Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Key Failure Factors of Building a Data Science Team

1,526 views

Published on

建立資料科學團隊的關鍵失敗因素

Published in: Data & Analytics
  • Be the first to comment

Key Failure Factors of Building a Data Science Team

  1. 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. 2. Prelog – Style & Fit
  3. 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. 4. Current Stat. of (Big) Data Science Source: Capgemini Consulting, “Big Data Survey”, November 2014.
  5. 5. Current Stat. of (Big) Data Science
  6. 6. Current Stat. of (Big) Data Science Source: Capgemini Consulting, “Big Data Survey”, November 2014.
  7. 7. Current Stat. of (Big) Data Science Source: Capgemini Consulting, “Big Data Survey”, November 2014. Mgmt & Org.
  8. 8. KFF1: Add Data Scientists Reach & Richness UU Reach (DAU)
  9. 9. KFF1: Add Data Scientists BDSales + AS Sales + CM Data BD Data Engineer + Data Scientist Conversions + 3rd Tracking
  10. 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. 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. 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. 13. KFF3: Political Conflicts Data Science Venn Diagram Cross-functional data Performance attribution Analysts Legacy integration CTO / Tech lead
  14. 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. 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. 16. World, Model & Theory Credit: John F. Sowa
  17. 17. Summary - Model
  18. 18. Summary - Innovation
  19. 19. 謝謝大家! chaocraig@gmail.com

×