Data quality management incorporates a cycle of continuous analysis, observation, and improvement leading to overall improvement in the quality of information used to make decisions. The following resources may assist you in reviewing the data quality management cycle in your court.
This article presents a look at data quality management in Harris County with a focus on their transition to electronic records. The final point of the article is key to data quality management in the courts:
"a consistent message from the court leadership team - judges, clerks of court, court administrators, managers, and supervisors - is required to initiate and sustain a high level of data quality."
Source: Court Statistics Project, Caseload Highlights - Notes from the Field
This article looks at the role of chief data officer from a corporate perspective but a number of points are applicable to the courts. The following excerpt on the levels of data quality maturity may be helpful in assessing your court's data quality improvement activities.
Level 1: Uncertainty. The technicians in the organization stumble over data defects as their programs crash, or the businesspeople complain. There is no proactive data quality improvement process in place. Basically, the organization is asleep and doesn't want to be awakened.
Level 2: Awakening. A few isolated individuals acknowledge the dirty data and try to incorporate some data quality disciplines in their projects. However, there still is no enterprise-wide support for data quality improvement, no data quality group, and no funding.
Level 3: Enlightenment. The organization starts to address the root causes of its dirty data through program edits and data quality training. A data quality group is created, and there is funding for data quality improvement projects. The data quality group immediately performs an enterprise-wide data quality assessment and institutes several data quality disciplines.
Level 4: Wisdom. The organization proactively works on preventing future data defects by adding more data quality disciplines to its data quality improvement program. Managers across the organization accept personal responsibility for data quality. Incentives for improving data quality replace incentives for cranking out systems at the speed of light.
Level 5: Certainty. The organization is in an optimization cycle by continuously monitoring and improving its data defect-prevention processes. Data quality is an integral part of all business processes. Every job description requires attention to data quality, reporting of data defects, determining the root causes, improving the affected data quality processes to eliminate the root causes, and monitoring the effects of the improvement. Basically, the culture of the organization changes.
Source: Cutter Consortium
These presentation slides provide an overview of the Research Division and Business Practice Unit of Hennepin District Court in Minnesota. In this all electronic court, the Business Practices Unit has the main purpose of identifying and reducing risk through data quality management. The presentation provides information on how the unit was formed, roles and responsibilities of the unit, and how the unit enhanced court operation outcomes.
Source: Presentation by Dr. Marcy Podkopacz & Dr. Matt Johnson, Fourth Judicial District of Minnesota, Hennepin County, at National Association for Court Management, 2016 Midyear Conference.
For additional information on this topic or to discuss how OCA can help you with data quality, please contact OCA's Scott Griffith, Director of Research and Court Services, or Amanda Stites, Research Specialist at (512) 463-1625.