Data Quality

One of the primary purposes of conducting workforce analytics is to engage in data-driven decision making about the workforce. Decisions cannot be effective if they are based on poor-quality data. Thus, it is important to ensure that any data that will be used for workforce analytics are accurate and can be trusted. The first step is to establish what standards, or criteria, the data should meet. Examples include (a) relevance—are the data useful and relevant for business needs? (b) completeness—are there missing data? and (c) uniqueness—is this the only record of this information, or is the information duplicated elsewhere? 

The next step involves assessing data quality, in light of the selected criteria. There are numerous ways to do this, ranging from simple and cursory to sophisticated and thorough. A simple, initial step is to do a visual scan of the data and talk with people who are familiar with them, to get a general sense of the extent to which the quality criteria are met. More advanced approaches involve performing statistical analyses or using a data profiling tool.

If there are data quality issues, the next step is to determine the reason for each data quality issue. Examples of potential reasons include inconsistent or unclear data entry expectations, a poor user interface, or barriers to obtaining data. If there is a desire to correct existing data, data cleansing tools can facilitate the process. Finally, to ensure high-quality data in the future, agencies should focus on business rules, data system architecture, data processes, and personnel. 

In this video, Megan Paul, QIC-WD Workforce Team Lead, provides further details on how to ensure high-quality data in the future.

The QIC-WD team developed a resource, Data Quality, to provide further details. The resource highlights 9 criteria that are relevant for workforce data, suggests ways to assess data quality, provides information on how to correct data and improve the quality of future incoming data, and links to other resources and tools.

The content contained in this blog post was developed as part of the QIC-WD’s Child Welfare Workforce Analytics Institute. The Institute was designed to facilitate growth and collaboration between leaders in child welfare and human resources in their awareness, knowledge, and use of data analytics.

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