,Introduction
I never planned on writing a second book. Heck, I thought writing one book was enough to
check this item off my bucket list. But so much has changed since I wrote my first book that I
felt compelled to continue to explore this once-in-a-lifetime opportunity for organizations to
leverage data and analytics to transform their business models. And I'm not just talking the
“make me more money” part of businesses. Big data can drive significant “improve the quality
of life” value in areas such as education, poverty, parole rehabilitation, health care, safety, and
crime reduction.
My first book targeted the Information Technology (IT) audience. However, I soon realized
that the biggest winner in this big data land grab was the business. So this book targets the
business audience and is based on a few key premises:
Organizations do not need a big data strategy as much as they need a business strategy
that incorporates big data.
The days when business leaders could turn analytics over to IT are over; tomorrow's
business leaders must embrace analytics as a business discipline in the same vein as
accounting, finance, management science, and marketing.
The key to data monetization and business transformation lies in unleashing the
organization's creative thinking; we have got to get the business users to “think like a
data scientist.”
Finally, the business potential of big data is only limited by the creative thinking of the
business users.
I've also had the opportunity to teach “Big Data MBA” at the University of San Francisco
(USF) School of Management since I wrote the first book. I did well enough that USF made
me its first School of Management Fellow. What I experienced while working with these
outstanding and creative students and Professor Mouwafac Sidaoui compelled me to
undertake the challenge of writing this second book, targeting those students and tomorrow's
business leaders.
One of the topics that I hope jumps out in the book is the power of data science. There have
been many books written about data science with the goal of helping people to become data
scientists. But I felt that something was missing—that instead of trying to create a world of
data scientists, we needed to help tomorrow's business leaders think like data scientists.
So that's the focus of this book—to help tomorrow's business leaders integrate data and
analytics into their business models and to lead the cultural transformation by unleashing the
organization's creative juices by helping the business to “think like a data scientist.”
Overview of the Book and Technology
The days when business stakeholders could relinquish control of data and analytics to IT are
over. The business stakeholders must be front and center in championing and monetizing the
organization's data collection and analysis efforts. Business leaders need to understand where
and how to leverage big data, exploiting the collision of new sources of customer, product, and
operational data coupled with data science to optimize key business processes, uncover new
monetization opportunities, and create new sources of competitive differentiation. And while it's
not realistic to convert your business users into data scientists, it's critical that we teach the
business users to think like data scientists so they can collaborate with IT and the data scientists
,on use case identification, requirements definition, business valuation, and ultimately analytics
operationalization.
This book provides a business-hardened framework with supporting methodology and hands-
on exercises that not only will help business users to identify where and how to leverage big data
for business advantage but will also provide guidelines for operationalizing the analytics, setting
up the right organizational structure, and driving the analytic insights throughout the
organization's user experience to both customers and frontline employees.
How This Book Is Organized
The book is organized into four sections:
Part I: Business Potential of Big Data. Part I includes Chapters 1 through 4 and sets the
business-centric foundation for the book. Here is where I introduce the Big Data Business
Model Maturity Index and frame the big data discussion around the perspective that
“organizations do not need a big data strategy as much as they need a business strategy
that incorporates big data.”
Part II: Data Science. Part II includes Chapters 5 through 7 and covers the principle
behind data science. These chapters introduce some data science basics and explore the
complementary nature of Business Intelligence and data science and how these two
disciplines are both complementary and different in the problems that they address.
Part III: Data Science for Business Stakeholders. Part III includes Chapters
8 through 12 and seeks to teach the business users and business leaders to “think like a
data scientist.” This part introduces a methodology and several exercises to reinforce the
data science thinking and approach. It has a lot of hands-on work.
Part IV: Building Cross-Organizational Support. Part IV includes Chapters
13 through 15 and discusses organizational challenges. This part covers envisioning,
which may very well be the most important topic in the book as the business potential of
big data is only limited by the creative thinking of the business users.
Here are some more details on each of the chapters in the book:
Chapter 1: The Big Data Business Mandate. This chapter frames the big data discussion
on how big data is more about business transformation and the economics of big data
than it is about technology.
Chapter 2: Big Data Business Model Maturity Index. This chapter covers the Big Data
Business Model Maturity Index (BDBM), which is the foundation for the entire book. Take
the time to understand each of the five stages of the BDBM and how the BDBM provides
a road map for measuring how effective your organization is at integrating data and
analytics into your business models.
Chapter 3: The Big Data Strategy Document. This chapter introduces a CXO level
document and process for helping organizations identify where and how to start their big
data journeys from a business perspective.
Chapter 4: The Importance of the User Experience. This is one of my favorite topics.
This chapter challenges traditional Business Intelligence reporting and dashboard
concepts by introducing a more simple but direct approach for delivering actionable
, insights to your key business stakeholders—frontline employees, channel partners, and
end customers.
Chapter 5: Differences Between Business Intelligence and Data Science. This
chapter explores the different worlds of Business Intelligence and data science and
highlights both the differences and the complementary nature of each.
Chapter 6: Data Science 101. This chapter (my favorite) reviews 14 different analytic
techniques that my data science teams commonly use and in what business situations
you should contemplate using them. It is accompanied by a marvelous fictitious case
study using Fairy-Tale Theme Parks (thanks Jen!).
Chapter 7: The Data Lake. This chapter introduces the concept of a data lake, explaining
how the data lake frees up expensive data warehouse resources and unleashes the
creative, fail-fast nature of the data science teams.
Chapter 8: Thinking Like a Data Scientist. The heart of this book, this chapter covers
the eight-step “thinking like a data scientist” process. This chapter is pretty deep, so plan
on having a pen and paper (and probably an eraser as well) with you as you read this
chapter.
Chapter 9: “By” Analysis Technique. This chapter does a deep dive into one of the
important concepts in “thinking like a data scientist”—the “By” analysis technique.
Chapter 10: Score Development Technique. This chapter introduces how scores can
drive collaboration between the business users and data scientist to create actionable
scores that guide the organization's key business decisions.
Chapter 11: Monetization Exercise. This chapter provides a technique for
organizations that have a substantial amount of customer, product, and operational data
but do not know how to monetize that data. This chapter can be very eye-opening!
Chapter 12: Metamorphosis Exercise. This chapter is a fun, out-of-the-box exercise
that explores the potential data and analytic impacts for an organization as it
contemplates the Business Metamorphosis phase of the Big Data Business Model
Maturity Index.
Chapter 13: Power of Envisioning. This chapter starts to address some of the
organizational and cultural challenges you may face. In particular, Chapter 13 introduces
some envisioning techniques to help unleash your organization's creative thinking.
Chapter 14: Organizational Ramifications. This chapter goes into more detail about
the organizational ramifications of big data, especially the role of the Chief Data
(Monetization) Officer.
Chapter 15: Stories. The book wraps up with some case studies, but not your traditional
case studies. Instead, Chapter 15 presents a technique for creating “stories” that are
relevant to your organization. Anyone can find case studies, but not just anyone can
create a story.
Who Should Read This Book
This book is targeted toward business users and business management. I wrote this book so that
I could use it in teaching my Big Data MBA class, so included all of the hands-on exercises and
templates that my students would need to successfully earn their Big Data MBA graduation
certificate.
I think folks would benefit by also reading my first book, Big Data: Understanding How Data
Powers Big Business, which is targeted toward the IT audience. There is some overlap between
I never planned on writing a second book. Heck, I thought writing one book was enough to
check this item off my bucket list. But so much has changed since I wrote my first book that I
felt compelled to continue to explore this once-in-a-lifetime opportunity for organizations to
leverage data and analytics to transform their business models. And I'm not just talking the
“make me more money” part of businesses. Big data can drive significant “improve the quality
of life” value in areas such as education, poverty, parole rehabilitation, health care, safety, and
crime reduction.
My first book targeted the Information Technology (IT) audience. However, I soon realized
that the biggest winner in this big data land grab was the business. So this book targets the
business audience and is based on a few key premises:
Organizations do not need a big data strategy as much as they need a business strategy
that incorporates big data.
The days when business leaders could turn analytics over to IT are over; tomorrow's
business leaders must embrace analytics as a business discipline in the same vein as
accounting, finance, management science, and marketing.
The key to data monetization and business transformation lies in unleashing the
organization's creative thinking; we have got to get the business users to “think like a
data scientist.”
Finally, the business potential of big data is only limited by the creative thinking of the
business users.
I've also had the opportunity to teach “Big Data MBA” at the University of San Francisco
(USF) School of Management since I wrote the first book. I did well enough that USF made
me its first School of Management Fellow. What I experienced while working with these
outstanding and creative students and Professor Mouwafac Sidaoui compelled me to
undertake the challenge of writing this second book, targeting those students and tomorrow's
business leaders.
One of the topics that I hope jumps out in the book is the power of data science. There have
been many books written about data science with the goal of helping people to become data
scientists. But I felt that something was missing—that instead of trying to create a world of
data scientists, we needed to help tomorrow's business leaders think like data scientists.
So that's the focus of this book—to help tomorrow's business leaders integrate data and
analytics into their business models and to lead the cultural transformation by unleashing the
organization's creative juices by helping the business to “think like a data scientist.”
Overview of the Book and Technology
The days when business stakeholders could relinquish control of data and analytics to IT are
over. The business stakeholders must be front and center in championing and monetizing the
organization's data collection and analysis efforts. Business leaders need to understand where
and how to leverage big data, exploiting the collision of new sources of customer, product, and
operational data coupled with data science to optimize key business processes, uncover new
monetization opportunities, and create new sources of competitive differentiation. And while it's
not realistic to convert your business users into data scientists, it's critical that we teach the
business users to think like data scientists so they can collaborate with IT and the data scientists
,on use case identification, requirements definition, business valuation, and ultimately analytics
operationalization.
This book provides a business-hardened framework with supporting methodology and hands-
on exercises that not only will help business users to identify where and how to leverage big data
for business advantage but will also provide guidelines for operationalizing the analytics, setting
up the right organizational structure, and driving the analytic insights throughout the
organization's user experience to both customers and frontline employees.
How This Book Is Organized
The book is organized into four sections:
Part I: Business Potential of Big Data. Part I includes Chapters 1 through 4 and sets the
business-centric foundation for the book. Here is where I introduce the Big Data Business
Model Maturity Index and frame the big data discussion around the perspective that
“organizations do not need a big data strategy as much as they need a business strategy
that incorporates big data.”
Part II: Data Science. Part II includes Chapters 5 through 7 and covers the principle
behind data science. These chapters introduce some data science basics and explore the
complementary nature of Business Intelligence and data science and how these two
disciplines are both complementary and different in the problems that they address.
Part III: Data Science for Business Stakeholders. Part III includes Chapters
8 through 12 and seeks to teach the business users and business leaders to “think like a
data scientist.” This part introduces a methodology and several exercises to reinforce the
data science thinking and approach. It has a lot of hands-on work.
Part IV: Building Cross-Organizational Support. Part IV includes Chapters
13 through 15 and discusses organizational challenges. This part covers envisioning,
which may very well be the most important topic in the book as the business potential of
big data is only limited by the creative thinking of the business users.
Here are some more details on each of the chapters in the book:
Chapter 1: The Big Data Business Mandate. This chapter frames the big data discussion
on how big data is more about business transformation and the economics of big data
than it is about technology.
Chapter 2: Big Data Business Model Maturity Index. This chapter covers the Big Data
Business Model Maturity Index (BDBM), which is the foundation for the entire book. Take
the time to understand each of the five stages of the BDBM and how the BDBM provides
a road map for measuring how effective your organization is at integrating data and
analytics into your business models.
Chapter 3: The Big Data Strategy Document. This chapter introduces a CXO level
document and process for helping organizations identify where and how to start their big
data journeys from a business perspective.
Chapter 4: The Importance of the User Experience. This is one of my favorite topics.
This chapter challenges traditional Business Intelligence reporting and dashboard
concepts by introducing a more simple but direct approach for delivering actionable
, insights to your key business stakeholders—frontline employees, channel partners, and
end customers.
Chapter 5: Differences Between Business Intelligence and Data Science. This
chapter explores the different worlds of Business Intelligence and data science and
highlights both the differences and the complementary nature of each.
Chapter 6: Data Science 101. This chapter (my favorite) reviews 14 different analytic
techniques that my data science teams commonly use and in what business situations
you should contemplate using them. It is accompanied by a marvelous fictitious case
study using Fairy-Tale Theme Parks (thanks Jen!).
Chapter 7: The Data Lake. This chapter introduces the concept of a data lake, explaining
how the data lake frees up expensive data warehouse resources and unleashes the
creative, fail-fast nature of the data science teams.
Chapter 8: Thinking Like a Data Scientist. The heart of this book, this chapter covers
the eight-step “thinking like a data scientist” process. This chapter is pretty deep, so plan
on having a pen and paper (and probably an eraser as well) with you as you read this
chapter.
Chapter 9: “By” Analysis Technique. This chapter does a deep dive into one of the
important concepts in “thinking like a data scientist”—the “By” analysis technique.
Chapter 10: Score Development Technique. This chapter introduces how scores can
drive collaboration between the business users and data scientist to create actionable
scores that guide the organization's key business decisions.
Chapter 11: Monetization Exercise. This chapter provides a technique for
organizations that have a substantial amount of customer, product, and operational data
but do not know how to monetize that data. This chapter can be very eye-opening!
Chapter 12: Metamorphosis Exercise. This chapter is a fun, out-of-the-box exercise
that explores the potential data and analytic impacts for an organization as it
contemplates the Business Metamorphosis phase of the Big Data Business Model
Maturity Index.
Chapter 13: Power of Envisioning. This chapter starts to address some of the
organizational and cultural challenges you may face. In particular, Chapter 13 introduces
some envisioning techniques to help unleash your organization's creative thinking.
Chapter 14: Organizational Ramifications. This chapter goes into more detail about
the organizational ramifications of big data, especially the role of the Chief Data
(Monetization) Officer.
Chapter 15: Stories. The book wraps up with some case studies, but not your traditional
case studies. Instead, Chapter 15 presents a technique for creating “stories” that are
relevant to your organization. Anyone can find case studies, but not just anyone can
create a story.
Who Should Read This Book
This book is targeted toward business users and business management. I wrote this book so that
I could use it in teaching my Big Data MBA class, so included all of the hands-on exercises and
templates that my students would need to successfully earn their Big Data MBA graduation
certificate.
I think folks would benefit by also reading my first book, Big Data: Understanding How Data
Powers Big Business, which is targeted toward the IT audience. There is some overlap between
