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

1. APPLIED DATA SCIENCE
Giovanni Lanzani – Chief Science
Officer
GoDataDriven
@gglanzani

2. WHO AM I
Italy
01
Leiden
University
02
KPMG
03
GoDataDriven
04

4. WHAT IS MACHINE LEARNING

5. LEARNING
FROM DATA
• You have some (lots) of
data
• You need to generalize

6. 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!!!

7. WHAT’S DATA SCIENCE

8. ULTIMATELY
• It’s about creating value from data
• Using Machine Learning, Advanced Analytics, and
visualization

9. 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

10. HOW TO GET THERE

11. TRADITIONAL DATA
WAREHOUSE
ARCHITECTURE
EDW
Data consumer
Web app
Dashboard /
Reporting
Traditional
Business app

12. AND NOW?
?
Data consumer
Web app
Dashboard /
Reporting
Traditional
Business app
API

13. 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

14. BUT…
• We got a data scientist working on trees, and forests
• Neural networks!
• Deep learning!!!

15. WHAT DO COMPANIES ACTUALLY NEED
• Put things into production
• They don’t teach that in any
data science course or MOOC
(that I know)

16. THE THREE
HURDLES
Credit to Jon Shave gdd.li/lavaredo

17. OVERSIMPLIFYING
Requirements
Data
Sources
Exploration
Modeling
Products
Feedback
Data scientist ML
engineer
Data
engineer
Data
engineer
🤦🤦♀️
🤦🤦
Customers

18. 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

19. SOLVING
THEM

20. BUSINESS CASE
Business case for
• True Positives
• True Negatives
Cost of
• False Positives
• False Negatives

21. DATA
Data {insert
something here}
should be pro grade

22. 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

23. 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

24. TAKEAWAYS
• POs should know “their stuff”
• Automate all the data movements
• Hire data scientists that are good at programming (or hire machine
learning engineers)

25. QUESTIONS?
• We’re hiring
• Data & Machine Learning Engineers!
• career@godatadriven.com