Data Fabric Implementation

Data Architecture Modernization Challenge

A federal law enforcement agency was struggling with fragmented data systems that hindered their mission to combat international crime and illegal narcotics trafficking. Their suite of software applications supporting foreign assistance activities lacked proper integration, making it difficult to effectively plan, track, and evaluate progress toward strategic goals. The agency needed a comprehensive assessment of their data management maturity and a roadmap to transform their disparate systems into a cohesive, efficient data ecosystem.

Our team conducted a thorough data portfolio review to determine the maturity level of each system and developed a detailed gap analysis. Based on these findings, we designed and implemented a data fabric architecture in Azure Cloud with seven specialized work zones: governance, storage, ingest, data lake-house, prep and train, learn and inquire, and model and serve. We deployed enterprise data governance tools via Azure Kubernetes Services, established appropriate data storage solutions, and implemented advanced analytics capabilities including machine learning and closed-loop AI processing for secure handling of sensitive information.

The modernized data architecture enabled the agency to transform raw data into actionable insights while maintaining strict security and compliance requirements. Our implementation of generative AI delivered real-time natural language processing for system users, intelligent code deployment evaluation, and automated compliance checking for HR records. The new platform also facilitated secure data sharing with other federal systems, preventing training assistance to ineligible foreign participants. By migrating from standalone servers to Azure SQL, we reduced infrastructure footprint while enhancing performance and establishing proper data retention policies that optimized costs.