---
title: "Stop building data products. Start building data services."
description: "Howden’s rapid acquisition pace exposed limits in an enterprise data model built around one product per use case, downstream quality checks, and dashboard-driven consumption. Group Chief Data Officer Barry Panayi describes shifting to open, governed data services, moving mastering and quality checks closer to ingestion, and codifying reconciliation in the Accord data model. On Databricks, the company consolidated more than 100 sources of record, standardized pipelines and shared code, and built reusable assets for cross-domain analytics, while continuing to productionize models as consistent services. The account argues that AI agents require a composable services layer, and that insight lag—the time between data existing and being usable—matters more than freshness; conversational analytics through Genie also reduced dashboard-building work."
---

# Stop building data products. Start building data services.

[Databricks](https://yomu.fyi/company/databricks) · Aly McGue · Jun 11, 2026

**Type:** Explainer

## Summary

Howden’s rapid acquisition pace exposed limits in an enterprise data model built around one product per use case, downstream quality checks, and dashboard-driven consumption. Group Chief Data Officer Barry Panayi describes shifting to open, governed data services, moving mastering and quality checks closer to ingestion, and codifying reconciliation in the Accord data model. On Databricks, the company consolidated more than 100 sources of record, standardized pipelines and shared code, and built reusable assets for cross-domain analytics, while continuing to productionize models as consistent services. The account argues that AI agents require a composable services layer, and that insight lag—the time between data existing and being usable—matters more than freshness; conversational analytics through Genie also reduced dashboard-building work.

## Context

Howden’s growth through acquisitions, agent-based workflows, and rapidly expanding data sources made a slower enterprise data playbook increasingly restrictive. Integration after acquisitions took about six months, fragmented sources limited adoption, and teams repeatedly reconciled up to four correct versions of the same data point because there was no common data model or taxonomy.

## Approach / What changed

Howden shifted from one data product per use case toward open, governed data services. It moved data mastering and quality checks closer to ingestion, built the Accord data model to codify reconciliation, and used Databricks for standardized pipelines, shared code, reusable data assets, and cross-domain analytics. The company also introduced Genie for conversational access to governed data.

## Takeaways

- Howden reduced repeated reconciliation by codifying business logic in the Accord data model rather than relying on people to determine the correct contextual version of each metric.
- Moving mastering and quality checks closer to ingestion was intended to make acquired data usable faster and reduce fragmentation, slow integration, and duplicate effort.
- Genie answered questions with numbers or charts and, in Howden’s US retail business, reportedly saved hundreds of hours of dashboard building that might have been used only once.

**Tags:** [AI Agents](https://yomu.fyi/topic/ai-agents), [Architecture](https://yomu.fyi/topic/architecture), [Databricks](https://yomu.fyi/topic/databricks)

- Source: [Databricks](https://www.databricks.com/blog/stop-building-data-products-start-building-data-services)
- Source URL: https://www.databricks.com/blog/stop-building-data-products-start-building-data-services
- Ingested by Yomu: 2026-08-30T17:03:29.281Z

[Read original post](https://www.databricks.com/blog/stop-building-data-products-start-building-data-services)