Jamie Davidson’s Post

It’s time for a thoughtful rebundling of the data stack. The modern data stack broke apart the legacy monolithic BI stack. Best-in-class point solutions were pieced together. At first, it was ETL (Fivetran or Stitch), data warehouse (Redshift, then Snowflake or BigQuery), and business intelligence (Looker, Mode or Periscope). Then we added orchestration, dbt, data catalogs, data observability, and reverse ETL not to mention adjacent workflows like ML / AI. Analytics alone has been broken apart into SQL tools, visual discovery tools, governed reporting, customer-facing, spreadsheets, notebooks, and augmented analytics with AI. There is value in having a point solution that is materially better for valuable use cases. But increasingly, I’ve spoken to more and more data leaders who are questioning whether the value is worth the cost. The cost in complexity, in integration, in siloing, in overlapping licenses. I recently spoke with a data team that has a dedicated procurement person to manage the 15+ relationships they have with vendors. 😬 With analytics, there is value in bringing complimentary use cases together. SQL is great for exploration for analysts, spreadsheets are great for operations. Reporting should be standardized and managed. The same model you use to power internal reporting should be leveraged to power customer-embedded reporting. Omni is rebundling the BI stack.