The role of police agencies across the United States has changed dramatically over the last decade and a half, creating ever-increasing demands on technology and data infrastructures. Despite the constant collection of critical information such as criminal records, officer safety concerns, call histories, and much more, the core systems that housed that information were—and in some cases, remain—fragmented and siloed.
Rich, detailed information that could support a safer policing profession and safer communities was often inaccessible, unnecessarily limiting officers to only their instincts. This disconnect placed a strain on departments that strived to meet modern expectations around performance, transparency, and accountability. Even as other sectors rapidly moved toward data centralization and digital transformation, policing remained largely analog. The systems intended to support public safety were often too slow or disjointed to do so effectively. However, in recent years, there has been a renaissance, a global reckoning to shift data from being ancillary to being a core aspect of policing.
This shift is already helping departments build trust and deliver public safety in ways that are fairer and more responsive. The integration of information like crime data, call history, mental health records and community service in real-time has redefined what is possible for modern policing. For example, after the Atlanta Police Department redesigned its patrol zone structure using data-driven modeling, the department recorded a 5.8 percent reduction in response times for high-priority 911 calls.1 What started as a technology update has turned into a new way to think about how policing should work today.
Policing Before Integration
Police work isn’t always in the field, even though that’s where officers are needed most. Agencies have been bogged down by systems that didn’t connect and agencies that didn’t coordinate, limiting the information officers had when they needed it most. The tech landscape was dominated by legacy systems that operated in silos. Most departments managed computer-aided dispatch (CAD), records management systems (RMS), personnel records, use-of-force reports, and even mental health flags through entirely separate databases. Few, if any, of these systems communicated with each other in a meaningful way.
This fragmentation made it nearly impossible for officers to easily get a full view of an incident or an individual. A responding officer might have access to basic dispatch notes but would often be unaware of whether the person involved had previous violent offenses or a known mental health history. This lack of information left officers walking into situations with more questions than answers, an environment in which mistakes became more likely.
In the 1990s and 2000s, this was the norm for most agencies. Outside of a handful of major cities, police data analysis infrastructure was minimal. While large departments like the New York City Police Department (NYPD) and the Los Angeles, California, Police Department had begun investing in crime analysts and predictive tools, most U.S. agencies lacked the personnel and systems required for any meaningful analysis. Many departments had no dedicated crime analysts, and those that did were often limited by poor data quality or access restrictions.
“Departments that embrace data as core infrastructure, not as an afterthought or isolated initiative, will be the ones most prepared to respond and lead.”
The consequences of this disconnect were major. Missed patterns led to repeated deployments to the same locations without resolving the root causes. Fragmented data often meant decisions were made without full context, raising the chances of misjudgment or escalation. Data that could have flagged individuals as vulnerable or at high risk often went unseen until it was too late.
Even early real-time crime centers (RTCCs), like those piloted in some metropolitan areas in the mid-2000s, struggled with these limitations.2 Their value was undermined by the very systems they depended on. For example, one early RTCC in the U.S. Midwest relied heavily on manually entered spreadsheets and PDF crime bulletins, a process that was not only time-consuming but also error prone.
A 2013 RAND Corporation report on predictive policing programs highlighted this problem, noting that the effectiveness of data-driven initiatives hinges on the quality, accessibility, and interoperability of the underlying data. Without integration, data were a burden and not an asset.3
Turning Points: 2010–2017
Between 2010 and 2017, U.S. policing began a slow but meaningful transformation. Data began to emerge as a strategic asset and not just as a compliance tool. The seeds of today’s real-time data environment were planted during these years, driven by shifting public expectations and the early successes of data-informed strategies. One of the earliest and most impactful changes was the gradual formalization of crime analyst roles in police departments.
This era also brought intense scrutiny to policing practices, catalyzed by high-profile incidents of police violence and misconduct. Following the 2014 death of Michael Brown in Ferguson, Missouri, federal agencies increased calls for transparency and accountability.4 In response, many departments adopted body-worn cameras and implemented new reporting protocols. As departments adopted these reforms, they were suddenly in possession of huge volumes of new data with a responsibility to use the information wisely.
“Many departments are still stuck with outdated systems, political pushback, and tech vendors that make switching tools almost impossible”
At the same time, the idea of “precision policing” began gaining widespread traction. First popularized by Commissioner Bill Bratton during his tenure at the NYPD, the term referred to targeting police resources at individuals or locations with the greatest impact on crime.5 The approach required rigorous data collection and analysis to justify resource deployment and even budget allocation. Departments that couldn’t demonstrate a data-driven rationale for their actions found it increasingly difficult to secure funding.
Additionally, the White House’s Police Data Initiative, launched in 2015 under the Obama administration, encouraged agencies to publish use-of-force and stop-and-frisk data in accessible formats.6 Though voluntary, the initiative sparked a wave of modernization across dozens of departments, many of which had never made their data public before. With increased visibility also came increased responsibility. More accurate and integrated data systems were needed to meet these rising expectations.
These years also marked the beginning of a broader reckoning with bias in policing. Research in the mid-2010s made clear that biased outcomes often stemmed from biased inputs such as incomplete data, subjective reporting, and lack of context.7 Agencies began to realize that improving data quality and integration was a path to fairer policing.
These trends helped reshape the foundation of U.S. policing. By the end of 2017, more departments were rethinking how information could inform decision-making in the field by investing in civilian analysts and creating digital transparency portals. While still early, the groundwork for modern, integrated policing was beginning to be laid.
The Modern Era: 2018–2025
The years following 2018 ushered in a new phase of digital transformation in U.S. policing, one marked by real adoption. By this point, the idea that data could shape more effective and impartial policing had taken root. What had once been a forward-thinking concept was now becoming an operational reality. Departments across the United States began investing in cloud-based tools, building out analyst units, and launching RTCCs to transform how they used data to inform daily operations.
The rise of RTCCs was perhaps the most visible symbol of this shift. Once limited to the largest urban departments, these centers now exist in even the smallest cities and have become the operational nerve center for modern police work. RTCCs equip analysts and officers with access to things like video feeds, geospatial data, and multiagency information in seconds.8 According to the Electric Frontier Foundation, more than 225 RTCCS now operate across the United States.9
Departments no longer see data collection as a bureaucratic chore or compliance obligation but as a strategic advantage. Many departments have built out full-fledged analyst divisions that are staffed with civilian professionals who bring data science and geospatial expertise. This evolution has also been driven by the migration to cloud-based platforms. Systems that were once locked in on-premise servers with limited access are now securely accessible anywhere from mobile devices in patrol cars to dispatch centers. This shift enables real-time collaboration across personnel and departments, with officers now having access to full case histories and contextual details that previously would have required hours or days of manual digging.
The National Institute of Justice has noted the growing importance of analytics in modern policing, particularly in the context of gun violence prevention and hot spot policing.10 While smart systems must be approached with caution, their success depends on the access and quality of data. Real-time systems ensure that decision-makers are not relying on outdated or incomplete information. Ultimately, the modern era of policing is defined by a mindset shift, as data are no longer something to collect after the fact. They are a living, dynamic resource that shape the present and help inform what comes next.
What’s Still Broken
Despite the widespread progress, many agencies across the United States continue to operate with fragmented data infrastructures that bring severe limitations. For every department embracing integrated, cloud-based tools, there are dozens still hamstrung by outdated systems and incompatible vendors.
At the root of the problem is persistent system siloing. Many departments still rely on legacy CAD systems and RMS that cannot communicate with each other. Even in departments that have digitized their records, simple tasks like running a multi-keyword search across systems can be functionally impossible. Officers are often forced to rely on memory or multiple phone calls just to access the basics about an individual or incident. This results in lost time, missed warning signs, and in some cases, preventable harm.
“As capabilities grow, so does the ethical responsibility to implement them with transparency and fairness.”
Compounding the issue are the “walled gardens” created by some technology vendors. These proprietary systems often restrict interoperability to lock agencies into long-term contracts. While some vendors prioritize open data standards, many do not, which can create additional costs and logistical hurdles when departments attempt to modernize. The U.S. Government Accountability Office has documented how these vendor lock-in issues can stall technology adoption and limit data sharing across agencies.11
Interagency data sharing remains another significant obstacle. RAND researchers found that regional data-sharing initiatives are often frustrated by legal, operational, and political barriers, even when all participants agree on the value of collaboration.12 Without shared infrastructure or standardized data formats, agencies continue to struggle to identify broader crime patterns that span city and state lines.
This isn’t just an IT headache, as it can put officers and people within the community at risk. A good example is coordinated retail theft, a growing issue across the United States. According to the National Retail Federation (NRF), organized retail crime cost U.S. retailers $112 billion in 2022 alone.13 These crimes often span multiple jurisdictions, yet most agencies lack the tools or agreements to spot cross-jurisdiction trends. As a result, investigations are fragmented and reactive, allowing offenders to exploit the cracks in the system.
The promise of real-time, integrated policing will remain out of reach until agencies have the funding and support to upgrade infrastructure that works across silos. The challenge is institutional. Beyond just aligning on the value of shared data, departments need to advocate for open standards and resist the inertia that keeps outdated systems in place.
The Next 15 Years
As policing continues to evolve, the next 15 years will be defined by the institutional readiness to embrace a more integrated, data-driven future. Real-time data access will be a baseline expectation, not just a luxury or pilot program. Agencies that treat information in the same way they do other tools like vehicles or firearms will be the most well-positioned to lead.
One major shift on the horizon is regional data sharing becoming a requirement rather than a best practice. The days of single-jurisdiction databases operating in isolation are numbered. Statewide and multi-county platforms are becoming essential for responding across police agencies and adjacent social service sectors. Efforts like the FBI’s National Data Exchange (N-DEx) are early examples of what this kind of interoperability can look like at scale.14
Access to real-time insights will also transform not only operations but planning, as agencies will lean on live dashboards and cross-agency trend analyses to help inform decisions. For example, officer wellness programs could begin using real-time scheduling and incident exposure data to reduce burnout and improve morale within departments. Already, some agencies are using data to track traumatic incident exposure to better allocate mental health resources.
In this swiftly approaching future, civilian analysts will become just as essential as sworn personnel. These professionals will help proactively detect trends and provide early warnings, while officers are able to focus on fieldwork. Artificial intelligence won’t take over the analyst’s role; instead, it will help by flagging important patterns without unnecessary hours of manual digging.
As capabilities grow, so does the ethical responsibility to implement them with transparency and fairness. The only way data can help reduce bias is if the systems behind the data are designed to be transparent and accountable. Departments will need to invest in regular audits and inclusive data governance policies that center around community trust. Technology will not solve cultural issues, but leadership can ensure the tools are used to reinforce integrity rather than obscure it.
Conclusion
Over the past 15 years, the biggest shift in policing hasn’t been a new gadget or training course, but a shift in mindset. This shift centered on a recognition that integrated and accessible data can be even more powerful in policing than any firearm. When used ethically and effectively, data become force multipliers for public safety.
Real-time data systems represent a fundamental evolution in how departments serve their communities. Agencies that harness these tools can more intelligently allocate resources and transparently communicate with the public. Along with improving results, these tools help departments earn the public’s trust.
Still, the transformation is far from finished. Many departments are still stuck with outdated systems, political pushback, and tech vendors that make switching tools almost impossible. Closing these gaps will require committed leadership and widespread efforts to create shared standards and incentives.
As the field enters its next chapter, departments that embrace data as core infrastructure, not as an afterthought or isolated initiative, will be the ones most prepared to respond and lead. Tomorrow’s policing will be shaped by the information available and the intention to use it wisely.
| Editorial Note: An earlier version of this article mistitled and mistakenly attributed the concept that biased outcomes may result from incomplete data, subjective reporting, and a lack of context to a 2024 report published by the Center for Policing Equity. The paper by Matthew A. Graham, correctly titled Compounding Anti-Black Racial Disparities in Police Stops, does not make this argument; instead it examines how systemic and structural factors produce racial disparities in policing. |
Notes:
1Sebastian Borge, Renato Paes Leme, and Jon Kleinberg, “Fairness and Efficiency in Policing: Data-Driven Patrol Zone Design,” arXiv, April 1, 2021.
2Calvin Hennick, “Real-Time Crime Centers Help Solve Cases and Secure Communities,” StateTech, April 16, 2025.
3Walter L. Perry et al., Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations (RAND Corporation, 2013).
4Tyler Biscontini, “Shooting of Michael Brown,” EBSCO Research Starters, 2023.
5William J. Bratton and Jon Murad, “Precision Policing: A Strategy for the Challenges of 21st Century Law Enforcement,” in Urban Policy 2018 (Manhattan Institute, 2018).
6Megan Smith and Roy L. Austin, “Launching the Police Data Initiative,” Obama Whitehouse Blog, May 18, 2015.
7Kate Crawford, “The Hidden Biases in Big Data,” Harvard Business Revew, April 1, 2013.
8“The Mission of a Real Time Crime Center,” Bureau of Justice Assistance, archived webpage.
9Electric Frontier Foundation, Atlas of Surveillance.
10Beth Pearsall, “Predictive Policing: The Future of Law Enforcement?” NIJ Journal 266 (June 2010): 16–19.
11U.S. Government Accountability Office, Cloud Computing: Selected Agencies Need to Implement Updated Guidance for Managing Restricted Licenses, 2025.
12Brian A. Jackson et al., Knowing More, But Accomplishing What? (RAND Corporation, 2017).
13National Retail Federation, “Shrink Accounted for Over $112 Billion in Industry Losses in 2022, According to NRF Report,” press release, 2023.
14Federal Bureau of Investigation, “National Data Exchange (N-DEx).”
Please cite as
Jacob Cramer, “The Quiet Revolution: Why Data Became the Most Important Tool in the Field,” Police Chief Online, October 01, 2025.
