Evolution of analytics at Wattpad
summary
Wattpad runs on ~90M users and finds analytics time split 70% ad hoc, 20% experiments, 10% deep analytics, which makes analytics a service desk. The team builds a new BI layer: a backend for data modeling and testing plus a frontend dashboarding tool, hires analytics engineers, migrates dashboards after vendor POCs, and trains stakeholders. Result: ad hoc volume declines, analytics frees time for higher‑value work, data quality and consistent self‑serve results improve at scale.
AI-written summary · about
chapters (12)
- 0:00Introduction to talk
- 1:35What is Wattpad
- 3:19Data infrastructure in 2021
- 7:16Analytics team structure
- 10:13Work distribution in analytics
- 14:11Root causes of inefficiency
- 18:51Goals for data improvement
- 20:16Shift to self-serve analytics
- 22:21Roadmap for new solution
- 24:29Current data organization
- 25:56Impact of changes
- 27:23Conclusion and Q&A
related talks
38mUnleashing the Power of Analytics Engineering
41mStep By Step Guide to Building an Application Analytics System
21mData-driven approaches in a technology startup
03mthe challenges of data wrangling
01h 06mYahoo!, Big Data, and Microsoft BI: Bigger and Better Together
20mWebsite Analytics in My Pocket Using Microsoft Fabric
45mData as culture as software
36mAssuring Business Outcome Through Analytics