How do you explain your data quality to others?

1 Define your data quality dimensions

Before you explain your data quality to others, you need to define what data quality means for your specific context and purpose. Data quality can be assessed based on different dimensions, such as completeness, validity, accuracy, consistency, timeliness, and relevance. Depending on your data sources, methods, and goals, you may prioritize some dimensions over others. For example, if you are reporting on customer satisfaction, you may focus more on validity and relevance than on timeliness and consistency. You should explain to your audience what each dimension means, how you measure it, and why it matters for your performance reporting.

2 Use data quality indicators and metrics

To support your data quality explanations, you need to use data quality indicators and metrics that quantify and visualize your data quality levels. Data quality indicators are qualitative or quantitative measures that show the extent to which your data meets your quality criteria. Data quality metrics are numerical values that express your data quality indicators in a standardized way. For example, you can use data quality indicators such as error rates, missing values, duplicates, outliers, or feedback scores. You can use data quality metrics such as percentages, ratios, scores, or grades. You should explain to your audience what your data quality indicators and metrics are, how you calculate them, and what they mean for your performance reporting.

3 Share your data quality challenges and solutions

To explain your data quality to others, you also need to share your data quality challenges and solutions. Data quality challenges are the issues or problems that affect your data quality, such as data entry errors, data integration issues, data format inconsistencies, or data source changes. Data quality solutions are the actions or strategies that you take to address your data quality challenges, such as data validation, data cleansing, data standardization, or data governance. You should explain to your audience what your data quality challenges are, how they impact your performance reporting, and what your data quality solutions are, how they improve your performance reporting.

4 Use data quality examples and stories

To make your data quality explanations more engaging and memorable, you need to use data quality examples and stories. Data quality examples are specific instances or cases that illustrate your data quality dimensions, indicators, metrics, challenges, or solutions. Data quality stories are narratives or anecdotes that connect your data quality examples to your performance reporting goals, outcomes, or insights. For example, you can use a data quality example to show how a data validation process reduced your error rate from 10% to 2%. You can use a data quality story to show how this improved your customer retention rate from 80% to 90%. You should explain to your audience how your data quality examples and stories relate to your performance reporting.

5 Use data quality visuals and tools

To make your data quality explanations more clear and appealing, you need to use data quality visuals and tools. Data quality visuals are graphical representations of your data quality indicators, metrics, examples, or stories. Data quality tools are software applications or platforms that help you measure, monitor, improve, or communicate your data quality. For example, you can use a data quality visual such as a dashboard, a chart, a table, or a diagram to display your data quality levels, trends, or comparisons. You can use a data quality tool such as a spreadsheet, a database, a validator, or a report generator to manage, analyze, or present your data quality. You should explain to your audience how your data quality visuals and tools help you convey your data quality.

6 Invite feedback and questions

To explain your data quality to others, you also need to invite feedback and questions. Feedback and questions are valuable ways to check your audience's understanding, satisfaction, and interest in your data quality. Feedback and questions can also help you identify any gaps, errors, or improvements in your data quality or performance reporting. You should explain to your audience that you welcome their feedback and questions, and that you are open to discuss or clarify any aspect of your data quality. You should also thank them for their attention and participation in your data quality explanations.

7 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?