In modern law enforcement, every second matters. Whether responding to an active incident, analyzing digital evidence, or coordinating across jurisdictions, the ability to access and act on data in real time can directly impact outcomes.
An investigator needs information from multiple systems, but records management systems (RMS), computer automated dispach (CAD), license plate reader data, digital evidence platforms, and other SaaS tools don’t always talk to one another. Records don’t line up cleanly. Reports have to be compiled manually or pulled from someone else’s queue. By the time everything is assembled, the situation has already evolved, or decisions have been made without the full picture.
Because of these challenges, we recently partnered with a major metropolitan police department on the east coast to reduce data processing time by 8x and eliminate 23 servers through a FedRAMP/CJIS compliant, serverless, real-time cloud-based architecture. That project reflects a broader trend: agencies are rethinking how their data environments are built.
Most of the slowdown isn’t tied to a single tool or team. It comes from how data environments have evolved over time.
Different systems (RMS, CAD, etc.) were introduced to solve specific problems. Over time, those tools often become isolated pockets of operational data, making it difficult to build a complete picture across investigations, patrol operations, evidence workflows, and reporting. Ownership of data is split across departments. Access often requires going through the right team or request process, rather than being immediately available. Reporting is handled after the fact, once data has been pulled and compiled.
Over time, that creates a consistent pattern:
Individually, these are manageable. Together, they slow everything down.
The work Red Oak Strategic did for our client focused on how data is ingested from siloed platforms, processed, and made available across the analytics environment. Their systems were revamped on AWS GovCloud using a serverless architecture. Instead of relying on scheduled batch jobs, data ingestion moved to continuous pipelines using AWS Glue and Step Functions. Data is processed as it arrives and made available immediately, rather than waiting for the next cycle to complete.
Access and governance were handled directly within the data layer. This was especially important for FedRAMP/CJIS-sensitive datasets and other controlled law enforcement data. AWS Lake Formation and Apache Iceberg were used to manage permissions, maintain data lineage, and support version-controlled datasets. That made it possible to meet FedRAMP/CJIS requirements without introducing additional systems or manual processes.
From an operational standpoint, the changes are straightforward:
Once the system was in place, the changes were immediate:
Those numbers reflect the underlying shift, but the day-to-day impact is more direct.
Investigators can access information as events unfold, without waiting for reports to be generated. Analysts aren’t spending time assembling datasets from disconnected systems or validating outputs across separate SaaS tools. Data is available, consistent, and usable without additional steps.
There’s a limit to what can be achieved by adding more tools to an existing legacy environment.
If the underlying data layer is slow or fragmented, everything built on top of it inherits those constraints, whether it’s reporting, analytics, or AI-driven tools.
What’s changing now is that more agencies are addressing that layer directly. Once data is consistently available and governed, downstream systems start to behave differently. Reporting becomes immediate. Collaboration becomes simpler. New AI capabilities can be introduced without working around existing limitations.
That shift doesn’t require a complete overhaul overnight. But it does require a different starting point: one where data is treated as a continuous, shared resource rather than something that has to be assembled after the fact.
Learn more about how law enforcement agencies are moving to real-time data environments.