Delivering Business Analytics

Practical Guidelines for Best Practice

Evan Stubbs

• Publisher: Wiley
• Year: 2013
• Print ISBN: 9781118370568
• eBook ISBN: 9781118559444
• Edition: 1
• Topic: Business
• Subtopic: Decision Making
• Index: Business

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.

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 ...

Table of contents

• Cover
• Contents
• Title
• Copyright
• Dedication
• Preface
• Acknowledgments
• Part One: Business Analytics Best Practices
• Part Two: The Data Scientist’s Code
• Part Three: Practical Solutions: People and Process
• Part Four: Practical Solutions: Systems and Assets
• Part Five: Practical Solutions: Data and Decision Making
• Appendix: The Cheat Sheets
• Glossary
• Further Reading
• About the Author
• Index