How do you use data quality indicators to monitor and improve your data collection process?

1 Define your data quality objectives

Before you start collecting data, define your data quality objectives (DQOs). These are the specific goals and requirements that your data must meet to support your analysis, decision-making, or reporting. For example, you may want your data to be representative of a certain population, cover a certain time period, or answer a certain question. Your DQOs will guide your selection of data quality indicators and help you evaluate your data quality performance.

2 Choose relevant data quality indicators

Depending on your DQOs, you may select different data quality indicators to monitor and improve your data collection process. For instance, accuracy measures the degree to which your data reflects the true values or characteristics of the data source or phenomenon. Completeness evaluates the proportion of your data that is present, available, or usable for your intended purpose. Consistency assesses the degree to which your data is coherent, compatible, and free of contradictions or discrepancies across different sources, methods, or formats. Timeliness determines the extent to which your data is current, up-to-date, and delivered within the expected or required timeframe. Validity gauges the extent to which your data conforms to the rules, standards, or specifications that define its quality, format, or structure. To measure these indicators, employ quantitative or qualitative methods such as surveys, audits, tests, checks, or reviews. Use tools or software like data quality dashboards, scorecards, or reports to automate or facilitate the measurement process.

3 Set data quality thresholds and targets

Once you have chosen your data quality indicators, you need to set data quality thresholds and targets. These are the minimum and desired levels of data quality that you expect or aim to achieve for each indicator. For example, you may set a threshold of 90% completeness and a target of 95% completeness for your data. Your thresholds and targets should be realistic, achievable, and aligned with your DQOs and stakeholder expectations.

4 Monitor and report your data quality performance

As you collect data, monitor and report your data quality performance regularly and systematically. This means comparing your actual data quality levels with your thresholds and targets for each indicator and documenting the results and findings. Use visual aids, such as charts, graphs, or tables, to display and communicate your data quality performance to yourself and others. Feedback mechanisms, such as surveys, interviews, or focus groups, can collect and incorporate the opinions and perspectives of your data users, providers, or beneficiaries.

5 Review and update your data quality indicators

Finally, review and update your data quality indicators periodically and as needed. This means assessing the relevance, usefulness, and appropriateness of your indicators for your current and future data collection needs and goals. You may need to modify, add, or remove some indicators based on changes in your DQOs, data sources, methods, tools, or stakeholders. Review and update your data quality thresholds, targets, performance, and improvement actions accordingly.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?