---
title: "Companies winning with AI built the data layer first"
description: "Trinity Industries’ experience is presented as a case for treating the data layer, rather than models or dashboards, as the foundation of enterprise AI. The railcar manufacturer migrated 95% of its enterprise data to a single Databricks lakehouse, adopted Medallion architecture, moved transformations upstream, and retired legacy dashboards containing nearly 600 measures. That consolidation supports streaming ETA predictions, procurement agents, and Genie conversational analytics: the ETA model is reported as 50% more accurate than industry ETAs, agents helped increase on-time material delivery by 15%, and Genie handles more than 1,000 questions monthly. The migration took close to a year, followed by six to eight months of additional work, but the account argues that trusted, standardized data enables experimentation, automation, and broader employee access to analysis."
---

# Companies winning with AI built the data layer first

[Databricks](https://yomu.fyi/company/databricks) · Aly McGue · Apr 29, 2026

**Type:** Explainer

## Summary

Trinity Industries’ experience is presented as a case for treating the data layer, rather than models or dashboards, as the foundation of enterprise AI. The railcar manufacturer migrated 95% of its enterprise data to a single Databricks lakehouse, adopted Medallion architecture, moved transformations upstream, and retired legacy dashboards containing nearly 600 measures. That consolidation supports streaming ETA predictions, procurement agents, and Genie conversational analytics: the ETA model is reported as 50% more accurate than industry ETAs, agents helped increase on-time material delivery by 15%, and Genie handles more than 1,000 questions monthly. The migration took close to a year, followed by six to eight months of additional work, but the account argues that trusted, standardized data enables experimentation, automation, and broader employee access to analysis.

## Context

Trinity faced fragmented workloads across Azure, AWS, and on-premises systems, separate model-serving setups, slow queries, dashboard sprawl, nearly 600 distinct measures, and repeated analysis caused by knowledge silos. These conditions made it difficult for leaders to determine which numbers were reliable and limited access to timely answers.

## Approach / What changed

Trinity migrated 95% of its enterprise data to a single Databricks lakehouse, adopted Medallion architecture, moved transformations upstream, consolidated core measures, and scrapped legacy dashboards. It unified streaming data for ETA prediction, used agents for procurement workflows, and expanded conversational analytics through Databricks Genie rooms.

## Takeaways

- Trinity’s migration consolidated enterprise data in a single lakehouse and moved transformations upstream through Medallion architecture, replacing dashboard-specific logic with core measures.
- A real-time cleaning and traversal-smoothing process corrects noisy GPS data and feeds an ETA model that updates within seconds; the model is reported as 50% more accurate than industry ETAs.
- Procurement agents increased on-time material delivery by 15%, while Databricks Genie grew to more than 1,000 questions per month and is driving a BI re-architecture.

**Tags:** [AI](https://yomu.fyi/topic/ai), [Databricks](https://yomu.fyi/topic/databricks), [Lakehouse](https://yomu.fyi/topic/lakehouse), [Streaming](https://yomu.fyi/topic/streaming)

- Source: [Databricks](https://www.databricks.com/blog/companies-winning-ai-built-data-layer-first)
- Source URL: https://www.databricks.com/blog/companies-winning-ai-built-data-layer-first
- Ingested by Yomu: 2026-08-31T03:40:31.072Z

[Read original post](https://www.databricks.com/blog/companies-winning-ai-built-data-layer-first)