CC Talk at Berekely

calculation | consulting data science leadership

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calculation|consultingData Science Leadership

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[email protected]


Who Are We?

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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 [email protected]

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mailto:[email protected]

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

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Demand Media

calculation | consulting data science leadership(TM)

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eHow.com

http://eHow.com

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?

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IPO

Panda

stock price 2011-2012

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DMD

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• first $1B collapse due to Panda ?

• CPC revenues down

• premium online publishers diedcollapse

?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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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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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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Problem: Data Science is Different

“When analytics are this important, they need senior management oversight”

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Thomas H. Davenport

Thomas H. Davenport


!Generating sustainable revenue requires Data Science Leadership and Execution

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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

!!

Algorithms, not data, generate revenue

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Problem: Algorithmic Accountability


!!

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)

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Demand Algos: Gas Station AnalogyProblem: where to open a gas station ?Need: good traffic, weak competition

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less competitors no trafficsweet spotgreat 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


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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[email protected]

CC Talk at Berekely

Business

Transcript of CC Talk at Berekely