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CC Talk at Berekely

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A talk on Data Science Leadership given at UC Berkeley

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CC Talk at Berekely

  1. 1. calculation | consulting data science leadership (TM) c|c (TM) charles@calculationconsulting.com
  2. 2. calculation|consulting Data Science Leadership (TM) charles@caclulationconsulting.com
  3. 3. calculation | consulting data science leadership Who Are We? c|c (TM) Dr. Charles H. Martin, PhD University of Chicago, Chemical Physics NSF Fellow in Theoretical Chemistry ! Over 10 years experience in applied Machine Learning Developed ML algos for Demand Media; the first $1B IPO since Google ! ! Lean Start Ups: Aardvark (acquired by Google), eHow Wall Street: BlackRock Fortune 500: Big Pharma, Telecom, eBay, … ! www.calculationconsulting.com charles@calculationconsulting.com (TM) 3
  4. 4. BackStory: in 2011, Search Changed. Forever. • first $1B IPO since Google • Machine Learning based SEO algorithms • Measure the demand for search, and fulfill it ! data science algorithms created a billion $ company c|c (TM) (TM) Demand Media calculation | consulting data science leadership(TM) 4 eHow.com
  5. 5. BackStory: in 2011, Search Changed. Forever. • Google adapted (Panda) • Lack of diversification • Lack of adaptation • Stock price never recovered ! algorithms without accountability: DMD or Google? c|c (TM) IPO Panda stock price 2011-2012 (TM) calculation | consulting data science leadership DMD (TM) 5
  6. 6. • first $1B collapse due to Panda ? • CPC revenues down • premium online publishers died collapse ? stock price 2011-2012 c|c (TM) $1B in ad revenue was repriced and reallocated Problem: Cornering the market on search induced a market crash calculation | consulting data science leadership(TM) 6
  7. 7. Organic Traffic Revenue / Margins Panda-Induced ‘Market Crash’ WebMD traffic up, margins negative traffic increased, yet revenues tanked c|c (TM) calculation | consulting data science leadership(TM) 7
  8. 8. c|c (TM) Panda-Induced ‘Market Crash’ Google CPC dropped just after Panda calculation | consulting data science leadership(TM) 8
  9. 9. a Panda-Induced ‘Market Crash’ Like Algo-Induced Stock Market Crashes • Black Monday 1987 repriced the implied vol curve (i.e. smile) • LCTM exploited fixed income arbitrage • Gaussian-Copula model enabled the housing market crash • eHow ML algos led to Google Panda c|c (TM) calculation | consulting data science leadership(TM) 9
  10. 10. Problem: Data Science is Different “When analytics are this important, they need senior management oversight” c|c (TM) Davenport Thomas H. Davenport calculation | consulting data science leadership ! Generating sustainable revenue requires Data Science Leadership and Execution (TM) 10
  11. 11. Problem: Big Data does not, by itself, yield Big Revenues (TM) c|c (TM) • Hadoop everywhere; ROI lacking • Hadoop is a cost center • ROI needs cut across business divisions • Engineering process is not the scientific process ! ! Algorithms, not data, generate revenue calculation | consulting data science leadership 11
  12. 12. c|c (TM) (TM) Problem: Algorithmic Accountability calculation | consulting data science leadership ! ! An asset is an economic resource. ! Anything tangible or intangible that is capable of being owned or controlled to produce value and that is held to have positive economic value is considered an asset. ! ! algorithms can be valuable assets (and have unforeseen liabilities) 12
  13. 13. Demand Algos: Gas Station Analogy Problem: where to open a gas station ? Need: good traffic, weak competition c|c (TM) less competitors no traffic sweet spot great traffic too many competitors calculation | consulting data science leadership ! ! all businesses balance supply and demand (TM) 13
  14. 14. c|c (TM) ! • Cross-functional engineering, product, marketing, finance • Autonomous: separate from the traditional engineering product lifecycle. self-organizing and self-managing • Experimental: form hypothesis, analyze data, make predictions, run backtests, A/B testing • Self-sustaining: not a cost center; generates revenue (TM) Data Science is Different calculation | consulting data science leadership 14
  15. 15. Managing: Data Science Process • Acquire Domain Knowledge • Formulate Hypothesis • Generate Model(s) from the Data • Predict Revenue Gains • Backtest Predictions on your Data • A/B Test in Production • Attribute Gains to Model(s) c|c (TM) (TM) acting solving framing calculation | consulting data science leadership 15
  16. 16. c|c (TM) ! • Systems Thinking: leveraging the inter-relationships between data, marketing, and the customer • Knowledge Transfer: mentoring — not training — to develop both personal mastery and team learning • Mental Models: create a base of small-scale models for thinking about how to use your data • Knowledge Sharing: foster collaboration between research, engineering, and product to drive revenue Managing: Learning from Data calculation | consulting data science leadership(TM) 16
  17. 17. (TM) c|c (TM) c | c charles@calculationconsulting.com

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