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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: SAS Institute Inc
- Sprache: Englisch
AVOID THE MISTAKES THAT OTHERS MAKE - LEARN WHAT LEADS TO BEST PRACTICE AND KICKSTART SUCCESS This groundbreaking resource provides comprehensive coverage across all aspects of business analytics, presenting proven management guidelines to drive sustainable differentiation. Through a rich set of case studies, author Evan Stubbs reviews solutions and examples to over twenty common problems spanning managing analytics assets and information, leveraging technology, nurturing skills, and defining processes. Delivering Business Analytics also outlines the Data Scientist's Code, fifteen principles that when followed ensure constant movement towards effective practice. Practical advice is offered for addressing various analytics issues; the advantages and disadvantages of each issue's solution; and how these solutions can optimally create organizational value. With an emphasis on real-world examples and pragmatic advice throughout, Delivering Business Analytics provides a reference guide on: * The economic principles behind how business analytics leads to competitive differentiation * The elements which define best practice * The Data Scientist's Code, fifteen management principles that when followed help teams move towards best practice * Practical solutions and frequent missteps to twenty-four common problems across people and process, systems and assets, and data and decision-making Drawing on the successes and failures of countless organizations, author Evan Stubbs provides a densely packed practical reference on how to increase the odds of success in designing business analytics systems and managing teams of data scientists. Uncover what constitutes best practice in business analytics and start achieving it with Delivering Business Analytics.
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Veröffentlichungsjahr: 2013
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Contents
Preface
Acknowledgments
Part One: Business Analytics Best Practices
Chapter 1: Business Analytics: A Definition
What Is Business Analytics?
Core Concepts and Definitions
Note
Chapter 2: The Competitive Advantage of Business Analytics
Advantages of Business Analytics
Challenges of Business Analytics
Establishing Best Practices
Notes
Part Two: The Data Scientist’s Code
Chapter 3: Designing the Approach
Think about Competencies, Not Functions
Drive Outcomes, Not Insight
Automate Everything Non-Value-Added
Start Flexible, Become Structured
Eliminate Bottlenecks
Notes
Chapter 4: Creating Assets
Design Your Platform for Use, Not Purity
Always Have a Plan B
Know What You Are Worth
Own Your Intellectual Property
Minimize Custom Development
Chapter 5: Managing Information and Making Decisions
Understand Your Data
It’s Better to Have Too Much Data Than Too Little
Keep Things Simple
Function Should Dictate Form
Watch the Dynamic, Not Just the Static
Note
Part Three: Practical Solutions: People and Process
Chapter 6: Driving Operational Outcomes
Augmenting Operational Systems
Breaking Bottlenecks
Optimizing Monitoring Processes
Encouraging Innovation
Notes
Chapter 7: Analytical Process Management
Coping with Information Overload
Keeping Everyone Aligned
Allocating Responsibilities
Opening the Platform
Part Four: Practical Solutions: Systems and Assets
Chapter 8: Computational Architectures
Moving Beyond the Spreadsheet
Scaling Past the PC
Staying Mobile and Connected
Smoothing Growth with the Cloud
Notes
Chapter 9: Asset Management
Moving to Operational Analytics
Measuring Value
Measuring Performance
Measuring Effort
Note
Part Five: Practical Solutions: Data and Decision Making
Chapter 10: Information Management
Creating the Data Architecture
Understanding the Data Value Chain
Creating Data-Management Processes
Capturing the Right Data
Notes
Chapter 11: Decision-Making Structures
Linking Analytics to Value
Reducing Time to Recommendation
Enabling Real-Time Scoring
Blending Rules with Models
Appendix: The Cheat Sheets
Glossary
Further Reading
About the Author
Index
WILEY AND SAS BUSINESS SERIES
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Copyright © 2013 by SAS Institute Inc. All rights reserved.
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Published simultaneously in Canada.
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Library of Congress Cataloging-in-Publication Data:
Stubbs, Evan.
Delivering business analytics : practical guidelines for best practice / Evan Stubbs.
p. cm. — (Wiley & SAS business series)
Includes bibliographical references and index.
ISBN 978-1-118-37056-8 (cloth); ISBN 978-1-118-55954-3 (ebk);
ISBN 978-1-118-55945-1 (ebk); ISBN 978-1-118-55944-4 (ebk)
1. Business planning. 2. Strategic planning. 3. Decision making. I. Title.
HD30.28.S785 2013
658.4′013—dc23
2012041500
To the dreamers, the innovators, the visionaries, and those who know things could be better—this book’s for you.
Preface
I had an interesting conversation during a book signing for my previous book, The Value of Business Analytics. I’d just come off a fairly intense trip that included talking to hundreds of executives about how they could drive better value through the use of business analytics. Over drinks after one event, I ended up talking to a friendly fellow who, rather apologetically it must be said, seemed like he had something to share.
It took a while, but after enough prompting he eventually built the courage to tell me what was on his mind. Paraphrasing, it was that “I liked your book but it didn’t seem like you’d explained anything I didn’t already know. . . . ”
Of all the possible reactions he was expecting, I think laughter had to be pretty far down the list! Much to his surprise, I wholeheartedly agreed with him; objectively, I don’t think anyone would disagree with anything I’ve written. Pragmatically, I think much of it is self-obvious. What fascinates me more than anything else is that so few teams actually do what’s apparently self-obvious. If everyone did, business analytics wouldn’t be seen as an arcane, confusing, and arguably mystical discipline.
Arthur C. Clarke once said that any piece of sufficiently advanced technology is indistinguishable from magic. Like it or not, that’s where we sit with business analytics; those who haven’t been skilled in the secret ways by past masters are left at a significant disadvantage.
The Value of Business Analytics was written for a general audience. It tried to help the doers make their lives easier by explaining how and why organizations fail to get behind business analytics. Drawing from a wide variety of case studies and research, it highlighted the four major characteristics of successful business analytics teams. The more I dealt with effective teams, the more I saw that they were unique.
They were exceedingly good at defining the value they were going to create. They paid specific attention to communicating that value to the broader organization. They understood the importance of delivering to a strategic road map, and possibly most importantly, they measured the value they created. I wrote the book because I felt that, although there’s an increasing library of books talking about business analytics, there are relatively few that provide practical, pragmatic advice on how to succeed on a day-to-day basis.
This book is more technical than my last; it drives into the detail. This is deliberate—where my last book was written for the doers, this book is written for the designers. I have made every effort to validate the approaches described in this book with a variety of knowledgeable practitioners across different industries, different sized organizations, and different geographies. All their input has been invaluable and any oversights in this book are solely mine. Some may disagree with what I’ve written. However, debate is good; if we are to develop as a discipline, the best place to start is with best practice.
This book aims to give data scientists and architects the ammunition they need to succeed. The biggest disservice the profession does for itself is hoarding information; many apparently challenging problems have already been solved, repeatedly in many cases. Unfortunately, finding these solutions is nearly impossible; although there are a multitude of books that deal with the specifics of strategy, data mining, business intelligence, or other highly focused areas of interest, there are few if any that try to establish a core toolkit from which common problems can be solved.
That, in a nutshell, is the focus of this book. It describes a variety of solutions to common problems, focusing specifically on those involving assets, information, technology, skills, and processes. Although it avoids referring to specific technologies, it drives into enough detail to help an architect establish a core requirements list. Although it doesn’t provide a series of role definitions, it outlines sufficient detail to help a newly appointed chief data scientist or chief analytics officer drive the greatest value out of the organization’s investment in business analytics. Finally, although there isn’t a single piece of code through this whole book, it will still help analysts understand how they can better design their applications to support enterprise-level execution.
WHY YOU NEED TO READ THIS BOOK
Why do you need to read this book? For a few simple reasons: Hopefully, it’ll give you the edge you need. It’ll almost definitely make your job easier. It should start you thinking about what does and doesn’t work in your specific context.
At its core, business analytics is about transformation and change. It’s about making more with less, creating insight from seemingly impenetrable information, and outperforming everyone else. Succeeding in business analytics will help you drive competitive differentiation as well as your own career. We live in a digital age, and those that know how best to take advantage of it will have the greatest success.
The thing is, we often don’t know what we don’t know. Knowing what’s possible is usually half the battle—theory helps provide some insight but a few hours’ worth of guided discovery can eliminate years of experience fueled by first principles. Practice makes it real. To make the examples as real as possible, this book emphasizes case studies wherever possible.
Every book needs at least one reason to exist. This book’s reasons are to explain why:
By reading this book, you’ll understand the logic behind each of these statements and discover ways to help simplify execution in a variety of situations. It distills decades of practical experience with business analytics into a small set of best practices, management principles, and general solutions.
These haven’t been defined arbitrarily—they were identified after repeatedly seeing some teams succeed and many fail. By reading this book, you’ll build an understanding of how business analytics drives competitive advantage. You’ll understand why some projects are more successful than others. And you’ll learn how to go about managing the analytically focused technology and data that will underpin your organization’s use of business analytics.
These guidelines and principles help establish the right philosophies and management patterns. What they can’t do, however, is tell you how to do it. To bridge the gap between theory and practice, this book provides practical solutions to common problems across:
People and process
, or how to go about solving problems related to developing competencies and creating reusable processes.
Systems and assets
, or how to go about managing assets and designing and managing technology architectures.
Data and decision making
, or how to go about capturing and managing data as well as how to improve operational decision making.
These principles haven’t appeared overnight—they are the result of extensive work with a variety of organizations ranging in size from thousands of employees to businesses with less than a hundred. This book draws from the successes and failures of these organizations and synthesizes them into a basic framework from which managers, designers, and architects can draw.
Some of these organizations have been using business analytics to do everything from advanced risk simulation to viral marketing. Others have been more interested in simply establishing a common platform from which they can start eliminating inefficient spreadmarts.1 Although their use and execution has varied, the logic behind their successes (and failures) hasn’t.
If you already know everything there is to know about leveraging business analytics, this book will serve as a way of communicating and validating your hard-earned experience. It will solidify most of what you already know into a series of principles you can communicate and foster. If you are studying the field as part of an undergraduate or postgraduate course, this book will help give you some practical insights into how business analytics is applied. If you are entering the field for the first time or are interested in moving beyond creating insight into driving change, this book will help provide a series of field-tested patterns to build on.
HOW TO USE THIS BOOK
The book is divided into five parts:
In the main, this book lacks narrative. Each section has been written such that it can be picked up and read independently from the rest of the book. This is very deliberate; rather than being written to be read cover to cover, this book was written to provide specific answers to specific questions.
The main benefit of this approach is that readers are free to consult this book as a reference, dipping in and out as items of interest catch their eye. Although the book structure offers a consistent internal flow and it can be read from beginning to end, there’s nothing that forces a reader to do so. Instead of a book that’s read once and left on the bookshelf, this book is intended to assist as a constant help, kept close by managers and analysts for reference and guidance. The breadth of content is such that everyone from newly created to highly mature teams should find something of interest between these covers.
Because of this, different sections contain an inevitable degree of repetition. When reviewing management guidelines, it would be impossible to emphasize the importance of retaining one’s intellectual property without referencing the risks and benefits of engaging an external consultant. It would be equally impossible to talk about the specifics of how to best engage external parties without at least referencing the reasons behind the recommendations.
Although it would have been possible to condense material into fewer topics with longer chapters, this would have had the undesirable effect of making the topics less “chunkable.” We don’t always have enough time for a full meal; sometimes all we need to keep going is a snack. To help readers understand these interdependencies, each section includes a list of related sections.
Part One, Business Analytics Best Practices, provides the major premise of this book: that best practice in business analytics stems from favoring approaches that emphasize the microeconomic advantages of business analytics. It provides the core vocabulary used throughout and outlines the link between business analytics and competitive advantage.
Part Two, The Data Scientist’s Code, provides a variety of principles to keep in mind that, if followed, help data scientists move toward best practice. These are general philosophies rather than specific answers—they represent some ideas and concepts that, if kept in mind, help make it easier to find a better path.
The last three parts provide a number of solutions to specific problems. All of these map into the general guidelines in some way. However, they don’t cover every possible solution—if the guidelines define the boundaries of the sets, the solutions fall within those sets. These solutions explain why some projects are not as successful as they could be, and they provide specific recommendations on how to go about improving day-to-day planning, design, and management.
As much as possible, the reasons behind these solutions are explained using case studies. These are based on real examples, albeit modified to protect the identity of the organizations in question. Although they sometimes provide positive examples, many of them aim to highlight how poor design decisions can limit effectiveness. While it’s possible that some readers may feel like they refer to their organization specifically, I sincerely hope they don’t!
These solutions focus on how organizations can avoid commonly faced problems in the practical application of business analytics. They provide a starting point for managers, architects, and practitioners to design their business analytics systems and solutions. Equally though, they should not be treated as a limited set of options; innovation comes from attempting the impossible and succeeding. Although the approaches described in this book represent a good snapshot of the most successful approaches used by organizations today and range from being simple to highly complex, they’re not an exhaustive list and should not be read as such.
To make it easier to interpret and apply these solutions, each one follows the same broad structure:
An overview of why it’s worth reading the section.
The background that leads to the issue.
A description of the pains and symptoms stemming from the issue.
A list of common solutions that, while logical, tend to lead to more problems.
A general solution that aligns with the best-practice principles described in this book along with associated benefits and limitations.
Each solution starts with an overview of the problem it solves and when it can be applied. Specific exclusions or warning are noted. The specific context and forces that define the situation are then explained, along with a practical example of how this context can emerge.
This context creates a problem with corresponding challenges and symptoms. The solution is then outlined, along with the benefits that stem from applying the solution as well as important limitations to the solution.
Readers are recommended to use the solutions described in this book to expand their awareness of what their peers are doing, to consider alternative approaches, and to develop a fuller understanding of the very practical advantages and disadvantages of different designs. Readers are also recommended to design their solutions based on the best-practice principles identified within this book unless, of course, it would mean sacrificing value or renewable return for the sake of complying with a series of bullet points. They help, but there’s always more than one way to solve a problem.
Part Three, People and Process, deals with the challenges associated with finding the right people, developing the right competencies, and driving effective use of technology at a resource level. Rather than focus on cultural considerations, this part focuses on structural considerations including techniques to assist with team retention, how best to augment existing enterprise resource planning (ERP) processes with analytical insight, how to treat intellectual assets as the value-creation property they are, and how to structure technology and teams to drive efficiency and avoid bottlenecks. Readers interested in the people management and process aspects of business analytics will find a great deal of valuable information in this part.
Part Four, Systems and Assets, deals with technology and asset related aspects of business analytics. It focuses on solutions associated with information processing, computation, and systems management. It covers the importance of establishing the right architectural principles in platform design, the value chains needed to support effective execution, considerations in technology selection, and the various technology management options available. Readers interested in understanding the implications of leveraging desktop computing, server-based processing, or cloud computing will be especially interested in this part.
Part Five, Data and Decision Making, deals with information management aspects of business analytics, covering both data storage and generating actionable insight. Key considerations covered within this part include the benefits and limitations of tightly coupling with the enterprise data warehouse, how the time available to make a decision influences design, and how to manage the often conflicting dynamic of analytical measures against business rules. It examines both how information storage can be managed as well as how to execute analytically based decisions in an operational context. Readers interested in designing operational systems will find this part highly informative.
NOTE
1. The painful situation in which spreadsheets are used not to visualize data but to store permanent data.
Acknowledgments
This book would have been impossible without the deep support that so many people provided. Thanks are due to Bill Franks, David Hardoon, and Felix Liao for providing their tremendously valuable insight and experience. As is his wont, Peter Kokinakos managed to clarify what was a rather clumsy structure into something that was tighter, punchier, and more persuasive. Thanks must go to Brendon Smyth whose advice fundamentally changed the book’s structure for the better at the eleventh hour.
There are a few people who went far above the call of duty to help me during my editing process. Despite being given a very rough early draft, Marnie MacDonald battled through what was in reality a very fragmented structure to give valuable feedback. Thanks to a shared taxi after a rather serendipitous conference, Warwick Graco somehow managed to tear through one of my late drafts, sparking tremendously valuable thoughts in the process. Neil Fraser was a constant support, providing very detailed feedback and suggestions for further research, without which this book would have greatly suffered.
On a more personal level, I’d also like to thank my sister Cassie who somehow managed to review copy while also getting married, making her own wedding rings, renovating her house, and looking after a rolling progression of friendly dogs. Your feedback helped with this book and the next.
More than anyone else, I’d like to thank my wife Vanessa for her absolutely tireless reading and rereading of multiple drafts, even when they were largely incomprehensible messes of unstructured discourse. Without her help and support, this book would have literally been impossible. I’d also like to thank two of my most important advisors: my daughter Amélie, who kept me company and kept me going in my study when the deadline was fast approaching, and my son Calvin, whose laughter lit my life, even when I was exhausted. I love you all.
Part One
Business Analytics Best Practices
We don’t know what we don’t know. This creates an interesting dynamic. We can accept the way things are as a given, and by doing so doom ourselves to mediocrity. Or we can experiment, hopefully innovate, and grasp new opportunities. Unfortunately, innovation requires taking chances. For many organizations, taking that first leap into business analytics is already seen as being risky enough.
Every day, we push the boundaries of what’s possible. Facebook has over a billion customers. A billion! Every day, the limits of what we don’t know decrease. Every problem we solve frees us to focus on the next, harder problem. In aggregate, we’re making faster progress than we’ve ever made since the first caveman decided to try and predict where his next hunt should take place.
The paradox is, of course, that these advancements aren’t shared. For most of us, our individual awareness of how to drive innovation through business analytics has lagged tremendously compared to the industry leaders. This shouldn’t be surprising—that same knowledge is justifiably seen as a competitive advantage to the organizations that generate it. So despite the general benefit that would come from sharing information, important insights and understandings remain hidden.
The basics aren’t hard. Successfully leveraging business analytics for competitive advantage requires understanding how to generate insight, how to manage information, and how to action that insight. The real secret is that business analytics isn’t about insight; it’s about change. And that makes all the difference.
Business analytics is about doing things differently; it’s about using information to test new approaches and drive better results. The challenging thing is that this does actually mean things need to change. Insight is great, but when we use the same management approaches we always have, we usually end up with the same result.
Ignoring the need to develop new competencies in our people generally leads to unchanged outcomes. Trying to shoehorn traditional data warehousing models into a business analytics context limits the insight that’s possible, not because the fundamentals of warehousing are wrong but because the discipline and objectives are different. They are related, but different, and running the same process will inevitably lead to the same outcome.
As a professional discipline, this holds us back. There remains a lack of clarity around how to solve what are, more often than not, common problems. We repeatedly reinvent the wheel, wasting valuable time, resources, and money, and there’s no good reason for it. Although it’s true that organizations that cannot overcome the simplest hurdles are held at a disadvantage compared to their peers, we do ourselves a disservice by not developing a broader industry maturity.
We learn from knowing what’s possible. We innovate by trying to overcome the impossible. Without knowing what other people are doing, we usually fail to do either.
This book attempts to fill that gap by sharing what others have already learned. To the first-time reader, some of it may seem obvious, some of it novel. Critically though, what’s seen as obvious varies from person to person; to someone who’s an experienced retailer but has never managed a business analytics project, even the simplest things can be surprising. On the other hand, to someone who’s completely wedded to retaining control over all aspects of information management, the way others are using cloud computing and leveraging external resources may be surprising.
Drawing from extensive experience and numerous real-world applications, this book distills a wide variety of successful behaviors into a small number of highly practical approaches and general guidelines. I hope that everyone, regardless of how experienced they are, will discover some novel and useful ideas within the covers of this book. By knowing what’s possible, we increase the odds of success.
Chapter 1
Business Analytics: A Definition
Before we define the guidelines that establish best practice, it’s important to spend a bit of time defining business analytics and why it’s different from pure analytics or advanced analytics.1
WHAT IS BUSINESS ANALYTICS?
The cornerstone of business analytics is pure analytics. Although it is a very broad definition, analytics can be considered any data-driven process that provides insight. It may report on historical information or it may provide predictions about future events; the end goal of analytics is to add value through insight and turn data into information.
Common examples of analytics include:
Reporting: The summarization of historical data
Trending: The identification of underlying patterns in time-series data
Segmentation: The identification of similarities within data
Predictive modeling: The prediction of future events using historical data
Each of these use cases has a number of common characteristics:
They are based on data (as opposed to opinion).
They apply various mathematical techniques to transform and summarize raw data.
They add value to the original data and transform it into knowledge.
Activities such as business intelligence, reporting, and performance management tend to focus on what happened—that is, they analyze and present historical information.
Advanced analytics, on the other hand, aims to understand why things are happening and predict what will happen. The distinguishing characteristic between advanced analytics and reporting is the use of higher-order statistical and mathematical techniques such as:
Operations research
Parametric or nonparametric statistics
Multivariate analysis
Algorithmically based predictive models (such as decision trees, gradient boosting, regressions, or transfer functions)
Business analytics leverages all forms of analytics to achieve business outcomes. It seems a small difference but it’s an important one—business analytics adds to analytics by requiring:
Business relevancy
Actionable insight
Performance measurement and value measurement
There’s a great deal of knowledge that can be created by applying various forms of analytics. Business analytics, however, makes a distinction between relevant knowledge and irrelevant knowledge. A significant part of business analytics is identifying the insights that would be valuable (in a real and measurable way), given the business’ strategic and tactical objectives. If analytics is often about finding interesting things in large amounts of data, business analytics is about making sure that this information has contextual relevancy and delivers real value.
Once created, this knowledge must be acted on if value is to be created. Whereas analytics focuses primarily on the creation of the insight and not necessarily on what should be done with the insight once created, business analytics recognizes that creating the insight is only one small step in a larger value chain. Equally important (if not more important) is that the insight be used to realize the value.
This operational and actionable point of view can create substantially different outcomes when compared to applying pure analytics. If only the insight is considered in isolation, it’s quite easy to develop a series of outcomes that cannot be executed within the broader organizational context. For example, a series of models may be developed that, although extremely accurate, may be impossible to integrate into the organization’s operational systems. If the tools that created the models aren’t compatible with the organization’s inventory management systems, customer-relationships management systems, or other operational systems, the value of the insight may be high but the realized value negligible.
By approaching the same problem from a business analytics perspective, the same organization may be willing to sacrifice model accuracy for ease of execution, ensuring that economic value is delivered, even though the models may not have as high a standard as they otherwise could have. A model that is 80 percent accurate but can be acted on creates far more value than an extremely accurate model that can’t be deployed.
This operational aspect forms another key distinction between analytics and business analytics. More often than not, analytics is about answering a question at a point in time. Business analytics, on the other hand, is about sustained value delivery. Tracking value and measuring performance, therefore, become critical elements of ensuring long-term value from business analytics.
CORE CONCEPTS AND DEFINITIONS
This section presents a brief primer and is unfortunately necessarily dry; it provides the core conceptual framework for everything discussed in this book. This book will refer repeatedly to a variety of concepts. Although 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, although there is tremendous potential for innovation, it has yet to develop a standard vocabulary.
The intent of the terms used throughout this book is simply to provide consistency, not to provide a definitive taxonomy or vocabulary. They’re worth reading closely even for those experienced in the application of business analytics—terms vary from person to person, and although readers may not always agree with the semantics presented here, given their own backgrounds and context, it’s essential that they understand what is meant by a particular word. Key terms are emphasized 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 and optimization)
Broadly speaking, analytics divides relatively neatly into techniques that help understand what happened and techniques that help understand:
What
will
happen.
Why it happened.
What is the best course of action.
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. 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. 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 new processes, reports, models, reports, 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 by having a team apply various competencies. A competency is a particular set of skills that can be applied to solve a wide variety of 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. These assets often include new processes, datamarts, models, or documentation. Often, tools are consolidated into a common analytical platform, a technology environment that ranges from being spread across multiple desktop personal computers (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 is to allow this insight to be applied automatically with strict requirements around reliability, performance, and availability.
The core concepts of people, process, data, and technology feature heavily in this book, and, although 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 toward better outcomes. When it comes to driving change, establishing a roadmap inevitably involves driving change across these four dimensions. Although this book isn’t explicitly written to fit with this framework, it relies heavily on it.
NOTE
1. Astute readers will notice that this section draws from my prior book, E. Stubbs, The Value of Business Analytics (Hoboken, NJ: Wiley, 2011).
Chapter 2
The Competitive Advantage of Business Analytics
Business analytics enables competitive advantage.1 Regardless of whether one uses classic SWOT (strengths, weaknesses, opportunities, threats) analysis, Porter’s five forces, the resource-based view of the firm, or Wilde and Hax’s delta model to identify and drive toward competitive differentiation, business analytics helps develop sustainable competitive advantage.
Intuitively, this makes sense: Smarter organizations that act on their insights tend to be more successful. Organizations that better understand their customers’ preferences and design their products to suit will easily differentiate themselves in the market. Insurers that have better awareness of the cost of risk will carry lower exposure than those that don’t.
It’s pithy, but it’s true: Making better decisions leads to better results, and business analytics helps organizations make better decisions. Counterintuitively, however, the specifics behind why this is so are harder to explain. Even those with extensive experience in the field often struggle to explain how business analytics supports competitive advantage beyond saying that “it creates better outcomes.” Although true, it lacks clarity, and the link between being smarter and achieving success remains vague.
Some organizations are willing to take this leap of faith. Through experience or experimentation, they succeed. Through a combination of time, trial, and error, they develop an awareness of what works and what doesn’t. These organizations are in the minority; most organizations are relatively risk averse and may not want to be the first to experiment with a new idea or initiative. Rather than start from first principles, they would rather make their investments with some degree of confidence that the approaches they’ll be following are grounded in both good theory as well as practical application.
Taking advantage of others’ experiences is reasonable and pragmatic. Despite this, it is a general rule is that the majority of projects involving either change2 or information technology (IT) delivery fail. Beyond just knowing that success is possible, success is easier when one knows the reasons behind success and encourages the behaviors and approaches that increase the odds of successful delivery. Unfortunately, the relative immaturity of business analytics as a discipline means that these best-practices and execution patterns are either not yet developed or largely ill-defined. Those who already know what works succeed, whereas those who don’t are forced to work off trial and error supported by guesswork and assumptions.
Compounding the challenge is that there is no one best policy or practice that fits all organizations. The best business-analytics applications support an organization’s unique business model and its strengths. They are a relative endeavor, one that capitalizes on an organization’s specific context and environment to achieve organization-specific tactical and strategic goals.
This uniqueness means that there’s no one-size-fits-all process that guarantees success. In this respect, business analytics has many parallels with strategic planning: Although there are high-level things that need to happen, the details are inevitably highly organization specific. Much as there isn’t one business model that fits all organizations, there isn’t one approach to business analytics.
This creates an apparent paradox. On one hand, it’s painfully obvious that some organizations are more successful than others when it comes to business analytics. Clearly, not every approach is equally effective. On the other hand, though, it’s also painfully clear that every organization needs a different approach to drive maximum value. So it’s not simply a case of copying one’s competitors!
The obvious answer is to hire a guru. The teams that do succeed usually have the benefit of a grizzled, battle-scarred individual with hard-won experience. However, success shouldn’t rely on the one capable individual. Like anything else, there are patterns and behaviors that increase the odds of success. This goes beyond making sure initiatives are aligned to strategy or having an understanding of how initiatives will create economic returns. The best guidelines are those that enable the process.
Ask a random sample of practitioners what they think drives success and they’ll inevitably say one or more of the following:
Having strong data management capabilities
Engaging with the right areas of the business
Being able to innovate
Giving the business what it needs rather than what it wants
Making sure information is accurate and trustworthy
Responding to business requests in an acceptable time frame
Delivering measurable value to the business
Having the trust of the business
These are all true. Unfortunately, they’re also imprecise. Although they enable success, they also fail to give any guidance on how to do it. Without knowing the general reasons that these are so significant in driving success, practitioners stick with what they know and are hesitant to try anything new.
This book aims to clarify this uncertainty. The rest of this chapter investigates the structural and economic reasons behind business analytics that lead to competitive differentiation. By doing so, it establishes a framework for establishing best practice. Although it’s unrealistic to expect that one process will fit all organizations, it’s eminently reasonable to have a series of guidelines that, if followed, will lead to best practice.
These will form the foundation for the rest of this book. Every best-practice guideline and solution described in this book aligns to the drivers described later; more than anything else, they establish an objective litmus test to check whether any given change could be seen as moving toward best practice. If a change runs counter to the recommendations described later, more likely than not it is a movement away from best practice and will create inefficiencies rather than efficiencies.
ADVANTAGES OF BUSINESS ANALYTICS
Strategically, business analytics enables differentiation. Knowing this doesn’t actually help explain why it’s true. And, without knowing why, it’s impossible to plan for the “how.” Without knowing how business analytics creates and augments competitive differentiation, it’s hard to know how best to go about improving things.
Business analytics, as a discipline, is primarily about driving change. This in turn means that those driving change must define what that change will look like. Some things are easier to define than others. For example:
Technology architectures are generally designed based on vendor-specific best practices. For these, the best approach is largely defined by reference-technology architecture.
Outcomes and predictions can be benchmarked on their accuracy and robustness. For these, the best approach is largely defined by the method that produces the best prediction when taking into account the need for stability and other statistical measures.
Information-management activities can be benchmarked on storage efficiency, query performance, and ease of interrogation. For these, the best approach is largely defined by quantifiable measures.
It’s great that some things are clear. Unfortunately, these represent a small proportion of the change needed! Getting the most from business analytics also requires people and process change, both of which generally lack easily quantifiable measures. Driving this change requires working out how things should look.
It’s tempting to try and approach best practices in business analytics as a series of standardized, expert-defined processes. This works well in other fields; enterprise resource planning systems and other operational systems are often based on a series of strongly defined process templates that help drive maximum efficiency across different organizations.
Unfortunately, this doesn’t really work. Common and specific best practices don’t exist in any useful sense in business analytics; because business analytics is aligned to organizational strategy, there can only ever be general advice. What works for one organization may not work for others.
The biggest reason that a strongly defined approach tends not to work is because there are too many activities that require weakly defined processes. Exploratory data analysis, for example, is by definition exploratory; one doesn’t know what’s going to work until one’s found it! Even worse, business analytics is also heavily linked to innovation. When you’re the first person to try a new approach, it’s impossible to base that process on an already-defined best-practice approach!
At this point, it may appear that best practice in business analytics is an oxymoron. This is obviously patently false; if it were true, there’d be no performance differences among organizations. There are certain principles that help drive efficiency, success, and competitive differentiation.
Consider the classic situation in which someone generates significant insights on their desktop PC using niche, nonenterprise tools. Once they understand what needs to be done, they call the execution team and send across a variety of reports that identify who should be contacted. To make sure it happens, they follow up repeatedly with the team and, over time, they deliver real economic value.
Now, consider another situation in which someone generates those same insights on an enterprise platform, thereby giving their peers the ability to capitalize on their insights. At the end of their analysis, they transform their insights into an asset that is then deployed into the contact-center management system, immediately updating the execution team with a refined contact list.
Both situations go through the same activities and both arrive at the same outcome. In this sense, each is as good as the other. However, the second offers a number of clear advantages. Minimally, it:
Cross-pollinates knowledge processes across the organization
Increases the odds of the execution team acting on the insights
Minimizes the time it takes to move from insight to action
These provide tangential benefits that go beyond the direct economic value they create. Through these extra benefits, they help drive competitive advantage. It is these extra benefits that help define whether a given change is directionally correct in moving toward best practice.
Competitive Advantage
From a microeconomic perspective, business analytics drives competitive advantage by generating:
Economies of scale
Economies of scope
Quality improvement
However, there are two key factors that hinder best practice and, when ineffectively managed, they can actively undermine competitive advantage.
These are:
Given this framework, best practice in business analytics can then be defined as any movement toward these positive outcomes that simultaneously avoids the associated negative constraints. With this, we have a tightly defined litmus test against which every process can be compared against best practice.
Maximizing Economies of Scale
Economies of scale occur when the average cost per output falls as production increases. This frequently occurs when fixed costs are proportionally higher than variable costs. When sunk costs are higher than the variable costs associated with production or service delivery, organizations have a strong incentive to increase production to maximum capacity to minimize average cost. The reason is simple—the first good produced carries all the cost!
The classic example for this lies in manufacturing. Plant and materials are usually many orders of magnitude more expensive than the variable costs associated with assembly. Setting up the factory might cost a few billion dollars, but the variable costs associated with assembling the product from raw materials might only cost in the tens of thousands of dollars. Given a well-defined cost curve with high sunk costs, the manufacturer needs to manufacture as much as they possibly can to achieve maximum price competitiveness.
Other sources of economies of scale can be achieved by reducing transaction costs or minimizing risk and volatility through volume. Many areas of modern business experience economies of scale to some degree:
Marketing can reuse their copy across multiple markets, driving down average campaign costs.
Finance can obtain lower interest rates by borrowing greater amounts of money.
Vendor management can achieve greater discounts through bulk purchasing.
When they exist and can be harnessed, economies of scale drive cost advantages. Markets that offer economies of scale allow first-mover organizations to achieve either a cost advantage or a margin benefit as they move across their production curve. Over time, this cost advantage may disappear as competitors also achieve economies of scale, assuming they can capitalize on them.
This is not always a guaranteed outcome—achieving true economies of scale may require know-how, unique intellectual property (IP), or unique technology that only the original organization has access to. When this occurs, those cost advantages become a form of sustainable competitive advantage rather than a transitory competitive advantage.
Business analytics exhibits a number of characteristics that lead to economies of scale. First, there are weak cost advantages to scaling the use of business analytics within an organization. Licensing models vary, but most current technology (hardware and software) tends to be licensed on a structural/capital cost basis. Volume purchasing allows organizations to insist on discounts, decreasing their average cost per processing unit or user.
Organizations reduce total capital investment by taking advantage of oversubscription. They may start by investing in hardware, software, and support for 50 PCs with 4 cores and 8 gigabytes of RAM each. Although the organization is paying for a total processing pool of 200 cores and 400 gigabytes of RAM, none of the users can actually capitalize on this pool; they’re all limited to their personal PC.
If these users all follow the typical burst processing pattern (in which their PCs all sit largely underutilized most of the time), the same organization could take advantage of these patterns and achieve the same outcome with a single blade environment that has 64 cores and 64 gigabytes of pooled RAM. Not only does every user get access to a higher-performing environment, but the organization also reduces its capital and support costs, driving economies of scale.
Of greater influence are the sunk costs that go along with driving sophistication. Although analytical solutions don’t need to leverage advanced techniques, sophistication can act as a source of competitive differentiation. More advanced techniques require significant amounts of experience and training, each of which carries heavy time and monetary costs. Becoming proficient in a specialized area can take years of postgraduate education and practical experience. Once developed, however, these same advanced techniques can be applied across multiple problems, driving sophistication across multiple problems. When these advanced techniques drive greater productivity or accuracy, organizations that successfully capitalize on them experience moderate to strong economies of scale.
Business analytics helps drive weak and strong economies of scale, partly for generic IT reasons and partly for discipline-specific characteristics. Probably more important, the broader industry has yet to effectively capitalize on these economies of scale, giving those organizations that do capitalize on them a current and possibly sustained competitive advantage.
Taking advantage of these economies of scale is the first way organizations achieve comparative cost efficiencies and drive competitive advantage against their peers.
Maximizing Economies of Scope
Economies of scope drive slightly different cost economies—rather than being related to volume, efficiencies come from breadth. Economies of scope occur when the average cost of production falls as the scope of activities increase. This is independent to an organization’s scale of operations, Rather than being related to volume of activity, efficiencies come purely from reusing inputs or competencies across multiple production lines.
Renewable energy is an often-cited source of economies of scope. A major byproduct of most manufacturing processes is heat. If left untapped, this heat acts as both a waste product as well as a source of market failure. The public cost of this negative externality is rarely factored into the market cost of the good. In isolation, manufacturers might see these byproducts as an inherent cost of production.
Approached from a different perspective though, these byproducts can actually create cost advantages through economies of scope. Progressive manufacturers have realized that these byproducts can be used for competitive advantage. Heat can be used to drive alternative sources of energy—by capturing heat-based waste products and blending them with other inputs such as water, manufacturers can reduce their costs in other areas. They can displace central heating in colder climates by recirculating heat. They can use this energy to drive secondary turbines, reducing externally sourced energy requirements. Or they can offset risk in volatile environments by using this energy to maintain backup power supplies. This general solution has applicability beyond pure manufacturing; another common context is managing data centers, the major byproduct of which is heat.
In each of these cases, consuming the byproducts of one value chain helps drive cost efficiencies across the business. At an aggregate level, economies of scope exist when diversifying activities across value chains that have correlated inputs and outputs helps drive down the average cost of production.
Compared to economies of scale, economies of scope occur less frequently. Typically, they manifest when the non-value-adding output of one value chain is a valuable input into an independent value chain. One of the reasons that economies of scope are rarer is that fewer markets or production processes have the necessary commonality of input or outputs within independent value chains.
Business analytics offers moderate to strong economies of scope, largely due to the incremental cost of developing assets using generalized competencies. To understand why, it’s important to revisit the value chain of a business analytics team. They will normally take a large set of data and use a variety of tools in conjunction with experience and technical skills to generate one or more intellectual property-based assets. These assets help drive a positive economic outcome for the business within a particular context (such as customer retention or inventory management) by providing automated recommendations and/or actions. These recommendations are of a higher quality than existing activities and, when executed, drive better outcomes.
The major drivers behind business analytics’ economies of scope lie in its reusable skills and direct linkage to higher-quality outcomes. Capitalizing on business analytics requires technical skills to varying degrees. At their simplest, these skills can be developed with a minimum of investment; given the right data, learning how to build a simple report in a well-designed toolkit should only take a matter of hours. At their most advanced, these skills can take years of theoretical training along with decades of practical experience. The most advanced forms of analytics can require highly advanced skills in mathematics, computer science, or information-systems design.
Business analytics generates economies of scope because its supporting skills can be reused to solve multiple business problems. Developing new skills carries significant costs; people are rarely productive while they come to terms with their new competencies. Becoming effective in a new area, therefore, carries substantial sunk costs. However, once developed, these competencies can be leveraged within multiple value chains and different contexts with relatively minimal incremental cost. Although this leads to moderate yet constrained economies of scale, it offers significant economies of scope.
For example, the competencies required to drive cross-sell and propensity-based marketing are the same as those needed to identify intentional noncompliance and active fraud. This reuse drives economies of scope, generating cost advantages in organizations that are successful in encouraging cross-functional applications of business-analytics resources.
Significantly, though, this cost advantage is only available to organizations that successfully overcome their internal resistance to reusing competencies across the business. Although this may seem like an obvious approach to take, very few organizations have developed a sufficient level of maturity in business analytics to realize that these economies exist and that they can capitalize on them.
Taking advantage of these economies of scope is the second way organizations achieve comparative cost efficiencies and drive competitive advantage against their peers.
Quality Improvement
Business analytics helps organizations do things better. It’s this aspect that provides the final key to competitive advantage: the ability of business analytics to drive higher quality. This in itself can be both profound and confusing.
Quality is a complex concept, one that seemingly defies definition. We know it when we see it, but explaining how it comes about is difficult. No one would argue that there’s a big difference between a performance sports car and budget hatchback, but trying to explain why can be frustrating. Usually, it devolves to a case of comparing features or simply saying that one is better, neither of which is entirely satisfactory.
A tremendous amount has been written on the metaphysics of quality, spanning right from the philosophical musings of Pirsig’s (1974) Zen Buddhist bent right though to Juran’s (1951), Sittig’s (1963), and Turner’s (1969) attempts to economically link cost to quality. A common theme within the research is that quality correlates highly with return, regardless of how difficult it is to define. Crosby (1979) went so far as to insist that quality played such a strong role in driving efficiency and savings that, in effect, quality is free.
This text takes the soft road. Rather than try and create a comprehensive definition for quality, it merely acknowledges the role that quality plays in driving better outcomes. For those looking for a more concrete definition, this book adopts the perspectives of both Deming (1988) and Drucker (1985): Quality helps drive both internal efficiencies (through reducing costs and driving productivity) as well as improving the customer’s end experience, increasing the amount they’re willing to pay or spend.
Although it’s a subjective concept, it’s useful to think of it as a comparative measure. Given a target outcome with multiple production options and a selection of common inputs, the production process that produces the best outcome can be seen as the higher-quality process. The best outcome is usually dictated by context; production efficiency, market demand, defect management, or a host of other measures may dictate which is the better outcome.
Practical examples abound. One approach may lead to better offer redemptions. One approach may lead to lower product variability. One approach may lead to a lower frequency of fraudulent claims. Given a series of quantifiable outcomes, an organization can benchmark every possible approach and identify which offers the highest quality.
Business analytics is not the only way to drive higher-quality outcomes. However, it’s a major one. Information and data, when effectively used, reduce a tremendous amount of uncertainty and, in almost all cases, they lead to a better outcome as long the insights are accurate and acted upon. There are two specific reasons for this. Business analytics:
