MCP in the News: How to Turn the Data Tsunami into Actionable Intelligence

Better data management and information exchange between the 911 system, LE agencies and the courts will enhance decision-making and improve outcomes

This article originally appeared in Police1 and can be viewed here.

By Kevin Murray and Darrin Reilly

An incredible amount of data exists in the world, and it is going to increase by orders of magnitude. In fact, some experts believe that the amount of data available worldwide will increase by 300 percent by 2025 – a short five years from now. That’s truly mind-boggling.

On a high level, more and better data leads to enhanced decision-making and improved outcomes, regardless of one’s business. But at ground level, for data to be useful it needs to be “actionable,” because a tsunami of raw information would be unmanageable at best, overwhelming at worst. This is especially true in the law enforcement and criminal justice environments, where lives are on the line every day and every second matters. There’s simply no time, in the moment, to sift through a big pile of data and try to make sense of it.

So, what do we mean when we say that data needs to be actionable? In part, it means that the data has context, that it has been analyzed in some manner to give it meaning – usually by leveraging artificial intelligence (AI) and/or machine-learning (ML) technologies. But it also means that the data can be shared and accessed readily – and that long has been a struggle for law enforcement and criminal justice agencies –collectively, the justice community – due in large measure to the complexity of the ecosystem, which consists of the following:

  • Law enforcement agencies (local, state and federal)
  • Prosecutors’ offices (local, state and federal)
  • The courts (local, state and federal)
  • The corrections system
  • The 911 community

A lot of data is generated at each stage. Caller data is captured by the 911 system. Incident data and evidence are captured, first by officers at the scene in the immediate aftermath of the event, and then later by detectives who do a deeper dive into what happened. Often, investigators working for prosecutors uncover additional evidence. Reports are written. Charges are filed. Convictions, sentences and court orders are handed down. The convicted are remanded into custody. All of these actions generate data that would be useful from one end of the ecosystem to the other if it only were easily accessible.

The data also needs to be gathered and screened in a consistent manner. This occurs today in emergency communications centers. A 911 call arrives and is fielded by a telecommunicator, who, in most cases, uses industry-standard response protocols – primarily law enforcement, fire/rescue and emergency medical services – to extract vital information from that caller and then to analyze that information to determine the appropriate response. The emergency response community must work toward developing a similar gathering and screening approach that can be used by field responders.

Not very long ago, the focus solely was on getting enough relevant data from a 911 call so that the appropriate emergency response could be dispatched. Now the justice community needs to find a way to leverage a treasure trove of data to improve outcomes. When citizens believe that law enforcement officers are acting appropriately as much as effectively – and when agencies can demonstrate that performance – they feel safer, and when that happens, trust builds.

All of this requires integration and communication of more and better data throughout the ecosystem.

So, how can this be accomplished? Here are a few suggestions:

  • Start thinking about data as digital evidence.
  • Break down long-standing silos that prevent seamless data-sharing between all elements of the ecosystem identified above by implementing automated workflows.
  • Interconnect disparate databases in a manner that enables automatic querying without any human component – to save time, reduce errors and eliminate the potential for evidence being lost or compromised.

Regarding the last bullet point, two approaches already are available to ecosystem entities that will help them accomplish these goals. One is a digital evidence management solution (DEMS), which unifies all of the evidence data related to a specific case in an easily accessible repository. Other approaches involve the use of data exchanges and enhanced data analytics via next-generation records management systems (RMS).

Optimizing Data Mining, Analytics and Reporting

Records management systems (RMS) enable law enforcement agencies to capture, store, retrieve, leverage and exchange a plethora of information pertaining to their operations. Such information spans the entire lifespan of an incident and may be found in files, reports, records and other document types. Examples of this information include 911 call data, computer-aided dispatch (CAD) data, investigative reports, citations and tickets, offender identification, warrants, arrest and booking information, and court and detention records.

Next-generation RMS go beyond these traditional functions by supporting data mining, analytics and report functions. Data analytics, including predictive analytics, are particularly important to the law enforcement sector justice community. Raw data without context has little to no utility. Data analytics provide the context that makes information actionable.

This ability manifests in numerous ways. One concerns predictive policing. At the heart of predictive policing is the ability to contextualize data using filters – such as crime mapping, geospatial analysis, historical data mining and social media analysis – to identify when and where crime is likely to occur, and then marshal the appropriate resources in those places.

Another concerns an emerging concept known as predictive early intervention. By analyzing data such as arrests, dispatches, citations, citizen complaints and internal affairs investigations, agencies can determine which officers may be at higher risk for adverse interactions with the communities they serve. Armed with this information, agencies can take corrective action – such as further evaluation, additional training and reassignment – to reduce the likelihood of future problems. The idea is to uncover patterns that could provide early warnings, perhaps by identifying certain keywords or phrases used by an officer that could indicate frustration, abusive attitudes, or even elevated stress levels.

The idea behind each of these examples is to enable authorized users to access whatever data they need, whenever they need it, regardless of whether they are in fixed or mobile environments. Achieving this goal will lead to better-informed decisions, more crimes solved, crimes solved faster and enhanced evidence that will lead to more convictions. More and better data also will lead to more crime prevented, through predictive-policing initiatives. All of this together will lead to citizens feeling safer and trusting the criminal justice system and its law enforcement component more.

Optimizing Data Exchange

In 2007, the Iowa Criminal Justice Information System (CJIS) was launched. Iowa CJIS is a “system of systems” designed to enable the integration and sharing of information between justice community entities across the state, seamlessly and securely, and in real-time. This is accomplished via numerous data exchanges that the state established.

According to the state’s Division of Criminal and Juvenile Justice Planning, Iowa CJIS typically performs about 260,000 data exchanges annually and serves 216 local police departments, 53 county attorney offices, 94 court clerks and numerous state-level agencies.

Iowa CJIS started with two data exchanges, but 25 exist today. The system architecture ensures that data is routed properly between the various data exchanges and within each data exchange, and is routed to the proper jurisdiction. The architecture also automates data-sharing workflows, ensures that they comply with all statutory and regulatory requirements, and performs validations by leveraging predefined business rules to ensure data integrity and accuracy.

Iowa CJIS has exponentially increased the amount of information that is being shared across the state’s justice community, has improved the accuracy of the data being sent, has almost entirely eliminated pertinent data being lost – which is common when information is shared manually – and has dramatically reduced the amount of time needed to deliver data. In addition, the rules that are in place ensure that only approved data is transmitted and, just as important, that it is sent only to those authorized to receive it.

Perhaps the biggest benefit is that the justice community in Iowa can be more creative, and more aggressive, in the initiatives that it pursues because its members know that more and better data is available to them via Iowa CJIS.

The Iowa CJIS project is an example of ecosystem-wide data integration. Iowa CJIS enables data exchange on a many-to-many basis by establishing connections between disparate systems and data hubs.

Optimizing Digital Evidence

An axiom within the justice community is that the more evidence that can be captured and leveraged by the prosecution, the better. Corollary to that axiom, however, is that evidence – regardless of type or quantity – has no utility if it is not easily accessed and shared, or worse, somehow falls through the cracks. The way to prevent such problems from occurring is to deploy a digital evidence management solution or DEMS.

A tremendous amount of evidence can be generated for a criminal proceeding. The following represents the proverbial tip of the iceberg:

Traditionally, all of this captured information has been cataloged and stored in file folders and evidence boxes using manual processes. This is problematic on several levels. Doing anything manually is time-consuming, labor-intensive and error-prone. Further, questions emerge regarding chain of custody – for example, was the evidence logged accurately and in a timely manner? Was access controlled properly?

When such questions arise, reasonable doubt is created, which makes it more challenging for detectives to apprehend criminals and for prosecutors to gain convictions – making already difficult jobs even more so.

Evidence also has a way of being misplaced – temporarily or permanently – which can result in an alleged perpetrator being set free when they should be locked up. Also, the ability to craft a clear timeline tied to the evidence, within the case file, becomes very difficult if not impossible. The end result is that the prosecution’s ability to clear cases in a timely manner is greatly impacted.

In recent years, evidence increasingly is being digitized. But this also is a time-consuming and labor-intensive process. Moreover, evidence data remains siloed, making it difficult to access and share the information. Often, critical evidence doesn’t get to prosecutors in time, and sometimes not at all. This too hinders prosecutions.

A DEMS provides the answer to all of these problems. Such a solution unifies all of the evidence data related to a specific case in an easily accessible repository. Once the evidence is uploaded to the system, there is no chance that it will be lost. The evidence is coded in such a way that there is no way that it will be omitted when detectives or prosecutors acquire the information within the digital case file. Policy-based controls, including multifactor authentication, ensure that only evidence data relevant to a specific case can be accessed, and only by those authorized to do so. Metadata associated with each request indicates the files that were selected, when the request was made and by whom – information that can be used to confirm that the custody chain remains unbroken.

In short, a DEMS ensures that law enforcement and prosecutorial entities are able to get their hands on all of the evidence they need, when they need it, to do their jobs – which in turn enables them to clear cases at a much faster rate, while at the same time ensuring that criminals are convicted properly. Several methods exist for implementing a DEMS, including enhancement of an existing records management system or procuring a hosted, cloud-based solution.

About the authors
Kevin Murray is executive chairman, while Darrin Reilly is president and CEO, of Mission Critical Partners. Headquartered in State College, Pennsylvania, MCP provides consulting services and solutions that support mission-critical communications. They can be emailed at and