DQO – Tracking Data Quality for Greater Confidence

Extensible Data Observability (DQO) is a process that helps organizations increase confidence in their data by tracking the data quality. The data quality is tracked by collecting data accuracy, completeness, and timeliness metrics.

This process allows organizations to identify issues with their data and take corrective action to improve the overall quality of their data. The result is greater confidence in the data and improved decision making.

DQO can be used to track any type of data, but it is especially useful for tracking financial data. Financial data is often used to make decisions that can have a significant impact on an organization. Therefore, it is important to have confidence in the accuracy of this data.

How DQO Works

The first step in DQO is to define what data quality metrics need to be collected. These metrics will vary depending on the type of data being tracked. For financial data, some common metrics include accuracy, completeness, and timeliness.

Once the metrics have been defined, the next step is to collect the data. This can be done manually or through automated means. Once the data has been collected, it will need to be analyzed to determine if there are any issues with the quality of the data.

If there are issues with the quality of the data, corrective action will need to be taken to improve the overall quality of the data. The goal of DQO is to continuously track and improve the quality of an organization’s data.

DQO is a process that helps organizations increase confidence in their data by tracking the data quality. By defining what quality metrics need to be collected and continuously tracking these metrics, organizations can take corrective action to improve the overall quality of their data. The result is greater confidence in the decision making process and improved business outcomes. https://dqo.ai/