Child welfare agencies must stay current on the demographic data about the children, youth, and families being served at any given time. Additionally, they must be able to ascertain the practices and services being provided by frontline staff, as well as services provided by contractors and community agencies. Finally, they must be able to determine whether outcomes of services and practices are meeting agency expectations. Sound assessments of practice and outcomes depend on correct, consistent, and complete data.
Even though agencies strive for data accuracy, in the best of systems there will occasionally be inconsistencies. For example, if the number of children free for adoption differs significantly from the number of children on whom termination of parental rights (TPR) has occurred, there is an obvious error in at least one of the numbers. The State’s process should be clear about who is responsible for entering data, ensuring data accuracy, and correcting errors that are found. Additionally, the process for correcting such errors should be clear, transparent, and effective.
Three of the most common elements that contribute to poor data quality are:
- Duplication of information across files and systems
- Incomplete and missing data elements
- Inconsistent or untimely data entry
To be successful, data quality improvement activities need widespread support and active involvement from all levels of staff. Data quality management must be a collaborative effort that bridges the gaps between the information technology (IT) department and the program divisions.
One approach to managing data might be a collaborative model in which the program side is accountable for ensuring that there are well-defined data quality rules, elements to be captured, measures, and acceptability levels, while IT is responsible for instituting and maintaining the architectural framework to ensure ease of capturing information, that rules are observed, and measures are accurately reported. As Reveal and Helfgott (2012) explained in their article Putting the Pieces Together, “In a fact-based decision-making culture, operational, policy, and regulatory data are all treated as assets of the agency and system, not the purview of a single office.”