PERFORMANCE MONITORING & EVALUATION TIPS CONDUCTING DATA QUALITY ASSESSMENTS
Data quality assessments (DQAs) help managers to understand how confident they should be in the data used to manage a program and report on its success. USAID’s ADS notes that the purpose of the Data Quality Assessment is to:
“…ensure that the USAID Mission/Office and Assistance Objective (AO) Team are aware of the strengths and weaknesses of the data, as determined by applying the five data quality standards …and are aware of the extent to which the data integrity can be trusted to influence management decisions.” (ADS 220.127.116.11)
This purpose is important to keep in mind when considering how to do a data quality assessment. A data quality assessment is of little use unless front line managers comprehend key data quality issues and are able to improve the performance management system.
While managers are required to understand data quality on an ongoing basis, a data quality assessment must also be conducted at least once every three years for those data reported to Washington. As a matter of good management, program managers may decide to conduct DQAs more frequently or for a broader range of data where potential issues emerge.
The ADS does not prescribe a specific way to conduct a DQA. A variety of approaches can be used. Documentation may be as simple as a memo to the files, or it could take the form of a formal report. The most appropriate approach will reflect a number of considerations, such as management need, the type of data collected, the data source, the importance of the data, or suspected data quality issues. The key is to document the findings, whether formal or informal.
A DQA focuses on applying the data quality standards and examining the systems and approaches for collecting data to determine whether they are likely to produce high quality data over time. In other words, if the data quality standards are met and the data collection methodology is well designed, then it is likely that good quality data will result.
This “systematic approach” is valuable because it assesses a broader set of issues that are likely to ensure data quality over time (as opposed to whether one specific number is accurate or not). For example, it is possible to report a number correctly, but that number may not be valid1 as the following example demonstrates.