© 2019–2022 by Adam Braff
Introduction
1.1 Problem definition
1.2 Use-case prioritization
1.3 Business intelligence
1.4 Cross-industry borrowing
1.5 Alternative data
1.6 Scaling up innovations
1.7 Data creation
1.8 Data monetization
1.9 Data governance
1.10 Future-proofing analytics
1.11 Privacy regulations
1.12 Outsourcing analytics
1.13 Defunct tools
1.14 Predictive analytics
1.15 Customer experience
1.16 Overfitting
2.1 Betting as training
2.2 Honest forecasting vs. extremism
2.3 Updating on new facts
2.4 Mitigating risk
2.5 Distinguishing luck from skill
2.6 Political forecasting
2.7 Teaming up
2.8 Managing uncertainty
2.9 Betting revisited
2.10 Algorithms vs. experts
2.11 Managing underconfidence
2.12 Forecasting crime
2.13 Calibration and error correction
2.14 Diverging forecasts
2.15 Lessons, part I
2.16 Lessons, part II
3.1 Hiring faster
3.2 Hiring ultracreatives
3.3 Office vs. remote work
3.4 The hybrid model
3.5 Translating analytics to business
3.6 Fighting cognitive bias
3.7 Fighting political bias
3.8 Spontaneity
3.9 The analyst’s career
4.1 Choosing and using frameworks
4.2 Grand theories of the Universe
4.3 Organizing for competitive advantage
4.4 Segmentation
4.5 Advanced business intelligence
4.6 Unglamorous use cases
4.7 Overoptimization
4.8 Speed vs. accuracy
4.9 Adaptability
Questions for Chapter 1
Questions for Chapter 2
Questions for Chapter 3
Questions for Chapter 4
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adam@braff.co
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