key failure factors of building a data scientist team

Data Scientist & Consultant 趙國仁 Craig Chao chaocraig@gmail.com

建立資料科學團隊的關鍵失敗因素

The KFF of building a Data Science team�

Prelog - CheuYuWhy

Prelog – Style & Fit

Agenda

•  Current Stat. of (Big) Data Science •  KFF of building a data science team

– Add data scientists – Analysis with reports than models – Political conflicts – Sales judgment

•  Some directions

Current Stat. of (Big) Data Science

Source: Capgemini Consulting, “Big Data Survey”, November 2014.

Current Stat. of (Big) Data Science

Current Stat. of (Big) Data Science

Source: Capgemini Consulting, “Big Data Survey”, November 2014.

Current Stat. of (Big) Data Science

Source: Capgemini Consulting, “Big Data Survey”, November 2014.

Mgmt & Org.

KFF1: Add Data Scientists Reach & Richness

UU Reach (DAU)

KFF1: Add Data Scientists

BD Sales + AS

Sales + CM

Data BD

Data Engineer + Data Scientist

Conversions + 3rd Tracking

KFF2: Analysis with reports than models

資料量大 資料多樣性

資料輸入 和處理速度快

資料真實性

Challenges of Big Data - 4V�

KFF2: Analysis with reports than models

Russ Merz, An Integrated Model of Media Satisfaction and Engagement: Theory, Empirical Assessment and Managerial Implications, Journal of Applied Marketing Theory, Nov 2011

BIG DATA

Hypotheses Machine Learning Data Mining

Machine-generated

All, Hyper space, …

Volume, Velocity, Variety, Veracity

deductive inductive

Cases

Models Models

Cases

KFF2: Analysis with reports than models

Segments Reports For Human

(Explanatory)

Models Data-driven Actions

Efficiency Intelligence Effectiveness Data Science is the art of turning data into actions.

KFF3: Political Conflicts Data Science Venn Diagram

Cross-functional data Performance attribution

Analysts

Legacy integration

CTO / Tech lead

KFF4: Sales Judgment •  Data late effect

– After a data management system – After data links and accumulation – After experiments & optimization

•  Pricing by CPM/CPC vs CPI/CPS •  Career path of sales head •  Positioning & Orientation

– Product companies: Apple, Google, AppNexus – Marketing companies: Microsoft, Uber – Sales companies: Oracle, SAP…

Some Directions •  Planned with Fail Fast •  Full functions with a Data Lab

– Develop operational data systems first? •  Secure funding •  Strong talents/Trust

– Experimenter with Stat/MLDM

•  A strong leader

World, Model & Theory

Credit: John F. Sowa

Summary - Model

Summary - Innovation

謝謝大家！

chaocraig@gmail.com