NOTE: This blog is adapted from this Databricks Data + AI Summit Video
Across every level of government, data is no longer just a byproduct of operations -- it's the key to improving citizen services, reducing waste, and making faster, more informed decisions. Federal mandates are reinforcing this shift as well. The IT Modernization Executive Order and the Federal Data Strategy have together laid the groundwork for a more resilient, forward-looking government by modernizing infrastructure and elevating data as a strategic asset. 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. Collectively, these initiatives make clear that becoming data-driven is no longer optional. However, as many public sector agencies are all too aware, the path to becoming truly data-driven is fraught with complexity.
Legacy systems, siloed data environments, and inconsistent tools have made it difficult -- if not impossible -- for agencies to extract timely insights or implement AI responsibly. The result is missed opportunities to reduce fraud, democratize data between agencies, and modernize operations to elevate mission outcomes.
Databricks is helping government agencies rethink 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. Known as the Data Intelligence Platform, it blends the openness and scalability of a lakehouse architecture with enterprise-grade governance, real-time analytics, and powerful machine learning capabilities. The result is a simplified, secure foundation that enables agencies to break down silos and collaborate across teams with confidence.
Government data systems have long been fragmented -- transactional workloads live in one place, analytics in another, and data lakes somewhere else entirely. Fraught with legacy systems that are not based on open standards, data sharing and integration become challenging and very expensive. In addition, each layer often holds its version of the truth, making it difficult to align efforts or respond quickly. This disjointed approach not only drives up costs and complexity but also undermines the accuracy, timeliness, and trustworthiness of the insights agencies rely on.
Databricks replaces fragmentation with a modern lakehouse architecture -- a cloud-native platform that unifies data engineering, analytics, and AI. Supporting open formats like Delta Lake and Iceberg allows agencies to maintain full control of their data within secure cloud environments, integrate and share data more easily, and use the best analytics tools for each mission. The result: less data movement, lower risk, and freedom from 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.
Governance That Enables Collaboration
Security and governance aren't just checkboxes in the public sector -- they're mission-critical. Whether it's financial data, personnel records, or sensitive operational metrics, agencies must manage access with precision, maintain full auditability, and comply with a wide range of regulatory requirements. 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. It also enables end-to-end data lineage and auditability, so agencies can trace exactly where data came from, how it was transformed, and who accessed it -- critical for compliance with Zero Trust or agency-specific data-sharing agreements.
For agencies that need to collaborate securely, Unity Catalog supports federated access, allowing teams to query and analyze data in external sources -- like legacy warehouses or operational databases -- without needing to physically move or duplicate the data. And because sensitive data can't be treated as an afterthought, Unity Catalog can automatically detect and tag personally identifiable information (PII) as it's ingested -- flagging items like Social Security numbers, addresses, or birthdates -- so appropriate protections can be applied from the start.
The result is a governance model that's not only robust and secure, but also flexible enough to support modern use cases like AI adoption, interagency collaboration, and open data initiatives, without ever losing control of the data.