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DATA PRODUCTS: 5 DEADLY SINS AND
HOW TO PREVENT THEM
Pride Wrath Lust Gluttony Sloth
Mathieu Bastian

Web Summit 2015, Dub...
ABOUT ME
• Data scientist & engineer
• Led data products team at LinkedIn
• Gephi co-founder
• Open-source contributor
2
DATA PRODUCTS
Source: http://bit.ly/1kMUPAe.
Tentative definition
User-facing production system
based on an automated learn...
DATA PRODUCTS
TODAY
4
PRIDE
"Excessive belief in one’s own abilities or
excessive love of oneself"
5
PRIDE
Source: http://www.themeasurementstandard.com/wp-content/uploads/2015/06/data-scientist-as-superman.jpg
6
With power comes responsibility
7
Source: http://www.economist.com/node/15579717
Who are you building it for?
Understand user intent
Integrate into the user flow
Explain recommendations to the user
Set ri...
Anticipate edge cases
9
WRATH
“Choice of violent and hateful actions
over love and patience"
10
WRATH
11
Exercise perseverance
Reward
Time
Phase II:
Growth
Phase III:
Maintenance
Phase I:
Inception
12
But have a plan
13
LUST
"Depraved thought, unwholesome
morality and desire for excitement"
14
LUST
Credits: Google Data Center
15
Perform due diligence
16
Thank the janitor & handyman
17
GLUTTONY
"The consumption of more of anything
than you need"
18
GLUTTONY
19
Avoid solo data scientists
20
Credits: Lucasfilm
Choose the right problem
M - Measurable
E - Explainable
R - Rapid prototyping
C - Core
I - Iterable
21
SLOTH
"Not caring about others or living life in a
fulfilling way"
22
SLOTH
23
Embrace continuous data pipelines
Source: http://http://azkaban.github.io/
24
Make data pipelines robust
Code
Upload
Run
workflow
Look at
logs
Code Upload
Run
workflow
PigUnit
25
THANK YOU!
Mathieu Bastian
@mathieubastian
www.linkedin.com/in/mathieubastian
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Data Products: 5 Deadly Sins and How To Prevent Them

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Data Stage keynote at WebSummit Dublin 2015. This presentation dives into the five most critical sins Data Product teams might encounter and calls to action to prevent them.

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Data Products: 5 Deadly Sins and How To Prevent Them

  1. 1. DATA PRODUCTS: 5 DEADLY SINS AND HOW TO PREVENT THEM Pride Wrath Lust Gluttony Sloth Mathieu Bastian
 Web Summit 2015, Dublin Credits:The Seven Deadly Sins, Nanatsu noTaizai & nimbus-mage.deviantart.com
  2. 2. ABOUT ME • Data scientist & engineer • Led data products team at LinkedIn • Gephi co-founder • Open-source contributor 2
  3. 3. DATA PRODUCTS Source: http://bit.ly/1kMUPAe. Tentative definition User-facing production system based on an automated learning algorithm 3
  4. 4. DATA PRODUCTS TODAY 4
  5. 5. PRIDE "Excessive belief in one’s own abilities or excessive love of oneself" 5
  6. 6. PRIDE Source: http://www.themeasurementstandard.com/wp-content/uploads/2015/06/data-scientist-as-superman.jpg 6
  7. 7. With power comes responsibility 7 Source: http://www.economist.com/node/15579717
  8. 8. Who are you building it for? Understand user intent Integrate into the user flow Explain recommendations to the user Set right user expectations Treat user like you would like to be treated 8 Credits: Google
  9. 9. Anticipate edge cases 9
  10. 10. WRATH “Choice of violent and hateful actions over love and patience" 10
  11. 11. WRATH 11
  12. 12. Exercise perseverance Reward Time Phase II: Growth Phase III: Maintenance Phase I: Inception 12
  13. 13. But have a plan 13
  14. 14. LUST "Depraved thought, unwholesome morality and desire for excitement" 14
  15. 15. LUST Credits: Google Data Center 15
  16. 16. Perform due diligence 16
  17. 17. Thank the janitor & handyman 17
  18. 18. GLUTTONY "The consumption of more of anything than you need" 18
  19. 19. GLUTTONY 19
  20. 20. Avoid solo data scientists 20 Credits: Lucasfilm
  21. 21. Choose the right problem M - Measurable E - Explainable R - Rapid prototyping C - Core I - Iterable 21
  22. 22. SLOTH "Not caring about others or living life in a fulfilling way" 22
  23. 23. SLOTH 23
  24. 24. Embrace continuous data pipelines Source: http://http://azkaban.github.io/ 24
  25. 25. Make data pipelines robust Code Upload Run workflow Look at logs Code Upload Run workflow PigUnit 25
  26. 26. THANK YOU! Mathieu Bastian @mathieubastian www.linkedin.com/in/mathieubastian

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