建立資料科學團隊的關鍵失敗因素

1. 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

2. Prelog – Style & Fit

3. 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

4. Current Stat. of (Big) Data Science
Source: Capgemini Consulting, “Big Data Survey”, November 2014.

5. Current Stat. of (Big) Data Science

6. Current Stat. of (Big) Data Science
Source: Capgemini Consulting, “Big Data Survey”, November 2014.

7. Current Stat. of (Big) Data Science
Source: Capgemini Consulting, “Big Data Survey”, November 2014.
Mgmt & Org.

8. KFF1: Add Data Scientists
Reach & Richness
UU Reach (DAU)

9. KFF1: Add Data Scientists
BDSales + AS
Sales + CM
Data BD
Data Engineer +
Data Scientist
Conversions +
3rd
Tracking

10. 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

11. 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

12. 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.

13. KFF3: Political Conflicts
Data Science Venn Diagram
Cross-functional data
Performance attribution
Analysts
Legacy integration
CTO /
Tech lead

14. 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…

15. 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

16. World, Model & Theory
Credit: John F. Sowa

17. Summary - Model

18. Summary - Innovation

19. 謝謝大家！
chaocraig@gmail.com