right context, even those with the greatest potential remain part of the peanut gallery, shouting epitaphs at those who wear the limelight.

It’s in times of crisis that leaders emerge—times of change, times like the present.

THE FUTURE IS NOW

Our world is a fascinating one; we’re at an inflection point, one defined by big data and business analytics. What was once science fiction is becoming reality. Let’s be frank though—that sounds pretty hackneyed. After all, hasn’t everything been science fiction once?

This is true. It’s also true, however, that science fiction is a deep well to draw from. A well where some ideas are so fantastical that it seems impossible that they’ll ever become reality. Asimov, a science fiction writer, for example, wrote speculatively of “psychohistory” in his Foundation series.1 A form of mathematical sociology, scientists would use massive amounts of behavioral information to predict the future. Through doing so, they were able to foresee the rise and fall of empires thousands of years in advance.

As with all good stories, power always comes with constraints. Accurate predictions were only possible given two conditions. First, the population whose behaviors were to be modeled needed to be sufficiently large—too small, and the predictions would become error-prone. Second, the population being modeled could not know it was being modeled. After all, people might change what they were doing if they knew they were being watched.

It seems fantastical, doesn’t it? Still, this is fundamentally the promise of big data. We know more about the world than ever before. Many of those being watched are still unaware of how much things have changed. Between national intelligence, security leaks, and the potential of metadata, most of us are only just realizing how much information is out there. And, by analyzing that data, we have the power to predict the future in ways that people still can’t believe. Amazon, for example, took out a patent in late 2013 on a process to ship your goods before you’ve ordered them.2 Big data offers unparalleled insights and predictive abilities, but only to those who know how to leverage it. For most, getting value from big data is a challenge. However, the reflection of every challenge is opportunity.

Things have changed. And, it’s a rare leader who isn’t aware he or she needs a plan to realize this opportunity. However, there’s a twist. It’s not just a good idea. It’s not something that’s going to happen. It’s happening now.

Catalyzed by books such as Thinking, Fast and Slow3 and Nudge,4 behavioral economics is already blending data with heuristics and psychology to create new models to describe and influence consumer behavior. Recognizing the power of a scientific approach to analyzing information, the U.K. government established a dedicated Behavioral Insights team to take advantage of these ideas. Formed in 2010 and nicknamed the “nudge unit,” their goal was to blend quantitative and qualitative techniques to improve policy design and delivery.5

The model has proved to be a popular one. In late 2012, the Behavioral Insights Team went global through partnership with the government of New South Wales in Australia. In mid-2013, the Obama administration appointed Yale graduate Maya Shankar to create a similar task force.

Paul Krugman, winner of the Nobel Memorial Prize for Economic Sciences, credits Asimov’s vision of a mathematical sociology as inspiring him to enter economics.6 This vision of a future shaped by our ability to analyze information is becoming real. And, it’s changing the face of medicine, policy, and business. Thanks to constantly increasing analytical horsepower and falling storage costs, the cost of sequencing the genome has dropped from US$100 million in 2001 to just over US$8,000 in 2013.7 More than just being cheaper, every decline in sequencing costs puts us that much closer to truly personalized medicine.

Even the social web is sparking innovation. Facebook’s acquisition of Oculus, Instagram, and Whatsapp wasn’t just an attempt to diversify. It was a deliberate attempt to stay engaged across all channels all the time. With over a billion people now on Facebook, it’s amazing what one can find by scanning personal interactions. Organizations like the United Nations (UN) are tracking disease and unemployment in real time through the large-scale analysis of social media.8 The Advanced Computing Center at the University of Vermont is using tens of millions of geolocated tweets in its Hedonometer project to map happiness levels in cities across the United States.9

The future is closer than it’s ever been. Taking the leap to Asimov’s psychohistory isn’t as far-fetched as it once might have seemed.

THE SECRET IS LEADERSHIP

It’s hard to ignore the potential of big data. Realizing it, though, that’s tricky. For every successful project there’s a mountain of failed projects. Few in the field have escaped completely unscathed. Anyone who says she has probably hasn’t been trying hard enough.

If you’re reading this book, it’s a fair assumption that you’re interested in linking big data to innovation. The cornerstone to this is business analytics. Big data and business analytics go together hand in glove. Without data, there can be no analysis. And without business analytics, big data is just noise. Together, they offer the potential for innovation. Innovation, however, requires change, and change is impossible without leadership.

Without value, all of this is meaningless. Big data has the potential to make things more efficient. It can generate returns. It might simply answer “the hard questions” that no one knows the solution to. Some of these benefits lead to internal value, such as productivity. Others lead to external value, such as revenue. Still others can lead to total reinvention through dynamic change. Not all of these are complementary. Because of this, harnessing the full potential of big data involves walking the tightrope between the dynamism of change and the stability of continuous improvement.

The secret behind success is leadership. Without it, it’s impossible to balance the opportunity for reinvention with the benefits of continual improvement. A strong leader can do more with access to limited capability than the best team can without a leader.

We don’t yet know the final impact of big data and business analytics. We do know, however, that it will change things. Change in itself isn’t new; we already live in a world where change has become so normal that it’s almost invisible. However, for reasons that are covered in the next chapter, big data is “bigger” than this. It’s likely to cause large-scale industrial and social disruption not seen since the industrial revolution, not because of what it is but because of what it represents.

Our future may be one where the economy only requires a tenth of the current workforce. Guided by the use of operational analytics and intelligent algorithms, it might lead to large-scale social unrest due to chronic unemployment and wealth centralization. It may be one where privacy becomes meaningless and the most personal aspects of our lives become public property. It may be one where precrime, the ability to predict crimes before they occur, becomes a reality.10

These may seem absurd, but, they’re already happening. Through automating analytics, some organizations are able to achieve orders of magnitude of higher levels of productivity than their peers. The impact this will have on the labor market is unclear. Katz, a Harvard economist, suggests that even though there’s no precedent for a structural change in the demand for jobs, today’s digital technologies present many unanswered questions.11 Historically, technological innovation has almost always led to greater long-run employment. Thanks to the potential of intelligent systems, the biggest question is this: Will the future reflect the past? It’s possible, as far-fetched as it might sound, that the entire middle-skilled strata of the labor market may simply become unemployable.12

The division between the “haves” and “have-nots” continues to grow. Sharing selfies and personal details has become the norm on SnapChat, Facebook, and a multitude of other social media sites. Through analyzing interests, social networks, and behavioral patterns, organizations such as Google, LinkedIn, and Facebook have become experts in guessing who you might know. And, some justice departments are already experimenting with predictive analytics to better understand the likelihood of recidivism for offenses such as driving under the influence or domestic violence.

The world doesn’t need custodians to navigate this period of rapid change. It needs leaders—people with the confidence, vision, and ability to redefine their world. Whether it’s for profit or for the common good, the future is business analytics.

NOTES

1

. Isaac Asimov,

Foundation

(Garden City, NY: Doubleday, 1951).

2

. U.S. Patent #8,615,473 B2.

3

. Daniel Kahneman,

Thinking, Fast and Slow

(New York: Farrar, Straus & Giroux, 2011).

4

. Richard H. Thaler and Cass R. Sunstein,

Nudge: Improving Decisions about Health, Wealth, and Happiness

(New Haven, CT: Yale University Press, 2008).

5

. Cabinet Office, “Behavioural Insights Team,”

www.gov.uk/government/organisations/behavioural-insights-team

(accessed Jan. 11, 2014).

6

. Paul Krugman, “Paul Krugman: Asimov’s Foundation Novels Grounded My Economics,”

Guardian News and Media

, Dec. 4, 2012,

www.theguardian.com/books/2012/dec/04/paul-krugman-asimov-economics

(accessed Jan. 11, 2014).

7

. National Human Genome Research Institute, “DNA Sequencing Costs,”

www.genome.gov/sequencingcosts

(accessed Jan. 11, 2014).

8

. United Nations Global Pulse,

www.unglobalpulse.org

(accessed Jan. 11, 2014).

9

.

Hedonometer

, “Daily Happiness Averages for Twitter, September 2008 to Present,”

www.hedonometer.org/index.html

(accessed Jan. 11, 2014).

10

. Philip K. Dick,

The Minority Report

(New York: Pantheon, 2002).

11

. David Rotman, “How Technology Is Destroying Jobs,”

MIT Technology Review

, Jun. 12, 2013,

www.technologyreview.com/featuredstory/515926/how-technology-is-destroying-jobs

(accessed Mar. 27, 2014).

12

. “The Onrushing Wave,”

Economist

(Jan. 18, 2014),

www.economist.com/news/briefing/21594264-previous-technological-innovation-has-always-delivered-more-long-run-employment-not-less

(accessed Mar. 27, 2014).

Chapter 2Disruption as a Way of Life

Talk of psychohistory and precrime might seem better suited to a science fiction convention than an executive briefing. However, the more our world changes, the more we need to question our assumptions. And, therein lies the trap—we’ve become so accustomed to change that we don’t even realize that it’s happening any more.

There’s an apocryphal parable about a frog in boiling water. While not true, it suggests that a frog’s nervous system is sufficiently underdeveloped and that when it’s put in cold water and the water is slowly heated, the frog won’t know it’s in danger until it’s boiled alive. Apart from being pretty cruel to the frog, it carries another message. We, collectively, are that frog.

Our world has changed. It’s changing at such an accelerating rate that we’ve lost track of the speed. Perception is relative; at walking speed, someone running past us seems swift. On a highway, someone overtaking us seems fairly lethargic. To the runner, though, the two cars are terrifyingly fast.

Alvin Toffler, one of the world’s most famous futurologists, coined the term “future shock” in 1970.1 In his book Future Shock he argued that too much change in too short a period of time would lead to shattering stress and disorientation. This would create a society characterized by social paralysis and personal disconnection. The rate of change he predicted has come to pass. However, he got the impact backward.

We, as a society, have looked change in the face and laughed. What’s fantastical one year is commonplace the next. In some cases, even within months; how many times in the last year have you found a device or application you couldn’t live without only to have it become such a central part of your life that you don’t even realize it’s there anymore?

There’s danger in this complacency. Just because we’re used to the water getting warmer, it doesn’t mean that we’re out of danger. The rest of this chapter will review five key trends that will fundamentally change the way we view the world over the next decade. These are:

The Age of Uncertainty

The Emergence of Big Data

The Rise of the Rōnin

The Knowledge Rush

Systematized Chaos

Again, this isn’t futurism; they are all already happening. Thus far, their impacts are still relatively small. With advance knowledge, a competent leader still has time to take advantage of them.

THE AGE OF UNCERTAINTY

Change will continue to accelerate and the resulting social complexity and economic interconnectedness will increase the frequency of unintended consequences and unexpected events. Dynamic management focused on emphasizing robustness rather than pure efficiency will become common. Leaders will need to become comfortable with uncertainty, planning for “unknown unknowns,” and trust sophisticated monitoring engines that leverage big data.

Ours is a magical time. Every day, we do things that would have been in realms of science fiction not even three decades ago. Twenty years ago, an international telephone call from New York to London cost approximately a dollar a minute.2 Today, we can videoconference for free on a device that fits in our pocket. The iPhone 5s, a high-specification mobile phone released in 2013, is faster than the MacBook Pro released in 2008, a high-end laptop. In less than five years, we’ve created a device that’s smaller, faster, has greater fidelity, offers mobile connectivity, and has over double the battery life.3

Over 23 years ago, Star Trek fantasized about the Personal Access Display Device, a hand-held computer with a touch-screen interface. In 2010, Apple launched the iPad, making Star Trek’s PADDs real and affordable. In isolation, that’s mind-blowing. However, the most fascinating thing about them is that in less than three years from when they were launched, the tablet as a personal computing device was taken for granted and largely commonplace.

The examples are endless. Toys can be shipped and delivered almost overnight from China that quite literally have millions of times more processing power than Apollo 11. Three-dimensional printers are commercially available and consumer friendly. Not only are electric cars such as the Tesla commercially available but Google is road-testing driverless cars. Facebook and Sony are developing commercially viable virtual reality systems. While we’re still waiting for our flying cars, the world’s closer to the future than ever before.

Communication and information is instantaneous, pervasive, and always-on; no matter where we are, we’re plugged in. To a kid, the idea of being involuntarily unplugged is almost inconceivable. With fourth-generation mobile connectivity and portable solar rechargers, even camping no longer offers an escape! The scale of this change is subtle; it sneaks up on you. Given enough exposure, even magic becomes mundane. Therein lies the danger.

The world is changing around us at an accelerating rate. As it does so, it changes us, for good or bad. Much like the industrial revolution, it’s not clear yet how this technology will impact society. Thus far, we know that it offers social and professional advantages to those who have it and know how to use it. And, quantitative analysis has shown that access and use of information technology is dependent on income and access to education.4 This carries with it a stark implication: access (or lack thereof) to information runs the risk of creating an entire social strata of “haves” and “have-nots.”

We live in a world where social, cultural and economic capital is dependent on one’s ability to connect, communicate, and create through technology. In this world, lacking these skills can create a true digital divide, one that has intergenerational implications. As change accelerates, it becomes that much harder for the disadvantaged to keep up.

While this is clearly a global concern, its implications also fall closer to home. The 2011 U.S. Census showed that only 71.7 percent of households accessed the Internet. While not terribly concerning in isolation, what is concerning is the lowest usage rates clustered around the less educated and those with low incomes.5 It’s a measure of the role that technology plays in our lives that some argue that this digital divide is a threat not only to economic mobility and social stability but even democratic representation.6

At the micro-level, information is power, both for the individual and the collective. It gives us the ability to network and connect with lost friends. However, it’s more than that. The ability to connect and communicate has already supported revolutions in Egypt, Tunisia, and Libya.7 What affects the individual has also had an effect on the organization. Globalization is easier than it’s ever been and location is rarely a barrier to business. At the macro-level, that same decline in communication costs has affected global trading patterns and competitive price advantage, especially in the case of differentiated products.8