With the advent of big data, came the need for ways to observe and track data usage and quality. Data quality observability (DQO) is a process that uses data mining techniques to identify, assess, and monitor the overall quality of data. By tracking the data quality, organizations can increase confidence in their data and make better decisions about how to use it.
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How DQO Works
DQO begins with data discovery, which is the process of identifying relevant data sets. Once the data sets have been identified, they are assessed for accuracy and completeness. This assessment is done through a combination of manual inspection and automated tools. After the assessment is complete, the data is monitored on an ongoing basis to ensure that it remains accurate and complete.
The Benefits of DQO
There are many benefits to using DQO within an organization. First, it provides a way to ensure that decisions are made based on accurate and timely information. Second, it can improve operational efficiency by reducing the need for manual inspection of data sets. Third, it can help identify errors early so that corrective action can be taken quickly. Finally, DQO can help save time and money by reducing the need for rework downstream in the decision-making process.
Extensible Data Quality Observability (DQO) is a process that uses data mining techniques to identify, assess, and monitor the overall quality of data. By tracking the data quality, organizations can increase confidence in their data and make better decisions about how to use it. There are many benefits to using DQO within an organization, including improved decision making, increased operational efficiency, and reduced time and costs associated with rework downstream in the decision-making process. https://dqo.ai/