Data Profiling for Data Trust

Data Perspective

What is Data Profiling?

Data Profiling is analyzing a data asset to provide statistical results about the data. It is a summary snapshot of the shape of the data including information about completeness, distribution, patterns, type, and duplication of the data. Organizations use this summary view to better understand the data structure, describe its value and confirm that it is good to use, or identify any anomalies and issues.

Analyzing a column in a table is a fast way to answer questions like:

  • How many rows are in this column?
  • Is the data type text or numeric?
  • What is the average length of the entries in the column?
  • What is the min / max value found in the column?
  • What percentage of the rows in this column are empty?

Data profiling can be a challenge because different technologies require different tools or methods to get summary views. Analysts may need to write SQL queries to find the stats and properties they need to understand their data. An automated solution will provide a quick way to get the insight that data stakeholders need to govern and use data.

Why is data profiling important?

Benefits of Data Profiling

Having a clear summary understanding of data benefits users across the organization.

Data Profiling in Cloud Environments

Organizations are adopting cloud technologies for increased analysis and collaboration, so it is critical to enable the analysts with high quality data since more stakeholders will use, share, and make business decisions from the data. Data profiling can surface anomalies that need to be addressed for data quality. Managing data quality to communicate preferred data sources is essential.

A cloud platform administrator can use data profiling to help determine what datasets to upload to a cloud environment. Once data is in the cloud, data analysts use it to choose which datasets to use for analysis and collaboration, while owners and data stewards will use it to select which datasets to certify and which datasets to archive.

Data Profiling Best Practices for Data Quality

Decision making based on poor-quality data creates significant risk and carries high financial, productivity, and reputational costs. Organizations are defining new data quality policies to specify the required levels of validity, completeness, currency and accuracy for information to maximize value and minimize risk to the enterprise. A best practice is to surface anomalies that need to be addressed for data quality. Automated data profiling enables organizations to keep a current view of their data and proactively address any data quality issues before they create significant negative business impact.

Data Profiling with BigID

BigID provides automated data profiling that eliminates the need to write manual queries. Included in BigID’s data intelligence platform, the data catalog can profile columns in tables across all data sources. With a single click, data teams can profile data by column and take action with BigID apps to address data quality or contact the data owner.

Reduce Risk and Increase Data Trust with BigID

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