How has the BI market evolved, and what challenges have teams faced in defining and utilizing metrics?

Business intelligence (BI) has been around for a long time. There were products like IBM Cognos and SAP Business Objects, which were this big, monolithic platforms that did everything from storing the data to transformations to metric calculations to visualization.

Over the years, what we’ve seen is that these monolithic stacks have become unbundled and nowadays, modern BI tools like Looker and Tableau are built on top of cloud data warehouses like Snowflake and BigQuery.

What we’re seeing now is that people want to consume data in the interfaces of their choice. However, metric definitions for many businesses are still defined within a single BI tool. The challenge for these companies is creating a source of truth for business definitions across the company for metrics like revenue or conversion.

I saw this problem firsthand while running the analytics team at Lyft. We didn't have the right semantics on top of the data, and different people on different teams were running different SQL queries that they thought were the right thing, but were actually wrong. The finance team was using spreadsheets, product teams were using BI tools, and there were a number of other surfaces in which people were consuming data.