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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to "think like a data scientist" as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. * Understand where and how to leverage big data * Integrate analytics into everyday operations * Structure your organization to drive analytic insights * Optimize processes, uncover opportunities, and stand out from the rest * Help business stakeholders to "think like a data scientist" * Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions.
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Seitenzahl: 379
Veröffentlichungsjahr: 2015
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Table of Contents
Introduction
Overview of the Book and Technology
How This Book Is Organized
Who Should Read This Book
Tools You Will Need
What's on the Website
What This Means for You
Part I: Business Potential of Big Data
Chapter 1: The Big Data Business Mandate
Big Data MBA Introduction
Focus Big Data on Driving Competitive Differentiation
Critical Importance of “Thinking Differently”
Summary
Homework Assignment
Notes
Chapter 2: Big Data Business Model Maturity Index
Introducing the Big Data Business Model Maturity Index
Big Data Business Model Maturity Index Lessons Learned
Summary
Homework Assignment
Chapter 3: The Big Data Strategy Document
Establishing Common Business Terminology
Introducing the Big Data Strategy Document
Introducing the Prioritization Matrix
Using the Big Data Strategy Document to Win the World Series
Summary
Homework Assignment
Notes
Chapter 4: The Importance of the User Experience
The Unintelligent User Experience
Consumer Case Study: Improve Customer Engagement
Business Case Study: Enable Frontline Employees
B2B Case Study: Make the Channel More Effective
Summary
Homework Assignment
Part II: Data Science
Chapter 5: Differences Between Business Intelligence and Data Science
What Is Data Science?
The Analyst Characteristics Are Different
The Analytic Approaches Are Different
The Data Models Are Different
The View of the Business Is Different
Summary
Homework Assignment
Notes
Chapter 6: Data Science 101
Data Science Case Study Setup
Fundamental Exploratory Analytics
Analytic Algorithms and Models
Summary
Homework Assignment
Notes
Chapter 7: The Data Lake
Introduction to the Data Lake
Characteristics of a Business-Ready Data Lake
Using the Data Lake to Cross the Analytics Chasm
Modernize Your Data and Analytics Environment
Analytics Hub and Spoke Analytics Architecture
Early Learnings
What Does the Future Hold?
Summary
Homework Assignment
Notes
Part III: Data Science for Business Stakeholders
Chapter 8: Thinking Like a Data Scientist
The Process of Thinking Like a Data Scientist
Summary
Homework Assignment
Notes
Chapter 9: “By” Analysis Technique
“By” Analysis Introduction
“By” Analysis Exercise
Foot Locker Use Case “By” Analysis
Summary
Homework Assignment
Notes
Chapter 10: Score Development Technique
Definition of a Score
FICO Score Example
Other Industry Score Examples
LeBron James Exercise Continued
Foot Locker Example Continued
Summary
Homework Assignment
Notes
Chapter 11: Monetization Exercise
Fitness Tracker Monetization Example
Summary
Homework Assignment
Notes
Chapter 12: Metamorphosis Exercise
Business Metamorphosis Review
Business Metamorphosis Exercise
Business Metamorphosis in Health Care
Summary
Homework Assignment
Notes
Part IV: Building Cross-Organizational Support
Chapter 13: Power of Envisioning
Envisioning: Fueling Creative Thinking
The Prioritization Matrix
Summary
Homework Assignment
Notes
Chapter 14: Organizational Ramifications
Chief Data Monetization Officer
Privacy, Trust, and Decision Governance
Unleashing Organizational Creativity
Summary
Homework Assignment
Notes
Chapter 15: Stories
Customer and Employee Analytics
Product and Device Analytics
Network and Operational Analytics
Characteristics of a Good Business Story
Summary
Homework Assignment
Notes
End User License Agreement
End User License Agreement
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Guide
Table of Contents
Begin Reading
List of Illustrations
Chapter 1: The Big Data Business Mandate
Figure 1.1 Big Data Business Model Maturity Index
Figure 1.2 Modern data/analytics environment
Chapter 2: Big Data Business Model Maturity Index
Figure 2.1 Big Data Business Model Maturity Index
Figure 2.2 Crossing the analytics chasm
Figure 2.3 Packaging and selling audience insights
Figure 2.4 Optimize internal processes
Figure 2.5 Create new monetization opportunities
Chapter 3: The Big Data Strategy Document
Figure 3.1 Big data strategy decomposition process
Figure 3.2 Big data strategy document
Figure 3.3 Chipotle's 2012 letter to the shareholders
Figure 3.4 Chipotle's “increase same store sales” business initiative
Figure 3.5 Chipotle key business entities and decisions
Figure 3.6 Completed Chipotle big data strategy document
Figure 3.7 Business value of potential Chipotle data sources
Figure 3.8 Implementation feasibility of potential Chipotle data sources
Figure 3.9 Chipotle prioritization of use cases
Figure 3.10 San Francisco Giants big data strategy document
Figure 3.11 Chipotle's same store sales results
Chapter 4: The Importance of the User Experience
Figure 4.1 Original subscriber e-mail
Figure 4.2 Improved subscriber e-mail
Figure 4.3 Actionable subscriber e-mail
Figure 4.4 App recommendations
Figure 4.5 Traditional Business Intelligence dashboard
Figure 4.6 Actionable store manager dashboard
Figure 4.7 Store manager accept/reject recommendations
Figure 4.8 Competitive analysis use case
Figure 4.9 Local events use case
Figure 4.10 Local weather use case
Figure 4.11 Financial advisor dashboard
Figure 4.12 Client personal information
Figure 4.13 Client financial information
Figure 4.14 Client financial goals
Figure 4.15 Financial contributions recommendations
Figure 4.16 Spend analysis and recommendations
Figure 4.17 Asset allocation recommendations
Figure 4.18 Other investment recommendations
Chapter 5: Differences Between Business Intelligence and Data Science
Figure 5.1 Schmarzo TDWI keynote, August 2008
Figure 5.2 Oakland A's versus New York Yankees cost per win
Figure 5.3 Business Intelligence versus data science
Figure 5.4 CRISP: Cross Industry Standard Process for Data Mining
Figure 5.5 Business Intelligence engagement process
Figure 5.6 Typical BI tool graphic options
Figure 5.7 Data scientist engagement process
Figure 5.8 Measuring goodness of fit
Figure 5.9 Dimensional model (star schema)
Figure 5.10 Using flat files to eliminate or reduce joins on Hadoop
Figure 5.11 Sample customer analytic profile
Figure 5.12 Improve customer retention example
Chapter 6: Data Science 101
Figure 6.1 Basic trend analysis
Figure 6.2 Compound trend analysis
Figure 6.3 Trend line analysis
Figure 6.4 Boxplot analysis
Figure 6.5 Geographical (spatial) trend analysis
Figure 6.6 Pairs plot analysis
Figure 6.7 Time series decomposition analysis
Figure 6.8 Cluster analysis
Figure 6.9 Normal curve equivalent analysis
Figure 6.10 Normal curve equivalent seller pricing analysis example
Figure 6.11 Association analysis
Figure 6.12 Converting association rules into segments
Figure 6.13 Graph analysis
Figure 6.14 Text mining analysis
Figure 6.15 Sentiment analysis
Figure 6.16 Traverse pattern analysis
Figure 6.17 Decision tree classifier analysis
Figure 6.18 Cohorts analysis
Chapter 7: The Data Lake
Figure 7.1 Characteristics of a data lake
Figure 7.2 The analytics dilemma
Figure 7.3 The data lake line of demarcation
Figure 7.4 Create a Hadoop-based data lake
Figure 7.5 Create an analytic sandbox
Figure 7.6 Move ETL to the data lake
Figure 7.7 Hub and Spoke analytics architecture
Figure 7.8 Data science engagement process
Figure 7.9 What does the future hold?
Figure 7.10 EMC Federation Business Data Lake
Chapter 8: Thinking Like a Data Scientist
Figure 8.1 Foot Locker's key business initiatives
Figure 8.2 Examples of Foot Locker's in-store merchandising
Figure 8.3 Foot Locker's store manager persona
Figure 8.4 Foot Locker's strategic nouns or key business entities
Figure 8.5 Thinking like a data scientist decomposition process
Figure 8.6 Recommendations worksheet template
Figure 8.7 Foot Locker's recommendations worksheet
Figure 8.8 Foot Locker's store manager actionable dashboard
Figure 8.9 Thinking like a data scientist decomposition process
Chapter 9: “By” Analysis Technique
Figure 9.1 Identifying metrics that may be better predictors of performance
Figure 9.2 NBA shooting effectiveness
Figure 9.3 LeBron James's shooting effectiveness
Chapter 10: Score Development Technique
Figure 10.1 FICO score considerations
Figure 10.2 FICO score decision range
Figure 10.3 Recommendations worksheet
Figure 10.4 Updated recommendations worksheet
Figure 10.5 Completed recommendations worksheet
Figure 10.6 Potential Foot Locker customer scores
Figure 10.7 Foot Locker recommendations worksheet
Figure 10.8 CLTV based on sales
Figure 10.9 More predictive CLTV score
Chapter 11: Monetization Exercise
Figure 11.1 “A day in the life” customer persona
Figure 11.2 Fitness tracker prioritization
Figure 11.3 Monetization road map
Chapter 12: Metamorphosis Exercise
Figure 12.1 Big Data Business Model Maturity Index
Figure 12.2 Patient actionable analytic profile
Chapter 13: Power of Envisioning
Figure 13.1 Big Data Vision Workshop process and timeline
Figure 13.2 Big Data Vision Workshop illustrative analytics
Figure 13.3 Big Data Vision Workshop user experience mock-up
Figure 13.4 Prioritize Healthcare Systems's use cases
Figure 13.5 Prioritization matrix template
Figure 13.6 Prioritization matrix process
Chapter 14: Organizational Ramifications
Figure 14.1 CDMO organizational structure
Figure 14.2 Empowerment cycle
List of Tables
Chapter 1: The Big Data Business Mandate
Table 1.1 Exploiting Technology Innovation to Create Economic-Driven Business Opportunities
Table 1.2 Evolution of the Business Questions
Chapter 2: Big Data Business Model Maturity Index
Table 2.1 Big Data Business Model Maturity Index Summary
Chapter 3: The Big Data Strategy Document
Table 3.1 Mapping Chipotle Use Cases to Analytic Models
Chapter 5: Differences Between Business Intelligence and Data Science
Table 5.1 BI Analyst Versus Data Scientist Characteristics
Chapter 6: Data Science 101
Table 6.1 2014–2015 Top NBA RPM Rankings
Table 6.2 Case Study Summary
Chapter 7: The Data Lake
Table 7.1 Data Lake Data Types
Chapter 8: Thinking Like a Data Scientist
Table 8.1 Evolution of Foot Locker's Business Questions
Chapter 9: “By” Analysis Technique
Table 9.1 LeBron James's Shooting Percentages
Chapter 10: Score Development Technique
Table 10.1 Potential Scores for Other Industries
Chapter 11: Monetization Exercise
Table 11.1 Potential Fitness Tracker Recommendations
Table 11.2 Recommendation Data Requirements
Table 11.3 Recommendations Value Versus Feasibility Assessment
Chapter 12: Metamorphosis Exercise
Table 12.1 Decisions to Analytics Mapping
Table 12.2 Data-to-Analytics Mapping
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 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 IBusiness 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.
In This Part
Chapter 1
: The Big Data Business Mandate
Chapter 2
: Big Data Business Model Maturity Index
Chapter 3
: The Big Data Strategy Document
Chapter 4
: The Importance of the User Experience
Chapter 1The 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 problem. In a growing number of leading organizations, data and analytics are critical to business success and long-term survival. Business leaders and business users reading this book will learn why they must take responsibility for identifying where and how they can apply data and analytics to their businesses—otherwise they put their businesses at risk of being made obsolete by more nimble, data-driven competitors.
The Big Data MBA introduces and describes concepts, techniques, methodologies, and hand-on exercises to guide you as you seek to address the big data business mandate. The book provides hands-on exercises and homework assignments to make these concepts and techniques come to life for your organization. It provides recommendations and actions that enable your organization to start today. And in the process, Big Data MBA teaches you to “think like a data scientist.”
The Forrester study “Reset on Big Data” (Hopkins et al., 2014)1 highlights the critical role of a business-centric focus in the big data discussion. The study argues that technology-focused executives within a business will think of big data as a technology and fail to convey its importance to the boardroom.
Businesses of all sizes must reframe the big data conversation with the business leaders in the boardroom. The critical and difficult big data question that business leaders must address is:
How effective is our organization at integrating data and analytics into our business models?
Before business leaders can begin these discussions, organizations must understand their current level of big data maturity. Chapter 2 discusses in detail the “Big Data Business Model Maturity Index” (see Figure 1.1). The Big Data Business Model Maturity Index is a measure of how effective an organization is at integrating data and analytics to power their business model.
Figure 1.1 Big Data Business Model Maturity Index
The Big Data Business Model Maturity Index provides a road map for how organizations can integrate data and analytics into their business models. The Big Data Business Model Maturity Index is composed of the following five phases:
Phase 1: Business Monitoring.
In the Business Monitoring phase, organizations are leveraging data warehousing and Business Intelligence to monitor the organization's performance.
Phase 2: Business Insights.
The Business Insights phase is about leveraging predictive analytics to uncover customer, product, and operational insights buried in the growing wealth of internal and external data sources. In this phase, organizations aggressively expand their data acquisition efforts by coupling all of their detailed transactional and operational data with internal data such as consumer comments, e-mail conversations, and technician notes, as well as external and publicly available data such as social media, weather, traffic, economic, demographics, home values, and local events data.
Phase 3: Business Optimization.
In the Business Optimization phase, organizations apply prescriptive analytics to the customer, product, and operational insights uncovered in the Business Insights phase to deliver actionable insights or recommendations to frontline employees, business managers, and channel partners, as well as customers. The goal of the Business Optimization phase is to enable employees, partners, and customers to optimize their key decisions.
Phase 4: Data Monetization.
In the Data Monetization phase, organizations leverage the customer, product, and operational insights to create new sources of revenue. This could include selling data—or insights—into new markets (a cellular phone provider selling customer behavioral data to advertisers), integrating analytics into products and services to create “smart” products, or re-packaging customer, product, and operational insights to create new products and services, to enter new markets, and/or to reach new audiences.
Phase 5: Business Metamorphosis.
The holy grail of the Big Data Business Model Maturity Index is when an organization transitions its business model from selling products to selling “business-as-a-service.” Think GE selling “thrust” instead of jet engines. Think John Deere selling “farming optimization” instead of farming equipment. Think Boeing selling “air miles” instead of airplanes. And in the process, these organizations will create a platform enabling third-party developers to build and market solutions on top of the organization's business-as-a-service business model.
Ultimately, big data only matters if it helps organizations make more money and improve operational effectiveness. Examples include increasing customer acquisition, reducing customer churn, reducing operational and maintenance costs, optimizing prices and yield, reducing risks and errors, improving compliance, improving the customer experience, and more.
No matter the size of the organization, organizations don't need a big data strategy as much as they need a business strategy that incorporates big data.
Focus Big Data on Driving Competitive Differentiation
I'm always confused about how organizations struggle to differentiate between technology investments that drive competitive parity and those technology investments that create unique and compelling competitive differentiation. Let's explore this difference in a bit more detail.
Competitive parity is achieving similar or same operational capabilities as those of your competitors. It involves leveraging industry best practices and pre-packaged software to create a baseline that, at worst, is equal to the operational capabilities across your industry. Organizations end up achieving competitive parity when they buy foundational and undifferentiated capabilities from enterprise software packages such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and Sales Force Automation (SFA).
Competitive differentiation is achieved when an organization leverages people, processes, and technology to create applications, programs, processes, etc., that differentiate its products and services from those of its competitors in ways that add unique value for the end customer and create competitive differentiation in the marketplace.
Leading organizations should seek to “buy” foundational and undifferentiated capabilities but “build” what is differentiated and value-added for their customers. But sometimes organizations get confused between the two. Let's call this the ERP effect. ERP software packages were sold as a software solution that would make everyone more profitable by delivering operational excellence. But when everyone is running the same application, what's the source of the competitive differentiation?
Analytics, on the other hand, enables organizations to uniquely optimize their key business processes, drive a more engaging customer experience, and uncover new monetization opportunities with unique insights that they gather about their customers, products, and operations.
Leveraging Technology to Power Competitive Differentiation
While most organizations have invested heavily in ERP-type operational systems, far fewer have been successful in leveraging data and analytics to build strategic applications that provide unique value to their customers and create competitive differentiation in the marketplace. Here are some examples of organizations that have invested in building differentiated capabilities by leveraging new sources of data and analytics:
Google: PageRank and Ad Serving
Yahoo: Behavioral Targeting and Retargeting
Facebook: Ad Serving and News Feed
Apple: iTunes
Netflix: Movie Recommendations
Amazon: “Customers Who Bought This Item,” 1-Click ordering, and Supply Chain & Logistics
Walmart: Demand Forecasting, Supply Chain Logistics, and Retail Link
Procter & Gamble: Brand and Category Management
Federal Express: Critical Inventory Logistics
American Express and Visa: Fraud Detection
GE: Asset Optimization and Operations Optimization (Predix)
None of these organizations bought these strategic, business-differentiating applications off the shelf. They understood that it was necessary to provide differentiated value to their internal and external customers, and they leveraged data and analytics to build applications that delivered competitive differentiation.
History Lesson on Economic-Driven Business Transformation
More than anything else, the driving force behind big data is the economics of big data—it's 20 to 50 times cheaper to store, manage, and analyze data than it is to use traditional data warehousing technologies. This 20 to 50 times economic impact is courtesy of commodity hardware, open source software, an explosion of new open source tools coming out of academia, and ready access to free online training on topics such as big data architectures and data science. A client of mine in the insurance industry calculated a 50X economic impact. Another client in the health care industry calculated a 49X economic impact (they need to look harder to find that missing 1X).
History has shown that the most significant technology innovations are ones that drive economic change. From the printing press to interchangeable parts to the microprocessor, these technology innovations have provided an unprecedented opportunity for the more agile and more nimble organizations to disrupt existing markets and establish new value creation processes.
Big data possesses that same economic potential whether it be to create smart cities, improve the quality of medical care, improve educational effectiveness, reduce poverty, improve safety, reduce risks, or even cure cancer. And for many organizations, the first question that needs to be asked about big data is:
How effective is my organization at leveraging new sources of data and advanced analytics to uncover new customer, product, and operational insights that can be used to differentiate our customer engagement, optimize key business processes, and uncover new monetization opportunities?
Big data is nothing new, especially if you view it from the proper perspective. While the popular big data discussions are around “disruptive” technology innovations like Hadoop and Spark, the real discussion should be about the economic impact of big data. New technologies don't disrupt business models; it's what organizations do with these new technologies that disrupts business models and enables new ones. Let's review an example of one such economic-driven business transformation: the steam engine.
The steam engine enabled urbanization, industrialization, and the conquering of new territories. It literally shrank distance and time by reducing the time required to move people and goods from one side of a continent to the other. The steam engine enabled people to leave low-paying agricultural jobs and move into cities for higher-paying manufacturing and clerical jobs that led to a higher standard of living.
For example, cities such as London shot up in terms of population. In 1801, before the advent of George Stephenson's Rocket steam engine, London had 1.1 million residents. After the invention, the population of London more than doubled to 2.7 million residents by 1851. London transformed the nucleus of society from small tight-knit communities where textile production and agriculture were prevalent into big cities with a variety of jobs. The steam locomotive provided quicker transportation and more jobs, which in turn brought more people into the cities and drastically changed the job market. By 1861, only 2.4 percent of London's population was employed in agriculture, while 49.4 percent were in the manufacturing or transportation business. The steam locomotive was a major turning point in history as it transformed society from largely rural and agricultural into urban and industrial.2
Table 1.1 shows other historical lessons that demonstrate how technology innovation created economic-driven business opportunities.
Table 1.1 Exploiting Technology Innovation to Create Economic-Driven Business Opportunities
Technology Innovation
Economic Impact
Printing Press
Expanded literacy (simplified knowledge capture and enabled knowledge dissemination and the education of the masses)
Interchangeable Parts
Drove the standardization of manufacturing parts and fueled the industrial revolution
Steam Engine (Railroads and Steamboats)
Sparked urbanization (drove transition from agricultural to manufacturing-centric society)
Internal Combustion Engine
Triggered suburbanization (enabled personal mobility, both geographically and socially)
Interstate Highway System
Foundation for interstate commerce (enabled regional specialization and wealth creation)
Telephone
Democratized communications (by eliminating distance and delays as communications issues)
Computers
Automated common processes (thereby freeing humans for more creative engagement)
Internet
Gutted cost of commerce and knowledge sharing (enabled remote workforce and international competition)
This brings us back to big data. All of these innovations share the same lesson: it wasn't the technology that was disruptive; it was how organizations leveraged the technology to disrupt existing business models and enabled new ones.
Critical Importance of “Thinking Differently”
Organizations have been taught by technology vendors, press, and analysts to think faster, cheaper, and smaller, but they have not been taught to “think differently.” The inability to think differently is causing organizational alignment and business adoption problems with respect to the big data opportunity. Organizations must throw out much of their conventional data, analytics, and organizational thinking in order to get the maximum value out of big data. Let's introduce some key areas for thinking differently that will be covered throughout this book.
Don't Think Big Data Technology, Think Business Transformation
Many organizations are infatuated with the technical innovations surrounding big data and the three Vs of data: volume, variety, and velocity. But starting with a technology focus can quickly turn your big data initiative into a science experiment. You don't want to be a solution in search of a problem.
Instead, focus on the four Ms of big data: Make Me More Money (or if you are a non-profit organization, maybe that's Make Me More Efficient). Start your big data initiative with a business-first approach. Identify and focus on addressing the organization's key business initiatives, that is, what the organization is trying to accomplish from a business perspective over the next 9 to 12 months (e.g., reduce supply chain costs, improve supplier quality and reliability, reduce hospital-acquired infections, improve student performance). Break down or decompose this business initiative into the supporting decisions, questions, metrics, data, analytics, and technology necessary to support the targeted business initiative.
CROSS-REFERENCE
This book begins by covering the Big Data Business Model Maturity Index in Chapter 2. The Big Data Business Model Maturity Index helps organizations address the key question:
How effective is our organization at leveraging data and analytics to power our key business processes and uncover new monetization opportunities?
The maturity index provides a guide or road map with specific recommendations to help organizations advance up the maturity index. Chapter 3 introduces the big data strategy document. The big data strategy document provides a framework for helping organizations identify where and how to start their big data journey from a business perspective.
Don't Think Business Intelligence, Think Data Science
Data science is different from Business Intelligence (BI). Resist the advice to try to make these two different disciplines the same. For example:
Business Intelligence focuses on reporting what happened (descriptive analytics). Data science focuses on predicting what is likely to happen (predictive analytics) and then recommending what actions to take (prescriptive analytics).
Business Intelligence operates with schema on load in which you have to pre-build the data schema before you can load the data to generate your BI queries and reports. Data science deals with schema on query in which the data scientists custom design the data schema based on the hypothesis they want to test or the prediction that they want to make.
Organizations that try to “extend” their Business Intelligence capabilities to encompass big data will fail. That's like stating that you're going to the moon, then climbing a tree and declaring that you are closer. Unfortunately, you can't get to the moon from the top of a tree. Data science is a new discipline that offers compelling, business-differentiating capabilities, especially when coupled with Business Intelligence.
CROSS-REFERENCE
Chapter 5 (“Differences Between Business Intelligence and Data Science”) discusses the differences between Business Intelligence and data science and how data science can complement your Business Intelligence organization. Chapter 6 (“Data Science 101”) reviews several different analytic algorithms that your data science team might use and discusses the business situations in which the different algorithms might be most appropriate.
Don't Think Data Warehouse, Think Data Lake
In the world of big data, Hadoop and HDFS is a game changer; it is fundamentally changing the way organizations think about storing, managing, and analyzing data. And I don't mean Hadoop as yet another data source for your data warehouse. I'm talking about Hadoop and HDFS as the foundation for your data and analytics environments—to take advantage of the massively parallel processing, cheap scale-out data architecture that can run hundreds, thousands, or even tens of thousands of Hadoop nodes.
We are witnessing the dawn of the age of the data lake. The data lake enables organizations to gather, manage, enrich, and analyze many new sources of data, whether structured or unstructured. The data lake enables organizations to treat data as an organizational asset to be gathered and nurtured versus a cost to be minimized.
Organizations need to treat their reporting environments (traditional BI and data warehousing) and analytics (data science) environments differently. These two environments have very different characteristics and serve different purposes. The data lake can make both of the BI and data science environments more agile and more productive (Figure 1.2).
Figure 1.2 Modern data/analytics environment
CROSS-REFERENCE
Chapter 7 (”The Data Lake“) introduces the concept of a data lake and the role the data lake plays in supporting your existing data warehouse and Business Intelligence investments while providing the foundation for your data science environment. Chapter 7 discusses how the data lake can un-cuff your data scientists from the data warehouse to uncover those variables and metrics that might be better predictors of business performance. It also discusses how the data lake can free up expensive data warehouse resources, especially those resources associated with Extract, Transform, and Load (ETL) data processes.
Don't Think “What Happened,” Think “What Will Happen”
Business users have been trained to contemplate business questions that monitor the current state of the business and to focus on retrospective reporting on what happened. Business users have become conditioned by their BI and data warehouse environments to only consider questions that report on current business performance, such as “How many widgets did I sell last month?” and “What were my gross sales last quarter?”
Unfortunately, this retrospective view of the business doesn't help when trying to make decisions and take action about future situations. We need to get business users to “think differently” about the types of questions they can ask. We need to move the business investigation process beyond the performance monitoring questions to the predictive (e.g., What will likely happen?) and prescriptive (e.g., What should I do?) questions that organizations need to address in order to optimize key business processes and uncover new monetization opportunities (see Table 1.2).
Table 1.2 Evolution of the Business Questions
What Happened?(Descriptive/BI)
What Will Happen?(Predictive Analytics)
What Should I do?(Prescriptive Analytics)
How many widgets did I sell last month?
How many widgets will I sell next month?
Order [5,0000] units of Component Z to support widget sales for next month
What were sales by zip code for Christmas last year?
What will be sales by zip code over this Christmas season?
Hire [Y] new sales reps by these zip codes to handle projected Christmas sales
How many of Product X were returned last month?
How many of Product X will be returned next month?
Set aside [$125K] in financial reserve to cover Product X returns
What were company revenues and profits for the past quarter?
What are projected company revenues and profits for next quarter?
Sell the following product mix to achieve quarterly revenue and margin goals
How many employees did I hire last year?
How many employees will I need to hire next year?
Increase hiring pipeline by 35 percent to achieve hiring goals
CROSS-REFERENCE
Chapter 8 (“Thinking Like a Data Scientist”) differentiates between descriptive analytics, predictive analytics, and prescriptive analytics. Chapters 9, 10, and 11 then introduce several techniques to help your business users identify the predictive (“What will happen?”) and prescriptive (“What should I do?”) questions that they need to more effectively drive the business. Yeah, this will mean lots of Post-it notes and whiteboards, my favorite tools.
Don't Think HIPPO, Think Collaboration
Unfortunately, today it is still the HIPPO—the Highest Paid Person's Opinion—that determines most of the business decisions. Reasons such as “We've always done things that way” or “My years of experience tell me …” or “This is what the CEO wants …” are still given as reasons for why the HIPPO needs to drive the important business decisions.
