key failure factors of building a data scientist team
TRANSCRIPT
Data Scientist & Consultant 趙國仁 Craig Chao [email protected]
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
