Winning with Data
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Bien Frank. Winning with Data
Introduction
Chapter 1. Mad Men to Math Men: The Power of the Data-Driven Culture
Operationalizing Data: Uber's Competitive Weapon
The Era of Instant Data: You Better Get Yourself Together
Data Supply Chains: Buckling Under the Load
Management by Opinion: The Illusion of Knowledge
Our Vantage Points
Chapter 2. Four Problems with Data Today: Breadlines, Obscurity, Fragmentation, and Brawls
Data Breadlines for the Data-Poor
Data Obscurity: The Failure of the Card Catalog
Rogue Databases and Analysts: The Data Fragmentation Problem
Data Brawls: When Miscommunication Devolves into Arguments
Chapter 3. Business Intelligence: How We Got Here
Business Intelligence Is Born: The First Query
Databases for the Masses: Oracle Commercializes Codd's Invention
Legacy BI: A Three-Layer Cake
Google's Answer to Huge Data: Vanilla Boxes
600 Petabytes per Day: HiPal at Facebook
Extreme Data Collection: The New Normal
Looker: Weaving the Data Fabric
Chapter 4. Achieving Data Enlightenment: Gathering Data in the Morning and Changing Your Business's Operations in the Afternoon
Not Just Another Person with an Opinion
Aligning Sales Teams in Real Time
Scaling Sales Teams with Data
Determining Customer Satisfaction at Every Point in the Buyer Journey
The Rosetta Stone: Developing a Shared Data Language
The One Equation That Defines the Business
Brutal Intellectual Honesty: Speaking Data to Power
Putting Pride in Its Place: How Data Transforms Cultures
Chapter 5. Five Steps to Creating a Data-Driven Company – From Recruiting to Regression, It All Starts with Curiosity: Changing the Culture
It All Starts with Curiosity
Why You Should Stop Listening to Your Boss
How to Recruit Curious People
Chapter 6. From Hacks to Harmony: The Typical Progression of Data-Driven Companies
Step 1: Ask Your Friend, the Engineer
Step 2: Bastardize an Existing Solution
Step 3: Access Raw Data
The Crux of the Problem
Bring Your Own BI: The Five Letters That Will Change the Data World
The Power of a Unified Data-Modeling Layer
The Final Step: A Data Fabric
Chapter 7. Data Literacy and Empowerment: The Core Responsibilities of the Data Team
The Illusion of Validity: How to Avoid Data Biases
Correlation versus Causation
How Facebook and Zendesk Engender Data Literacy
Walking the Data Gemba: Training by Walking Around
Chapter 8. Deeper Analyses: Asking the Right Questions
When Data Confounds Our Intuition: How to Handle Ambiguity
Data Is Useless Unless You Can Act On It
Defining New Opportunities by Creating New Metrics That Matter
The Fastest Growing Media Site of All Time
How to Run a Data-Backed Experiment: Step by Step
Chapter 9. Changing the Way We Operate
Change Begins with a Story
Deliver Data with Panache: Structuring Presentations to Inspire
Chapter 10. Putting It All Together
Acknowledgments
Appendix. Revenue Metrics
Business Revenue Metrics
Engagement Metrics
Distribution Metrics
Index
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Отрывок из книги
Silicon Valley owes its existence to a Frenchman living in Boston. Born in France in 1899, Georges Doriot graduated from the University of Paris in 1920 and matriculated at the Harvard Business School in 1921. Four years after graduation, he became the assistant dean and associate professor of industrial management at Harvard.1 Five years later, he would be promoted to full professor, in large part due to his beloved manufacturing course that graduated more than 7,000 students during his tenure through 1966. The year-long course tested the general management skills of second-year MBA students, and the final reports of students often exceeded 600 pages.2 In Creative Capital, Doriot biographer Spencer E. Ante summarized his interviews of former Doriot students:
A sinewy 5 feet 10 inches tall, with incisive blue eyes, a thin mustache, and a penchant for fine tobacco to stuff his iconic pipe, Doriot was highly decorated by the U.S. military. In 1940, he became a U.S. citizen to assume a military post created for him by a former student, Major General Edmund Gregory. Appointed lieutenant colonel and chief of the Military Planning Division, Doriot managed all the procurement for the U.S. Army, from trucks to uniforms to rations.
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Over the spumante, my wife asked which topics garnered the most interest. I didn't know the answer. So, I began to study the factors that attracted readers: title length, the number of subheadings, the presence of images, voice and tone, time of day to publish, and many others. I learned quite a bit.
I have 48 seconds with a reader. No pretty images, no witty title, no amount of social media validation from influencers will entice the reader to linger. Tweets sent at 8:54 to 8:59 a.m. Pacific Time generate 25 percent more views than those sent a few minutes after 9 a.m. But e-mail subscribers prefer to read content around 10 a.m., a nice midmorning break. Would e-mail readers like to read posts after lunch?, I wondered. A two-week experiment showed they most certainly did not! Open rates fell in half.
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