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How police agencies can share criminal intelligence data
A recent DOJ-commissioned report provides guidance for police investigators and crime analysts
Criminals seldom limit their activity to a specific jurisdiction so it is important that crime analysis efforts look well beyond the siloed information produced by a single organization. Increasingly, agencies are utilizing shared databases that allow analysts to consider incidents that are taking place in adjoining cities, information that might prove crucial to resolving a crime series but that would go unnoticed if the data were not readily available. Improvements in analysis software and increased computing power, combined with the recognition that a more inclusive use of data can yield better results, are motivating many law enforcement leaders to invest in tools that are effective force multipliers when it comes to patrol and investigative operations.
Today’s crime analysts have an array of tools available that aid in the early determination of emerging crime patterns and in the identification of a single individual as a likely suspect. The potential effectiveness of these tools is largely dependent on the depth and breadth of the data sources available for analysis. A 2017 DOJ-commissioned report, A Blueprint for Interagency and Cross-Jurisdictional Data Sharing (available in full below), offers practical lessons for launching data sharing, integration and analysis efforts that can better inform crime prevention and reduction strategies. The report, published by the nonprofit Urban Institute, addresses the major challenges that are likely to be encountered and offers strategies to overcome those challenges. The blueprint also serves as a guide to facilitating cross-sector analysis and identifies trends in technology and practice relevant to data sharing. Following is a summary of the key findings and recommendations outlined in the 85-page report.
Challenges of data sharing in law enforcement
Resources are often a primary concern and will determine the scope and sustainability of data-sharing efforts. It takes staff time to generate and review data, as well as significant technical capability in terms of data infrastructure. Organizations may face substantial challenges regarding the use of their data systems for analytic purposes, particularly during the initial implementation period. Ensuring compatibility of datasets is a key concern, as is data security.
Organizational culture may be such that there is actual opposition to the concept of sharing data. Data-sharing agreements will have little chance of success if staffing and management within an agency do not support data sharing or if staff turnover weakens the communication between partners. Lack of shared goals or recognized mutual benefits can result in friction and weaken data integration efforts. Staff members tasked with execution and implementation of this type of effort need the technical expertise to manage project demands. Effective lines of communication must be in place to introduce new leadership and management to data-sharing protocols.
A stable and centralized leadership is key to promoting system utility, but it may be difficult to identify long-term leadership that can be accepted by all project partners. There must be coordination among partners to maintain the momentum as efforts progress and evolve. Some data-sharing participants may fear public scrutiny or have concerns that release of information might have negative economic impact, resulting in varying levels of support for greater transparency.
In addressing the above challenges, each potential data-sharing partner must weigh the costs and utility of integrating their data, noting that the degree of effort may range from one-time participation to establishment of an ongoing system. Agencies should try to implement structures that adequately support ongoing data sharing so that they can monitor trends and respond to challenges as they arise.
Key steps to achieving data sharing in law enforcement
Develop a framework for data integration. Start with the determination of the types of questions that need to be answered, allowing for consideration of the benefits that can result from examining the relationship between crime and other variables.
Organize the research team to ensure that projects have a clear, yet flexible, leadership that ensures progress while allowing for engagement of new opportunities as they emerge. A good approach is to utilize a central project manager who oversees the work of several subject matter experts, thus balancing competing priorities.
Identify data sources while giving due consideration to data availability and quality, willingness to share data, the utility of data content and the effort needed for analysis based on formatting or organization. There should be regular engagement among data-sharing partners. Participants should be careful to avoid redundant requests and remain conscious of the frequency of new data requests. A mutually-agreed-upon timeline for collaboration and accountability will help ensure that data-sharing efforts move forward.
Build and manage relationships. The solicitation of data-sharing partners can be challenging, especially when there are public relations or operational risks for an agency. To address these concerns, participants must be able to demonstrate the value of integrated data sharing to other partners and ensure that operational and technical frameworks are in place to maintain data security.
Data management structures must be created that facilitate the efficient analysis of data. It is important that data from contributing partners be cleaned, coded and reconciled. The reconciliation of data should be prioritized, and a common framework established to deal with situations where jurisdictions use different terminology to describe a similar occurrence or observation. A data dictionary can be invaluable in this process.
To ensure effective analysis, integration of data should include structuring that facilitates efficient management. Although data can be integrated at the level of either an individual or a place, integration at a place level is often easier. Data integration will be most sustainable and useful if it is built into the daily functions of the agencies collecting the data.
The increased use of GPS-enabled devices could benefit data collection efforts, providing an opportunity to analyze granular, real-time data. Consideration should be given to how such geographic information can factor into the analysis of crime patterns.
There is great potential for data integration having a positive impact on public safety given the ongoing development of technological tools and the increasing willingness to embed data more thoroughly in the daily work of public agencies. However, concerns over privacy and the protection of civil rights will be a significant issue and there are justified concerns about data security. Agencies entering into data-sharing partnerships must establish sufficient safeguards and protocols to protect against data breaches.
The future of integrated data will involve partnerships between practitioners, academics and researches wherein each stakeholder communicates needs and developments with stakeholders from other fields. The result of this type of strategic partnership approach will enable jurisdictions to effectively examine the relationships of neighborhood dynamics and crime, allowing police to respond more holistically to incidents by drawing on information beyond traditional criminal justice sources.
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