The promise of self-service analytics asserts that business users should be empowered make data-driven decisions quickly without having to involve the analytics team, while critics say that it could lead to faulty choices. In this presentation we’ll cover topics such as acknowledging diverse customer needs, choosing the right tools, understanding the pitfalls, and considering the future of self-service analytics. And cake.

1. Self-Service Analytics:
Teaching people to fish in an ocean of data
June Dershewitz,Twitch

2. What is Twitch?

4. How does data get used at Twitch?
Custom data pipeline -
20B records a day
describing engagement
with our platform
Stored in a data lake -
S3, Redshift, Spectrum
We use data to
understand and optimize
our business - video,
social, commerce, ads,
content, marketing

5. What is self-service analytics?
“I just want to get data without talking to people.”
Enabling people to do the things that analysts do for them, but for themselves.
Who are our customers?
● Executives
● Managers of product/marketing/sales
● Technical staff
● Nontechnical staff
● Etc.

6. misinterpretation
Balancing agility and trust
bottleneck

7. chaos
Balancing agility and trust
bottleneck

9. The
DIYers

10. A data catalog empowers DIYers
Definition: A data catalog is a metadata repository that enables people to enrich,
inventory, share, and collaborate with data across the enterprise.
TL;DR: It’s data about your data!
All in one place
Combines automation and crowdsourcing

11. A data catalog empowers DIYers
feature wiki
schema
browser usage data
documentation partial,
manual
partial,
automated
no
crowdsourced comments
& indications of quality
no no no
data dependency map no no partial,
automated
directory of data SMEs partial,
manual
partial,
automated
partial,
automated

12. A data catalog empowers DIYers
feature
a good data
catalog
documentation yes
crowdsourced comments
& indications of quality
yes
data dependency map yes
directory of data SMEs yes

13. A data catalog empowers DIYers
● Agility: Don’t have to ask people to get things
(removes bottleneck).
● Trust: Takes the guesswork out of using data
(reduces misinterpretation).

14. Product
Managers

15. First: Analytics education and consultation

16. Next: Custom tools to answer harder questions

17. Self-service still involves people
then now

18. Enable PMs with education, consultation, and tools
● Agility: Put humans in the right spot to achieve
scale.
● Trust: Providing education limits misuse.
Providing a framework cultivates consistency.

19. The
QQers

20. A knowledge base (big or small) empowers QQers

21. The future of QQing

22. A knowledge base (big or small) empowers QQers
● Agility: Simplifies the question→answer
cycle. Reduces rework.
● Trust: Promotes using data in the first place.
If there’s too much friction, they might not use
data at all.

23. Takeaways
Ask when launching self-service work
• How is this initiative helping us balance agility and trust?
• How well are we addressing the diverse needs of our audience?
Further reading
• Industry report: Data Catalog is the New Black
• Blog post: Democratizing Data at Airbnb

24. Thank you!
● June Dershewitz
● linkedin.com/in/jdersh
● Twitch is hiring! jobs.twitch.tv