Metadata
- Author: Emily Thompson
- Full Title:: Productizing Analytics: A Retrospective
- Category:: 🗞️Articles
- Document Tags:: Looker,
- URL:: https://scientistemily.substack.com/p/analytics-program-retro
- Finished date:: 2024-03-22
Highlights
Leadership increasingly became convinced that our data team’s output could not scale with the needs of the business without enabling some kind of self-service analytics capability. (View Highlight)
The rollout team went with a simplified “hub and spoke” approach, where the hub was entirely auto-generated in order to ensure that data in Looker would stay up-to-date with the data warehouse without manual intervention. They did a lot of user interviews with different customers in the Product org, and had a process for onboarding each new group into a new content folder with a tutorial and information on how to navigate the process of building dashboards. Their goal was to onboard at least eight product teams in the first half of 2021. (View Highlight)
n the shorter term, to make sure we kept an eye on only one source of truth for our high-level business metrics, we chose to put all dashboards and reports that the data team built and approved into one central “Canonical Metrics” folder in Looker. The metrics in those dashboards were considered “production”, and had a much stricter set of requirements than anything built in the spaces created for stakeholders to sandbox in. (View Highlight)
We also enforced a dashboard “truth hierarchy”, that is, if in the process of exploring data in Looker, a stakeholder developed a metric that “disagreed” with a metric already established in the canonical dashboards, the burden of proof was on them to understand the misalignment. The data team was available to help with quick investigations through a company Slack channel, but the goal was to teach stakeholders how to fish and to avoid having to spend all of their time chasing down numbers that didn’t quite agree1. (View Highlight)