Data (art &) Science
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This document discusses how combining data science (machine processing) with human expertise (art) can create strategic capabilities for businesses. It argues that some processes benefit from both approaches through systematization, which allows for scale, specialization, feedback, and controlled variation. Building an effective capability requires the right people, processes, and technology, including data scientists, experimental design, custom applications, and APIs. If done correctly, such a combined capability could become a "power" that delivers superior and sustainable returns.
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Data (art &) Science
- 1. 1 Data (art &) Science Strata + Hadoop World 2015 Contact: Eric Colson | ecolson@stitchfix.com Feb 2015
- 2. 2
- 4. 4 Different Capabilities • Executing supervised and unsupervised learning algorithms • Execution of complex calculations • Discern between different and significantly different • Estimate and apply multivariate weights • … etc. • Cognitive abilities • Access to additional info • Powerful distinctions Let’s call this “Art” Let’s call this “Science” Machine Processing Expert-Human Processing
- 6. 6 1 2 3
- 7. Algorithms: our differen:a:ng asset • 35% of Amazon sales are driven from recommendations • 50% of LinkedIn connections are driven by recommendations • 75% of Netflix videos watched are from recommendations • 100% of Stitch Fix merchandise is sold by recommendations
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- 9. Scaling
- 10. 10 Benefits of Systematizing 1. Scale 2. Specialization 3. Feedback 4. Controlled Variation
- 11. 11 Feedback for more Learning
- 12. random() 12 Controlled Variation for more Exploration random()
- 13. Generalizing 13
- 14. 14 Common business processes Systematize when: • Both Art & Science are required • The process can benefit from • Scale • Specialization • Feedback • Controlled Variation Content Produc:on Pricing Partner Management Fraud Detec:on Etc. Merchandising
- 15. 15 Building the capability Process Technology People
- 16. 16 People Skills: • Frame the optimization problem • Train & implement algorithms • Design systems, processes, and workflows • Write production code • Communicate beautifully • Design experiments & measurement • Evangelize adoption • …etc. 1. From Dan Pink’s book, “Drive” They do exist. The horns are not always visible in early development. Grow them with big roles that provide autonomy, mastery, purpose1 Data Scientists need to become Data Artists.
- 17. 17 Process 1. Form a strong partnership between data science and functional team 2. Make them accountable 3. Practice proper experimental design
- 18. 18 18 Technology f (Ÿ) algorithms Custom App 1. Build custom solutions 2. Build APIs 3. Invest in a logging pipeline
- 19. 19 Enable a ‘Power’1 A capability becomes a “Power” if: 1. It enables Superior returns 2. The returns are Significantly differentiating 3. The capability is Sustainable (hard to copy) 1. See works of Hamilton Helmer, Stanford Consulting Professor, https://economics.stanford.edu/faculty/helmer
- 20. 20 Summary • Expert human judgment (Art) and machine processing (Science) contribute differently to strategic capabilities • Some processes call precisely for their combination • Systematizing gets you: Scale, Specialization, Feedback, Controlled Variation • Building the capability requires the right people, process, technology • The capability can enable a ‘power’