Composable Analytics Beats Metric Catalogs

This article argues that metric catalogs, while useful for defining individual metrics like MRR, fail to support the complex queries AI agents need, such as analyzing why MRR dropped across regions and segments. It introduces composable analytics as a semantic layer that treats metrics, dimensions, and relationships as composable objects, allowing agents to combine them freely and generate correct SQL. The article compares metric catalogs with composable semantic layers, citing industry research showing 4x faster time-to-insight and 30-70% cost reduction. It emphasizes that composable layers provide the 'grammar' AI agents need to reason with data, not just look up definitions. The piece also covers solutions like Cube, dbt MetricFlow, Dremio, and AtScale, and presents a maturity model for composable analytics. It is highly relevant to IT/technology, focusing on data architecture, AI integration, and semantic layer tools.