How to Build One Data Foundation for BI and AI: A Practical Path with Databricks

Most data architectures evolve over time as businesses adopt new technologies to meet different needs. A data warehouse supports BI and reporting, a data lake stores raw and unstructured data, and additional platforms handle new workloads. As a result, the same data is copied across systems, each with its own costs and access rules.

Over time, managing these separate systems becomes more complex. Data is updated at different times, reports use different definitions, and teams find it difficult to know which information to trust. The result is questions like:

“Why does the dashboard show a different number than the report?”
“Which copy of this data is the right one?”
“Who has access to what, and who approved it?”
“Why are we paying to store the same data three times?”

These aren’t reporting problems. They’re signs that the underlying data foundation needs attention.

Databricks offers a different approach through its Lakehouse architecture, which combines the capabilities of data lakes and data warehouses. It enables data engineering, BI, and AI workloads to work from a shared data foundation, with governance and access controls helping teams manage data consistently.

In this session, Vishwajeet and Chakshuv will walk you through why BI and AI are on seprate foundations today, what a unified data foundation looks like with Databricks, and how organizations can approach modernization step by step.