Data Profiling: The Mainstay of Data Quality

Data Perspective

Poor-quality data creates significant risk and carries high financial, productivity, missed opportunities, and reputational costs – and data profiling is the key to improving data quality. Recent researches have shown that poor data quality is responsible for an average of $15 million per year in business value losses. In response to this, organizations are defining new data quality policies to specify the required levels of validity, completeness, and accuracy for the information to have optimal risk and value to the enterprise.

At the same time, organizations are facing new regulatory requirements for protecting and reporting on high-value, sensitive, and personal data to properly maintain, secure, and report on their users’ and customers’ data.

Today, data quality and integrity are often monumental tasks as the amount of data sources increases, unstructured data is becoming more common, and tech stacks become more complex.

What is Data Profiling?

When looking at the quality of the data, a key element is data profiling. Data profiling involves analyzing “technical metadata” to provide statistical results about the completeness, distribution, patterns, emptiness, duplication of the data, and more. This will help organizations understand their data structure better, describe its value and flush out any anomalies and issues.

Some analysts describe data profiling as the “Data analysis capabilities that give business and IT functions (especially those supporting business users) insight into the quality of data and help them identify and understand data quality issues.”

Data profiling is a challenging endeavor. Different technologies require different data profiling tools to get results. More often than not, analysts need to write SQL queries to find all the stats and properties they need to understand their data. The process involves many laborious, and prone to errors manual steps to get results teams are looking for.

How does BigID do Data Profiling?

To help address data profiling and quality management, BigID provides out-of-the-box augmented capabilities for automated data profiling that eliminate the need for queries. Within one interface, enterprises get a unified data catalog view across all data sources, for structured and unstructured data, to assess if the data is fit for purpose of use.

BigID allows operationalizing effective data governance strategies by focusing on what matters the most: automating manual tasks, evaluating whether you can trust the data, and determining if further rules and actions are needed to improve data quality. Enterprises not only get unmatched situational awareness around their most high-value, sensitive, personal, and regulated data, but can identify duplicate and redundant data, find and remediate inaccurate data, and create quality policies and workflows.

Key Takeaways for Data Profiling

Having poor data quality generates risk, high costs, and can lead to regulatory fines. Data profiling is a key capability to help address data quality initiatives in any organization. BigID enables organizations to improve their data quality by actively monitoring the consistency, accuracy, completeness, and validity of data — and making sure it is fit for purpose and compliance. It also allows teams to evaluate data quality based on data profiling results, and get results automatically in a unified catalog view.

Schedule a demo to learn more about how BigID can help you with your data profiling challenges.