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Giovanni Lanzani GoDataDriven

Applied data science: where does it go wrong when developing data products?
Giovanni Lanzani

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Giovanni Lanzani GoDataDriven

  1. 1. APPLIED DATA SCIENCE Giovanni Lanzani – Chief Science Officer GoDataDriven @gglanzani
  2. 2. WHO AM I Italy 01 Leiden University 02 KPMG 03 GoDataDriven 04
  3. 3. WHAT IS MACHINE LEARNING
  4. 4. LEARNING FROM DATA • You have some (lots) of data • You need to generalize
  5. 5. BEST MODEL • Which one would you choose here? • It’s about making a tradeoff • This trade off is the most important job of the PO • A 100% correct answer might not exist!!!
  6. 6. WHAT’S DATA SCIENCE
  7. 7. ULTIMATELY • It’s about creating value from data • Using Machine Learning, Advanced Analytics, and visualization
  8. 8. WHEN YOU SAY DATA SCIENCE, COMPANIES UNDERSTAND • All the things big data • Predictive modeling & Advanced Analytics • More money • Do all the cool things the others are doing
  9. 9. HOW TO GET THERE
  10. 10. TRADITIONAL DATA WAREHOUSE ARCHITECTURE EDW Data consumer Web app Dashboard / Reporting Traditional Business app
  11. 11. AND NOW? ? Data consumer Web app Dashboard / Reporting Traditional Business app API
  12. 12. WHAT COMPANIES GOT • A lot of POCs • A lot of screenshots/presentations/dashboards on a laptop • Nice stories to tell to their network, about those screenshots and especially those dashboards • Headaches with data and infra even more scattered
  13. 13. BUT… • We got a data scientist working on trees, and forests • Neural networks! • Deep learning!!!
  14. 14. WHAT DO COMPANIES ACTUALLY NEED • Put things into production • They don’t teach that in any data science course or MOOC (that I know)
  15. 15. THE THREE HURDLES Credit to Jon Shave gdd.li/lavaredo
  16. 16. OVERSIMPLIFYING Requirements Data Sources Exploration Modeling Products Feedback Data scientist ML engineer Data engineer Data engineer 🤦🤦♀️ 🤦🤦 Customers
  17. 17. KAGGLE CURSE • gdd.li/toldYouSo • Many data scientists approach the problem at hand with a Kaggle-like mentality: delivering the best model in absolute terms, no matter what the practical implications are. • In reality it's not the best model that we implement, but the one that combines quality and practicality: a continuous balancing act • Netflix competition
  18. 18. SOLVING THEM
  19. 19. BUSINESS CASE Business case for • True Positives • True Negatives Cost of • False Positives • False Negatives
  20. 20. DATA Data {insert something here} should be pro grade
  21. 21. SKILLS • Participate in actually building production quality systems OR being proficient enough in R or python to hack together a prototype on a very small dataset? • Supply of the second group keeps growing while demand is flat or shrinking • Especially as executives get burned by “data scientists” who don't know how to help them build things of value
  22. 22. HIRING • Companies that are not engineering driven, often have trouble hiring good technical people • The “IQ” test is not really representative of applied data science • At GoDataDriven we do a “at home, at your convenience” assessment • Real dataset, real business question, real product • Models are software: treat them as such
  23. 23. TAKEAWAYS • POs should know “their stuff” • Automate all the data movements • Hire data scientists that are good at programming (or hire machine learning engineers)
  24. 24. QUESTIONS? • We’re hiring • Data & Machine Learning Engineers! • career@godatadriven.com

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