How can you conduct a data quality audit and report the results?

Data quality is crucial for any organization that relies on data to make decisions, optimize processes, or deliver value to customers. Poor data quality can lead to inaccurate insights, wasted resources, and missed opportunities. But how can you assess the quality of your data and report the results in a clear and actionable way? In this article, we will show you how to conduct a data quality audit and report the results using a simple framework and some tools.

1 Define your data quality dimensions

The first step in conducting a data quality audit is to define what data quality means for your specific context and goals. Data quality dimensions are the aspects of data that affect its fitness for use, such as accuracy, completeness, consistency, timeliness, validity, and uniqueness. Depending on your data sources, types, and purposes, you may want to focus on some dimensions more than others. For example, if you are dealing with customer data, you may want to ensure that it is accurate, complete, and unique, while if you are dealing with transaction data, you may want to ensure that it is consistent, timely, and valid.

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Dominique Ngo, MPH

Public Health Evaluation and Performance Management
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I find it helpful to make sure these data quality dimensions are thoroughly defined to make sure they do not leave room for interpretation. If there is room for interpretation, then inconsistencies can arise when conducting a DQA, especially when there are different actors collecting and cleaning data. One way to help increase data quality is to run a pilot and run a DQA on the pilot data, this may uncover potential data quality issues before starting data collection that actively contributes to goals.

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2 Choose your data quality metrics

The next step is to choose how to measure the data quality dimensions that you have defined. Data quality metrics are the indicators that quantify the level of quality of your data, such as error rate, completeness rate, duplication rate, currency rate, validity rate, and so on. You can use different methods to calculate these metrics, such as manual checks, data profiling, data cleansing, data validation, or data matching. You can also use tools like Excel, SQL, Python, or R to automate some of these tasks and generate reports. The key is to choose metrics that are relevant, reliable, and comparable for your data.

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3 Collect and analyze your data quality data

The third step is to collect and analyze the data quality data that you have obtained from your metrics. This involves reviewing the results, identifying the root causes of data quality issues, and prioritizing the actions to improve data quality. You can use various techniques to analyze your data quality data, such as descriptive statistics, charts, graphs, tables, dashboards, or scorecards. You can also use tools like Power BI, Tableau, or Qlik to visualize and explore your data quality data. The goal is to communicate the current state of your data quality, the impact of data quality issues, and the opportunities for improvement.

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4 Report your data quality findings and recommendations

The final step is to report your data quality findings and recommendations to your stakeholders. This involves presenting the data quality data, the analysis, and the action plan in a clear and concise way. You can use different formats to report your data quality findings and recommendations, such as slides, documents, emails, or videos. You can also use tools like PowerPoint, Word, or Canva to create and design your reports. The aim is to inform, persuade, and motivate your stakeholders to take action on data quality improvement.

By following these steps, you can conduct a data quality audit and report the results in a systematic and effective way. A data quality audit can help you assess the health of your data, identify the gaps and risks, and plan the solutions and benefits. A data quality report can help you communicate the value of data quality, align the expectations and responsibilities, and monitor the progress and outcomes.

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5 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?

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Mike Flanagan

Business Intelligence Developer with many years experience
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For me, as a Business Intelligence specialist, auditing data quality isn't a one-off or even a periodic event. Its a continuous process. BI solutions can fail or can thrive depending on how they handle DQ. Failing to address it properly results in low user confidence and the build-up of technical debt - BI teams spend more and more time investigating reported DQ issues and less time developing. The solution is simple: a) manage DQ as part of routine reporting, using agreed rules, where you reasonably can, b) monitor the number of DQ issues so the problem can be quantified, c) feed back detail to the business to facilitate data cleansing, remediation etc. And do so continuously, as part of your normal ETL processes and reporting.

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Yosif Valkov

Business Analyst Consultant - Navigating the Intersection of Data, Business & Innovation |(SSGBC) Lean Six Sigma Green Belt | Crypto Enthusiast
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In my humble experience I found useful to quantify the issue, keep track of the progress and maintain awareness of your stakeholders about the progress.

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Data Management

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How can you conduct a data quality audit and report the results?

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1 Define your data quality dimensions

2 Choose your data quality metrics

3 Collect and analyze your data quality data

4 Report your data quality findings and recommendations

5 Here’s what else to consider

Data Management

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How can you conduct a data quality audit and report the results?

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5

1 Define your data quality dimensions

2 Choose your data quality metrics

3 Collect and analyze your data quality data

4 Report your data quality findings and recommendations

5 Here’s what else to consider

Data Management

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