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 the two books (10 to 15 percent), but the first book sets the stage and introduces concepts that are explored in more detail in this book.
Tools You Will Need
No special tools are required other than a pencil, an eraser, several sheets of paper, and your creativity. Grab a chai tea latte, some Chipotle, and enjoy!
What's on the Website
You can download the “Thinking Like a Data Scientist” workbook from the book's website at www.wiley.com/go/bigdatamba. And oh, there might be another surprise there as well! Hehehe!
What This Means for You
As students from my class at USF have told me, this material allows them to take a problem or challenge and use a well-thought-out process to drive cross-organizational collaboration to come up with ideas they can turn into actions using data and analytics. What employer wouldn't want a future leader who knows how to do that?
Part I
Business Potential of Big Data
Chapters 1 through 4 set the foundation for driving business strategies with data science. In particular, the Big Data Business Model Maturity Index highlights the realm of what's possible from a business potential perspective by providing a road map that measures the effectiveness of your organization to leverage data and analytics to power your business models.
Chapter 1
The Big Data Business Mandate
Having trouble getting your senior management team to understand the business potential of big data? Can't get your management leadership to consider big data to be something other than an IT science experiment? Are your line-of-business leaders unwilling to commit themselves to understanding how data and analytics can power their top initiatives?
If so, then this “Big Data Senior Executive Care Package” is for you!
And for a limited time, you get an unlimited license to share this care package with as many senior executives as you desire. But you must act NOW! Become the life of the company parties with your extensive knowledge of how new customer, product, and operational insights can guide your organization's value creation processes. And maybe, just maybe, get a promotion in the process!!
NOTE
All company material referenced in this book comes from public sources and is referenced accordingly.
Big Data MBA Introduction
The days when business users and business management can relinquish control of data and analytics to IT are over, or at least for organizations that want to survive beyond the immediate term. The big data discussion now needs to focus on how organizations can couple new sources of customer, product, and operational data with advanced analytics (data science) to power their key business processes and elevate their business models. Organizations need to understand that they do not need a big data strategy as much as they need a business strategy that incorporates big data.
The Big Data MBA challenges the thinking that data and analytics are ancillary or a “bolt on” to the business; that data and analytics are someone else's
