Data Collection

Data Quality Considerations

Apart from Data Quality improvement other considerations need to be taken into account when improving your data quality. Sometimes the data is available but other environmental situations affect data quality. Two major areas to consider consist of coordination challenges and demands/pace. Considering environmental challenges to your data quality will aid in devising creative actions to improve data quality.

Coordination Challenges

Coordination challenges happen when there is a breakdown in the overall system to collect the data. Issues with workflow and the local environment of agencies can affect data.

Example: Organization A is missing a high percentage of Program Level Data such as Client Location. After a discussion with Organization A case managers, they were never trained to collect Client Location Information because it took more time away from their timed client workflow. To rectify this situation, a refresher training for case managers would need to be held to ensure everyone understands what information needs to be entered by every stakeholder.

Demands and Pace

Demands and pace can be a big issue for data quality improvement. As with many communities, there are fewer case managers and data entry people than the exuberant amount of clients needing intake. Demand can lead to a halt in intakes being processed as well as forgotten and rushed assessment questions resulting in errors. Circumstances such as this are harder to improve and in many circumstances require programmatic changes. However, it’s very important to be aware of these situations.

Common Issues

As we reviewed data quality considerations there are also common errors we all make that lead to poor data quality. The key is to recognize them and work to make the change. Below, are a few common error examples that can affect data quality.

Lack of Communication

Example: Stakeholder A did not tell Program B about new data changes needed for new grants until a data report was requested. This communication breakdown resulted in the new information needed being unavailable for reporting, which brings high percentages of errors to your data. One of the best ways to improve this for the future is quarterly meetings with stakeholders to make sure everyone is working for the same sets of information at this time.

Poor Data Security

Example: Program A never provided Security/Privacy Training to end-users. This leads to end-users sharing passwords to the system and updating client intake information with a 30-Day review assessment. This breakdown in data collection could result in missing information and the snapshot of homelessness at entry being altered. Two ways to rectify this situation include appointing one individual to review client files and accurately fix client data and provide not only security training but refresher training for everyone.

Improving data quality will look different for everyone but it’s imperative to pay attention and review. Your system quality depends on having a full understanding of the state of the data, stakeholders, and end-users. Once you understand these factors you can develop creative projects and training to ensure data gets improved. Improvement is the key to building efficient data systems that work for all stakeholders.

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