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When managing complex interagency collaborations, some pursue a vision of a universal data systems merger, a single platform that links law enforcement, justice, health, and human service data systems into one universal system. This model may appeal to those seeking to reduce or eliminate data entry and duplication, and to streamline communications. Yet, in practice, universal human services data systems integrations often fail to launch and fail to keep promises. Here’s why.

Companies frequently overpromise when selling software. You may have heard claims that interagency data integration will alleviate challenges to data access, case identification, duplication of entry, and communications simply by connecting existing systems. In reality, there are several reasons why vendor promises fail to deliver on these universal mergers. Moreover, this idea may distract agency leaders from adopting more effective and realistic strategies for problem-solving.

What is a universal interagency data merger?

Data integrations usually involve building bridges between systems that do not normally communicate. This creates extraction thresholds or rules about how much, what kind, and under what conditions data can be shared. Theoretically, once established, agencies can maintain their compliance and privacy standards while sharing essential records information.   

Why universal integrations fail 

Systems operate under diverse compliance frameworks and data extraction thresholds for a reason. Consumers place their trust and expectations in those frameworks. For example, law enforcement data falls under CJIS (Criminal Justice Information Services) standards, which strictly limit where and how data can be stored or accessed. Health systems must comply with HIPAA and, in some cases, 42 CFR Part 2 regulations for substance use disorder data standards that prohibit redisclosure without explicit consent. Meanwhile, education or youth services data may fall under FERPA, which requires distinct authorization for sharing student-related records. Each of these frameworks defines “authorized users” and “permissible use” differently, making a single merged database almost impossible to govern uniformly.

With governance, access, and controls resolved, there’s still work to be done. Reconciliation, field definitions, and data formats require years of oversight. Even when technology can bridge these systems, compliance frameworks themselves cannot easily align. A data field extracted under HIPAA-compliant protocols may not meet CJIS encryption or access controls, and vice versa. Matching records across systems is often an error-prone and time-consuming process. Software often uses proprietary systems with incompatible data formats or limited API access. Common issues include field mismatches, timestamp variations, or record verification - creating uneven access and risking data distortion.

Moreover, once information mergers occur, data typically need to remain in systems with the highest standards for consumer protection. Often, electronic health record systems are employed, which are inherently less accessible to agencies that do not operate as covered entities. Data ownership or deidentification for sharing among those that are not covered entities is much more complex and expensive. As a result, universal mergers often fail to yield useful data off-platform. Control and ownership are limited, and any access occurs under steeper scrutiny.

Cooperation is key to any interagency collaboration. Even with strong leadership, achieving universal interoperability is a multi-year process, and many agencies discover that they may never fully “get there.” Even with broad consent from agency leaders, consumer education and protection remain serious challenges to successful adoption.

Targeted evidence-driven data sharing

There are evidence-driven alternatives to universal mergers that focus on building ethical, secure, and collaborative networks for interagency data sharing in specific contexts and caseloads. Sharing a targeted and relevant subset of agency data, rather than a full data merger, allows teams to focus on the process and outcome indicators that matter most.

In this model, partners come together to agree upon small, purposeful subsets of data for sharing and review. They establish clear rules—using either basic identifiers or unique IDs—to reduce security and privacy risks across all vendor platforms. Sensitive information, such as PII, PHI, or billing histories, remains offsite and under the appropriate agency controls. Those who need access to data can enter fields and forms directly that can be shared.

A shared project data management site includes only project-focused indicators that interagency teams rely upon for performance management, impact assessment, and coordination. This pragmatic, evidence-based approach allows agencies to collaborate securely, meet reporting and compliance goals, and build long-term interoperability one layer at a time.

To learn more about how to set up and manage a compliant, targeted interagency data system, contact us at ARETGroup Co-Responder. Co-Responder is an evidence-driven human services data collection and management solution for community response. This innovative platform offers compliant interagency data management for today. Co-Responder allows agencies to collaborate securely, meet reporting and compliance goals, and build long-term interoperability one layer at a time. 

To learn more, contact us at support@aretgroup.com or visit aretgroup.com. To learn more about ARETGroup Co-Responder, visit coresponder.aretgroup.com.