Data Quality

Data quality is an overarching issue that includes timeliness, completeness and accuracy of data that is shared by stakeholder systems.  This principle is connected to other guiding principles, including identifiers, data sharing, and biometric identification.  From the national perspective, the justice community has made significant strides in enhancing the quality of disposition data. Yet justice stakeholders face challenges in capturing and reporting all disposition-related information and ensuring the data is complete. States and local partners must have some means of amending, updating, and correcting disposition information.

It is essential that all justice partners to adopt the same standard.  The central stakeholder in the justice system is the courts. Few jurisdictions have institutionalized unique person identifiers combined with biometric identification, and few courts have adopted any form of biometric identification of defendants appearing before the court to enable accurate matching and recording in the criminal history.  This applies equally to all justice partners.  Whenever disposition data are reported to the criminal history repository, biometric identifiers should be included.  When biometric data are not available, other key data elements such as Arrest Tracking Numbers, Charge Tracking Numbers and alternate person identifiers should be included.

It is possible to reliably match records without biometric identifiers to biometrically-based records in the criminal history using other well-defined and reliable data.  However, this form of matching requires significant human resources to complete.  Automation can improve accuracy and thoroughness through validation rules.  It promotes timeliness by using data processing capabilities to trigger exchanges as soon as they are ready. 

Innovation and business process revisions create opportunities for automation.  Conversely, technology creates opportunities to innovate and revise business processes.  Disposition reporting must be evaluated through both perspectives.  Entering a disposition into a court case management system that automatically triggers a message carrying the disposition data to the state repository is an example of putting both perspectives to use.

Timely and Accurate Data 

Quality assurance processes are well established in most states, yet the means to address inconsistent information varies significantly.  Ideally, a prosecutor and/or court reports a disposition that includes the necessary information for the repository to match to a person. When matching fails, the state repository typically generates an error report.
Many states have instituted manual disposition research efforts.  These efforts can be effective, but are costly, labor intensive, and largely dependent on unstable funding sources.  Such inefficiency is costly, as well as dangerous. Identifying incomplete or erroneous disposition records automatically and specifying the necessary corrective actions can help stakeholders reduce the backlog of missing criminal history information.
Case Study: Maryland's Electronic Disposition Error Reporting Process


Federal and state justice entities routinely conduct audits of criminal history information and frequently highlight gaps in arrest and disposition records. State and local agencies must have the means to address the audit findings and implement solutions to reduce the volume of incomplete or missing records. 
Case Syudy: Indiana's Automated Notification of Missing or Incomplete Reords