Building the Data Foundation for Automated Analytics
How data mesh, a governed semantic layer, and purpose-built MCP servers can make AI-powered analytics both trustworthy and flexible.
Originally published on Block engineering blog here.
I coauthored this piece with Alyssa Ransbury, Andrew Kuttig, and Phil Azar about the data foundation behind automated analytics at Block.
The short version: AI-powered analytics only works when the underlying data system is trustworthy. The post walks through how we approached that problem by combining data mesh principles, a governed semantic layer, and two purpose-built MCP servers with different jobs:
- Block Data MCP for deterministic, governed metric answers where accuracy matters most.
- Query Expert MCP for exploratory analysis, data discovery, and SQL generation with richer context.
The piece also covers why the project started with a concrete set of recurring business questions, why we optimized for 100% metric accuracy on governed metrics, and how governance, ownership, and metadata became part of the product instead of a separate process layered on top.
Adoption grew quickly: by Q1 2026, Block Data MCP reached 1,812 quarterly unique users and Query Expert MCP reached 1,899, while controlled tests showed an 87% reduction in analyst time.
Read the full article on the Block Engineering Blog.
