This document summarizes a talk given by Wassel Alazhar about lessons learned from a failed data science project. The key points are: 1) The project aimed to use data science to improve performance at two similar power plants but did not identify any actionable findings, instead delivering unnecessary features. 2) Several mistakes were made, including not properly defining the business problem, focusing on building software instead of delivering value, and failing to get user feedback. 3) Successful data science projects explore business problems through iterative experimentation and user feedback before building products, with the goal of steadily providing value from data. 4) Agile practices like collaboration, automation, and rapid feedback are still important for data

1. Dances with unicorns
Agile datascience
from exploration to adoption

2. Un grand merci à nos sponsors et partenaires

3. Oman 11.30 AM

4. Paris 9.45 AM

5. <24h
From user’s feedback to production

6. One month later...

7. This project has been
abandoned

8. That’s me!
Wassel Alazhar
Consultant, developer, problem solver
@wasselovski
https://github.com/jcraftsman

9. Agenda
● The full story
● What went wrong?
● What did we learn?
○ How to bring value from datascience
○ Explore and build
○ Efficient collaboration
○ Product quality
● Why this talk?
● Takeaways

10. The full story

11. A global energy leader

12. A global energy leader
Produce Deliver SELL

13. A global energy leader
Produce Deliver SELL
Sensors everywhere!
All along the value chain

14. The problem to solve
Produce Deliver SELL
Sensors everywhere!
All along the value chain

15. The problem to solve
Two new generation power plants
They are exactly the same
but...
Twin A Twin B

16. The problem to solve
Twin B is way more performant (i.e., makes money)
Twin A Twin B

17. The solution
Datascience can
help identifying
better operational
models for the
power plants

18. The solution
BIG DATA
+
DATA SCIENCE
=
MAGIC

19. The partner

20. The partner
A unicorn is a privately held startup company valued at over
$1 billion

21. The bill
But wait…
How much is that?
Nevermind.
It’s all on me!
It’s called innovation.
Great!

22. The team
Data engineers Data scientists App developers

23. To the Silicon valley
Data engineers Data scientists App developers

24. Week after week… Demo after demo

25. It couldn’t be any better

26. SURPRISE
Now, it’s all yours!
All you have to pay
for is the run.
Oh!
No, thanks.
I’m out of it.

27. Deception

28. What went wrong?

29. Building a software!

30. What was the Problem to solve?
Do you remember the twin power plants?
Twin A Twin B

31. Not what we’ve expected...
Problem solved explained quickly
No actionable findings

32. Instead we have delivered features!
Degradation analysis
Anomaly detection
The software can detect dust in the steam turbine!
PCA???

33. Feature
≠
VALUE

34. What did we learn?

35. Happy ending stories...
Predictive maintenance
Smart buildings
Ice detection
Heating and cooling efficiency

36. Business use case discovery
Don’t start with a software!
Explore
Observe
Confirm hypothesis or not!
Discover

37. Business use case discovery
Don’t explore in a dark lab!
Get feedback!

38. Business use case discovery
A python notebook is not a software!
It’s a tool for a study!

39. From study to product delivery
Business use case located?
Build!
VALUE

40. Product delivery
Not like this!

41. Wait, what does datascience look like in 2018?
How would you write a program for puppy recognition?

42. Wait, what does datascience look like in 2018?
You can:
● Try to define what a puppy face is
● Code all these rules!
Or, use Machine learning:
● Show a lot of puppy faces examples!
You don’t need to tell the algorithm what to do.
All you need is to show it a lot of examples!

43. Wait, what does datascience look like in 2018?
Take care of your examples (data pipeline)
Verify the results (predictions)

44. Putting it all together
Discovery:
Given a real world pictures sample, would it be possible to
recognize a puppy face?
The answer is 86% yes, 13% muffins, 1% unknown.
Product:
Play a dog kibble comercial whenever a puppy picture is
displayed!

45. Explore and build
Explore:
● Gathering data
● Cleaning data
● Feature engineering
● Defining model
● Training
● Predicting the output
=> Discover what you are able to do
with your data
Build:
● Data acquisition
● Data filtering
● Use model configuration
● Use model
● Training (or use a train set)
● Predicting the output
=> Steadily bring value from your
data

46. Explore and build iteratively
Explore:
● Gathering data
● Cleaning data
● Feature engineering
● Defining model
● Training
● Predicting the output
=> Discover what you are able to do
with your data
Build:
● Data acquisition
● Data filtering
● Use model configuration
● Use model
● Training (or use a train set)
● Predicting the output
=> Steadily bring value from your
data

47. Explore and build iteratively
Explore Build
Business
use case
discovery
Product delivery

48. Product delivery
You’re not done with datascience!
They should build together!

49. Building together
Code review
When? All the time!
Who? Everyone!
Why? Quality, collective
ownership and joy!

50. Building together
Pair programming
When? All the time!
Who? Everyone!
Why? Quality, collective
ownership and joy!

51. Building together
Mob programming
When? Whenever you start
something new or complex.
Who? Everyone!
Why? Collective
intelligence, collective
ownership, quality and joy!

52. Building together
TDD
Let’s be serious!
When? Whenever you change the
product’s behaviour.
Who? Everyone working on the product!
Why? Collective intelligence,
collective ownership, quality and joy!

53. Building together
TDD
Have you ever met a data
scientist who write unit tests
and refactor? I did! :)
It’s hard to imagine doing TDD
during an exploratory work though!
(i.e., when the target observable
behaviour is not yet defined)

54. Product delivery
Spikes and user stories

55. Product delivery essentials
Don’t lose time repeating boring stuff!
Automate!
Make data available for everyone!
Don’t treat your infra like pets!
Destroy and rebuild!
Don’t over-engineer though!

56. Product adoption
Stay close to the users!
Don’t plan too many features!
Incorporate feedback!

57. What is agile anyway?

58. Can datascience be agile?
It’s still true!
Even for:
● Big data
● AI
● Datascience

59. Why this talk?

60. Myths about datascience
Well… Things have slightly changed since then… But not that much!

61. Myths about datascience

62. Myths about datascience

63. Unicorns

64. New unicorns - Same old stories
You should draw your entire
model before you start coding!
Open a ticket!
You need to hire a machine
learning engineer!

65. Takeaways!

66. Takeaways!
Make people together!
Business value discovery => product delivery
Explore and build iteratively
Agile is still:
● Short feedback
● Small increments
● Take engineering seriously
work
learn

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