How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
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The document discusses the challenges and strategies involved in deploying data science platforms, with emphasis on the need for effective infrastructure, skilled personnel, and organizational structures. It highlights common issues that lead to project failures and presents various approaches to data project execution, including collaborative efforts and leveraging existing data sources. Additionally, it touches on the importance of integrating product management with data teams to enhance decision-making and automate processes.
How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
- 2. Hi ! I’m FLORIAN DOUETTEAU, CEO of Dataiku x 54 + x 1+ + 58 ++ It’s Me !! It’s our software !!
- 3. …and our software is The most complete Data Science platform Deployment
- 4. Dataiku - Data Tuesday Meet Hal Alowne Big Guys • 10B$+ Revenue • 100M+ customers • 100+ Data Scientist Hal Alowne BI Manager Dim’s Private Showroom Hey Hal ! We need a big data platform like the big guys. Let’s just do as they do! ‟ ”Average E-commerce Web site • 100M$ Revenue • 1 Million customer • 1 Data Analyst (Hal Himself) Dim Sum CEO & Founder Dim’s Private Showroom Big Data Copy Cat Project
- 7. LOL PLATFORM ANTI-PATTERN Test and Invest in Infrastructure == Skilled People or Go For Cloud / Packaged Infrastructure Your Brand New Hadoop Cluster is perceived as slow, not so used and not reliable
- 8. TECHNO MISMATCH ANTI-PATTERN Assume Being Polyglot or Be a Dictator VS VS The Python Clan The R Tribe The Old Elephant Fraternity The New Elephant Club
- 9. PREDICTIVE ANALYTICS DEPLOYMENT STRATEGY Website 2000’ winners Companies that were able to release fast "Artificial Intelligence with Data for Internet of Things" 2010’ winners Companies able to put intelligence in production ? Design a way to put “PREDITICTIVE MODELS” IN PRODUCTION
- 11. Classic Business Intelligence Team Organization Business Leader Data Consumer Line-of-business Data Consumer Business Project Sponsor BI Solution Architect Model Designer ETL Developer Dashboard / Report Designer DBA / IT Data Owner Specs
- 12. Data Science Team Organization Business Leader Data Consumer Line-of-business Data Consumer Business Project Sponsor Data Team Manager Data Engineer Data Analyst Data System Engineer / Data Architect Specs Data Scientist
- 13. Built From Scratch Business Leader Data Consumer Line-of-business Data Consumer Business Project Sponsor DBA / IT Data Owner Specs DATA SCIENTISTS EVERYWHERE
- 14. Built From Engineering Business Leader Data Consumer Line-of-business Data Consumer Business Project Sponsor Specs DATA ENGINEERS DATA ANALYSTS
- 15. Built From Analysts Business Leader Data Consumer Line-of-business Data Consumer Business Project Sponsor Specs
- 17. Perfectly Natural Hidden thoughts Business Project Sponsor Data Team Manager Data Engineer Data Analyst Data Scientist
- 18. Managing Extreme Personalities Data SCIENTIST Highly Creative Passionate Hard to hire ? Hard to manage ? Want to take your job ? Ambitious
- 19. Paired for Data Data Analyst Discover Patterns Data Engineer Make things work Fight data entropy Entropy tech entropy
- 20. When do you prefer ? One Analyst One Engineer One Data Scientist That work together ? Four data scientists
- 22. What is the main reason for data project to fail ? DATA NOT AVAILABLE
- 23. BUT FOR ONLY INCREMENTAL GAIN 50 30 20 0% 25% 50% 75% 100% Contribution to the overall project performance Business Goal Definition and Data Feature Engineering Algorithm
- 24. How to Get Data if you don’t have it THE GRASSHOPER THE SPIDER THE FOX
- 26. The Cicada : Optimistic and Opportunistic Data THE CICADA As a startup As a group inside a company - Build a new product using open data - Benefit from the data sharing initiative within your company - Wait for data to be available in your data lake
- 27. The Spider: Power of the Network THE SPIDER As a startup As a group inside a company - Create a network of (web trackers | sensors) - Make it available for free - Build your service on people’s collected data - Make a web service available to collect data - Promote it internally so that people use it
- 28. The Fox: Hunt for the Big Money first THE FOX As a startup As a group inside a company - Hunt for a Business Group within a large company with a problem - Build a SaaS solution using their data - Replicate to competitors - Take in a charge a critical problem as per the CEO’s request - Build your own integrated tech team to solve it - Use those ressources to reset data services internally
- 30. What is Big Data about ?
- 31. The Age Of Distributed Intelligence Global, Personalised and Real Time Data Driven Services
- 32. Data to Visualize or Data to Automate ? 2013 2014 2015 2016 2017 2018 Automated Decision VIsualize To Decide Moving to a world of automated decision making
- 33. Involve product team Product Feature Personalised Item Ranking Product Feature Notify User Only when Needed Product Feature: Historical Data For Path Optimisation Have Product Management Deeply Involved In the Data Team
- 34. Where is your added value ? Is the problem at the Core of my Business Process? Is it a common problem / with share data ? Go for Best of Breed SAAS Solution Can I Solve it on my own ? Really ? Build by the data team Build by the data team ? Build by the data team Hire Consultants and Learn Yes Yes No I can’t Ok, I can try Yes! No! No
- 35. Be aware of the confort zone Mission Critical Small Structured Large Diverse Sheer Curiosity Reporting for Finance in Any Industry Analyze Each Tweet Web Navigation For E-Merchant Ticket Data For Discounts in Retail Phone Call Logs for Security RTB Data For Advertising Customer Consumption For Anti-Churn in Utilities Optimization Filings For Fraud in Insurance Not Enough Data To Learn From ? Not Enough “Hard" Examples So that you can learn
- 36. Create an "API" Culture Do not share • Random Piece of Code • Flat File Do share • Reproductible documented workflows • Clean, documented APIs
- 37. Food for thoughts www.dataiku.com/blog Free Data Science Software www.dataiku.com/dss THANK YOU ! Data Science Is no longer a science
Editor's Notes
- #3: What do we do ? We help insurers develop new analytic data-driven products and platform withouth having to pay any technical debt from leaving already existing platform (that comes from the 90s) We help people build Data Labs