Breaking data silos to achieve AI readiness

As we've reached the one-year anniversary of the AI executive order, federal agencies have embraced the promise and opportunity that AI offers. However, in this new emerging technological era, AI applications are only as effective as the data they pull from, and when information is fragmented across departments or systems, the potential of AI is limited. This challenge — data silos — must be addressed in order for federal agencies to realize the true benefits AI offers.

Agencies need a comprehensive strategy that includes unifying siloed data, establishing standardized practices for streamlined data sharing, and promoting cross-departmental collaboration. This approach will enable agencies to break down data barriers — preparing data for AI and enhancing the ability to improve mission outcomes. 

Adopting advanced data integration platforms to unify siloed data

Data silos occur when data is kept within a singular location, leading to inconsistent and incomplete data across departments and agencies. Siloed data means information is fragmented and difficult to unify, making it challenging for teams to get a complete, cohesive view of the organization's data. By unifying data into a data plane, agencies eliminate the need to switch between different databases or tools to access information.

Adopting advanced data integration platforms involves using specialized tools and technologies to unify these separate data silos. Advanced integration platforms also leverage automation to regularly update and sync data across sources. This ensures that decision-makers always have access to real-time or near-real-time information. With all data accessible in one place, analytics teams can analyze trends, patterns, and insights from across the entire dataset rather than only specific silos, which boosts the quality of insights.

Establishing standardized practices for streamlined data sharing

Streamlined data sharing creates a common framework that limits errors when merging and analyzing data across departments. This improves the accuracy of data and in turn allows AI applications to better achieve their goals. Standardized data formats enable ease of automation and orchestration by making sure the data is rendered machine readable to a large extent. This results in algorithmic ease of leveraging data collections and process simplification and makes the value of data increase as its ease of access, search and use improves. The shared language enables effective communication not just across the technical stakeholders of the solution, but also the larger team consisting of the business stakeholders and any other potential customer of the solution, internal or external.

By following the same practices, data becomes easier to find and use, consequently making data sharing more straightforward and reliable. This ensures that users can trust the data they receive from other parts of the organization. Additionally, implementing a streamlined process of data sharing, there is no longer a need for repetitive data cleaning, reformatting or transformation, which boosts efficiency.

Promoting cross-departmental collaboration

Without cross-departmental collaboration on data management strategies, agencies risk facing discrepancies in the processing and managing of data.

Federal agencies are held to stringent standards for data privacy, security and regulatory compliance. When agencies share standardized processes for data management, they’re better positioned to comply with federal regulations, avoid breaches and maintain public trust. By making data more accessible across departments, federal agencies can break down silos, create efficiencies and ultimately serve the public more effectively.

The role of AI in data management hinges on the quality and relevance of the data it consumes. Federal agencies must prioritize using current, pertinent data — understanding that, like perishable goods, data has a limited shelf life. However, data silos obstruct this goal by fragmenting information across departments, severely limiting AI’s full potential. By streamlining the processes of data management and encouraging collaboration amongst departments, organizations can be sure that their data, and in turn their AI systems, will be optimized to achieve their missions.