A talk on Data Science Leadership given at UC Berkeley

1. calculation | consulting
data science leadership
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charles@calculationconsulting.com

2. calculation|consulting
Data Science Leadership
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charles@caclulationconsulting.com

3. calculation | consulting data science leadership
Who Are We?
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Dr. Charles H. Martin, PhD
University of Chicago, Chemical Physics
NSF Fellow in Theoretical Chemistry
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Over 10 years experience in applied Machine Learning
Developed ML algos for Demand Media; the first $1B IPO since Google
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Lean Start Ups: Aardvark (acquired by Google), eHow
Wall Street: BlackRock
Fortune 500: Big Pharma, Telecom, eBay, …
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www.calculationconsulting.com
charles@calculationconsulting.com
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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
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data science algorithms created a billion $ company
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Demand Media
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eHow.com

5. BackStory: in 2011, Search Changed. Forever.
• Google adapted (Panda)
• Lack of diversification
• Lack of adaptation
• Stock price never recovered
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algorithms without accountability: DMD or Google?
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IPO
Panda
stock price 2011-2012
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DMD
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6. • first $1B collapse due to Panda ?
• CPC revenues down
• premium online publishers died
collapse
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stock price 2011-2012
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$1B in ad revenue was repriced and reallocated
Problem: Cornering the market on
search induced a market crash
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7. Organic Traffic
Revenue / Margins
Panda-Induced ‘Market Crash’
WebMD traffic up, margins negative
traffic increased, yet revenues tanked
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Panda-Induced ‘Market Crash’
Google CPC dropped just after Panda
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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
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10. Problem: Data Science is Different
“When analytics are this important,
they need senior management oversight”
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Davenport
Thomas H. Davenport
calculation | consulting data science leadership
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Generating sustainable revenue requires
Data Science Leadership and Execution
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11. Problem: Big Data does not,
by itself, yield Big Revenues
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• Hadoop everywhere; ROI lacking
• Hadoop is a cost center
• ROI needs cut across business divisions
• Engineering process is not the scientific process
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Algorithms, not data, generate revenue
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Problem: Algorithmic Accountability
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An asset is an economic resource.
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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.
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algorithms can be valuable assets
(and have unforeseen liabilities)
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13. Demand Algos: Gas Station Analogy
Problem: where to open a gas station ?
Need: good traffic, weak competition
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less competitors
no traffic
sweet spot
great traffic
too many competitors
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all businesses balance supply and demand
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• 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
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Data Science is Different
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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)
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acting
solving
framing
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• 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
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charles@calculationconsulting.com