How do you communicate and report data quality results and insights in ORM?

1 Define data quality dimensions and criteria

Before you can communicate and report data quality results and insights, you need to define what data quality means for your ORM context and objectives. Data quality dimensions are the characteristics that describe the quality of the data, such as completeness, accuracy, timeliness, consistency, and validity. Data quality criteria are the specific standards or thresholds that measure the performance of the data quality dimensions, such as error rates, coverage ratios, or latency periods. By defining data quality dimensions and criteria, you can establish a common language and framework for data quality communication and reporting.

2 Identify data quality stakeholders and their needs

Data quality communication and reporting is not a one-size-fits-all exercise. Different stakeholders have different needs, expectations, and preferences for data quality information. For example, senior management may want a high-level overview of data quality performance and trends, while operational staff may need more detailed and actionable feedback on data quality issues and solutions. Therefore, you need to identify your data quality stakeholders and their needs, such as the frequency, format, content, and channel of data quality communication and reporting.

3 Use data quality dashboards and scorecards

Data quality dashboards and scorecards are effective tools for communicating and reporting data quality results and insights in a visual and interactive way. Data quality dashboards are dynamic and customizable displays that show the current status and performance of data quality dimensions and criteria, using indicators, charts, graphs, or maps. Data quality scorecards are static and standardized reports that show the historical and comparative performance of data quality dimensions and criteria, using metrics, benchmarks, or targets. By using data quality dashboards and scorecards, you can provide a clear and concise overview of data quality results and insights, as well as drill down into specific details or areas of interest.

4 Highlight data quality issues and impacts

Data quality communication and reporting is not only about presenting data quality results and insights, but also about highlighting data quality issues and impacts. Data quality issues are the gaps or deviations between the actual and expected data quality performance, such as missing, inaccurate, outdated, or inconsistent data. Data quality impacts are the consequences or implications of data quality issues for ORM processes, outcomes, or objectives, such as increased risk exposure, reduced risk awareness, or impaired risk decision-making. By highlighting data quality issues and impacts, you can raise awareness and urgency of data quality problems, as well as demonstrate the value and benefits of data quality improvement.

5 Recommend data quality actions and priorities

Data quality communication and reporting is not only about highlighting data quality issues and impacts, but also about recommending data quality actions and priorities. Data quality actions are the steps or measures that need to be taken to address or prevent data quality issues, such as correcting, validating, enriching, or standardizing data. Data quality priorities are the order or importance of data quality actions, based on factors such as severity, frequency, or business impact of data quality issues. By recommending data quality actions and priorities, you can provide guidance and direction for data quality improvement, as well as align and coordinate data quality efforts across stakeholders.

6 Solicit data quality feedback and suggestions

Data quality communication and reporting is not only about recommending data quality actions and priorities, but also about soliciting data quality feedback and suggestions. Data quality feedback is the input or response from stakeholders on the data quality results, insights, issues, impacts, actions, or priorities that you communicate or report. Data quality suggestions are the ideas or proposals from stakeholders on how to improve data quality results, insights, issues, impacts, actions, or priorities. By soliciting data quality feedback and suggestions, you can enhance data quality communication and reporting effectiveness, as well as foster data quality collaboration and ownership.

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?