Using the Databricks Data Intelligence Platform to Reimagine Government

Data is now more than just a byproduct of operations at every level of government; it is the key to improving citizen services, reducing waste, and making decisions faster and with more information. This shift is also being bolstered by federal mandates. By modernizing infrastructure and elevating data as a strategic asset, the Federal Data Strategy and the IT Modernization Executive Order have jointly laid the groundwork for a government that is more resilient and forward-looking. Building on that foundation, the Executive Order on Artificial Intelligence and directives to stop waste, fraud, and abuse highlight the need for advanced analytics and trustworthy data to both drive innovation and safeguard taxpayer dollars. As a whole, these initiatives demonstrate that becoming data-driven is now required. However, as many public sector agencies are all too aware, the path to becoming truly data-driven is fraught with complexity.
Agencies have found it difficult, if not impossible, to extract timely insights or responsibly implement AI due to legacy systems, siloed data environments, and inconsistent tools. The result is missed opportunities to reduce fraud, democratize data between agencies, and modernize operations to elevate mission outcomes.

Government agencies are using Databricks to reevaluate that reality. A New Foundation for Government Data

At the core of Databricks’ vision is a unified platform that brings together data and artificial intelligence in a single, seamless environment. It is referred to as the Data Intelligence Platform because it combines enterprise-grade governance, powerful machine learning capabilities, real-time analytics, and the openness and scalability of a lakehouse architecture. As a result, organizations are able to dismantle silos and confidently collaborate across teams with a simplified and secure foundation. Government data systems have long been fragmented—transactional workloads live in one place, analytics in another, and data lakes somewhere else entirely. Data sharing and integration become difficult and costly when confronted with outdated systems that are not built on open standards. Additionally, each layer frequently holds its own version of the truth, making it challenging to coordinate efforts and respond rapidly. In addition to increasing costs and complexity, this disjointed approach also undermines the reliability, timeliness, and accuracy of the insights that agencies rely on. Databricks replaces fragmentation with a modern lakehouse architecture—a cloud-native platform that unifies data engineering, analytics, and AI. Agencies can use the best analytics tools for each mission, integrate and share data more easily, and keep full control of their data in secure cloud environments by supporting open formats like Delta Lake and Iceberg. As a result, there is less data movement, less risk, and no vendor lock-in. More importantly, this streamlined architecture ensures that data is no longer just collected—it’s curated, governed, and immediately usable by analysts, operators, and decision-makers across the organization.
Collaboration-Enabled Governance Security and governance aren’t just checkboxes in the public sector—they’re mission-critical. Agencies must precisely manage access, maintain full auditability, and comply with a wide range of regulatory requirements, whether it’s sensitive operational metrics, personnel records, or financial data. Databricks addresses this with Unity Catalog, a built-in governance layer that sits directly on top of the lakehouse architecture. It provides a centralized, scalable way to manage data access, classify sensitive information, and maintain trust across teams and systems.

With Unity Catalog, agencies can implement fine-grained, role-based access controls across all their data assets—not just tables, but also files, dashboards, and even AI models. For example, an analyst at a state health department might be granted access to aggregated public health trends, while access to raw patient-level data remains restricted to authorized clinical researchers. Additionally, it enables auditability and end-to-end data lineage, allowing agencies to precisely track where data originated, how it was transformed, and who accessed it—essential for adhering to Zero Trust or agency-specific data-sharing agreements.