How can you collect, calculate, and store data quality metrics?

1 Define your data quality dimensions and criteria

Defining data quality dimensions is the first step to ensure data accuracy and usefulness. Depending on the context and domain, these dimensions can vary, but some common ones are accuracy, completeness, consistency, timeliness, and validity. To ensure a certain level of data quality, you need to define criteria and thresholds for each dimension. For instance, accuracy could be measured by the percentage of records that match a reference source; completeness by the percentage of records with no missing values; consistency by the number of errors or violations of data rules; timeliness by the average age or latency of the data; and validity by the percentage of records that pass validation checks.

2 Collect your data quality metrics

The next step is to collect data quality metrics from your data sources using manual inspection, automated assessment, or a hybrid evaluation. Manual inspection can be simple and low-cost, but it can be time-consuming, subjective, and prone to human errors. Automated assessment is fast and objective, but it can be costly, technical, and limited by the quality and availability of the tools or scripts. The hybrid evaluation combines manual and automated methods to provide a balanced and flexible approach. There are several tools that can help you collect your data quality metrics, such as data profiling tools, data quality tools, and data quality dashboards. Data profiling tools examine the structure, content, and metadata of your data while data quality tools can perform specific tasks such as cleansing or standardizing your data. Data quality dashboards visualize and monitor your data quality metrics using charts, graphs, tables, indicators, and alerts.

3 Calculate your data quality scores

Calculating data quality scores is the third step, which is a numerical value that reflects the overall or relative quality of your data. This helps in comparing, ranking, and prioritizing your data sources, use cases, and goals based on their data quality levels. To calculate your data quality scores, you can use different methods and techniques such as weighted average, composite index, or scoring model. For instance, the weighted average assigns different weights or importance factors to your data quality dimensions and then calculates the weighted average of your data quality metrics. The composite index involves normalizing or standardizing your data quality metrics to a common scale or range and then combining them into a composite index. As for the scoring model, it assigns different points or ratings to your data quality metrics based on their thresholds or ranges, and then sums up the points or ratings to get your data quality score. This will help you inform and reassure stakeholders as well as provide valuable insights for improving your cloud security posture.

4 Store your data quality metrics and scores

The final step is to store your data quality metrics and scores in a secure and accessible location for future use and reference. These can be stored in various formats and platforms, such as a centralized data quality repository, data quality metadata, or a data quality report. A data quality repository can help you organize, document, and govern your metrics and scores. Data quality metadata can assist in integrating, tracing, and sharing your metrics and scores. Furthermore, a data quality report can inform stakeholders and users about the status of your metrics and scores. All of these options will provide actionable feedback for improving your data quality.

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?