Deficiencies in the quality and interoperability of law enforcement data hamper law enforcement decisionmaking and operations. In this report, subject-matter experts discuss how data governance and data management (DG/DM) can address these issues and identify the most-pressing needs to leverage DG/DM knowledge to enable major improvements in the quality, availability, and interoperability of law enforcement data.

Using Data Governance and Data Management in Law Enforcement

Building a Research Agenda That Includes Strategy, Implementation, and Needs for Innovation

John S. Hollywood, Dulani Woods, Samuel Peterson, Michael J. D. Vermeer, Brian A. Jackson

ResearchPublished Sep 11, 2024

Deficiencies in the quality and interoperability of law enforcement data have been identified as major problems that hamper law enforcement decisionmaking and operations. Data governance and data management (DG/DM) can address these issues by improving the quality and shareability of data. On behalf of the National Institute of Justice, the Police Executive Research Forum and RAND convened a panel to identify the most-pressing needs to leverage DG/DM knowledge to enable major improvements in the quality, availability, and interoperability of law enforcement data.

The panelists identified five themes: improving law enforcement's DG/DM capabilities; improving protections on law enforcement data; improving community participation in data decisionmaking; developing novel data and processes to support broad, multiagency conceptions of community safety; and improving the value of traditional law enforcement data. The panelists rated the problems and potential solutions they described to identify a set of high-priority needs for improving the quality and integrity of community safety data for law enforcement agencies and all other agencies and groups involved in the community safety enterprise. These needs and supporting context are described in this report. The highest-priority theme emerging from the workshop was using DG/DM to improve community safety data protections in various ways, including developing guidelines, core processes, training, and guidance for agencies to work with vendors and improving community participation in data decisionmaking.

Key Findings

  • Data collection (and policing processes more broadly) focuses on incidents, criminal investigations, people, and cases. Thinking about problems and larger crime-generating issues and potential solutions outside direct policing responses is not historically supported.
  • Failure to prevent abuse of emerging technologies (e.g., facial recognition systems) might lead to loss of access to them. Standard practices on how to properly use technologies are needed.
  • Improper use of technologies can create destructive feedback loops in which certain communities are over-enforced because of sensor hits (cameras, shot detection), which in turn leads to more technology and sensor hits.
  • Key parties have not historically been in the room to make data decisions. An equity process during planning and implementation is needed to detect and mitigate any data problems. This needs to happen explicitly.
  • There are concerns about commercial providers using certain data formats for reasons of their own, locking in agencies with specific vendors, and charging a lot more for agencies to access their own data or provide specific data exchanges. Even when two agencies share the same platform, they often cannot share information because the data were set up differently.

Recommendations

  • Develop tools and training that can make nontraditional data — especially free text in database comments and community surveys — available for decisionmaking.
  • Establish model policies on performing technology audits that can identify the key security, privacy, and civil rights risks of new technologies and identify mitigation strategies.
  • Develop model policies and auditing for methods that identify certain people as high risk, prejudging people before they have committed crimes. Any such method should be transparent, require a criminal predicate, and include due process.
  • Disseminate model roles and responsibilities for persons accountable for data (who might not be in police departments).
  • Federal or state standards or guidelines for vendors should be expanded to include data shareability, data ownership, data usage provisions, and transferability.
  • Develop common contract language that includes such critical data governance requirements as data privacy, data ownership, legal and regulatory compliance, and transferability.
  • Develop standard business glossaries and use cases for national data collections, computer-assisted dispatch systems, and records management systems.

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Citation

RAND Style Manual
Hollywood, John S., Dulani Woods, Samuel Peterson, Michael J. D. Vermeer, and Brian A. Jackson, Using Data Governance and Data Management in Law Enforcement: Building a Research Agenda That Includes Strategy, Implementation, and Needs for Innovation, RAND Corporation, RR-A108-22, 2024. As of October 8, 2024: https://www.rand.org/pubs/research_reports/RRA108-22.html
Chicago Manual of Style
Hollywood, John S., Dulani Woods, Samuel Peterson, Michael J. D. Vermeer, and Brian A. Jackson, Using Data Governance and Data Management in Law Enforcement: Building a Research Agenda That Includes Strategy, Implementation, and Needs for Innovation. Santa Monica, CA: RAND Corporation, 2024. https://www.rand.org/pubs/research_reports/RRA108-22.html.
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The research described in this report was supported by the National Institute of Justice and conducted by the Justice Policy Program within RAND Social and Economic Well-Being.

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