Data profiling defined and best practices to ensure high-quality data - DIH

Last week, I discussed data onboarding, and I received numerous questions about data profiling. It is a critical step in data processing. So today, let’s talk about data profiling – what it is, and some best practices for doing it right.

Data profiling is the process of examining, analyzing, and summarizing data sets to understand their structure, content, and quality. The primary goal of data profiling is to ensure that data is accurate, complete, and suitable for its intended use. This is achieved by collecting statistics and information about the data, such as data types, frequency of values, null values, patterns, and outliers.

The Main Types of Data Profiling.

There are several types of data profiling:

  • Structure Discovery: This involves understanding the metadata, for example, checking if data conforms to the expected formats or data types.
  • Content Discovery: This includes evaluating the data’s actual content, identifying value distributions, frequency counts, and missing values.
  • Relationship Discovery: This identifies relationships between different data elements, such as primary-foreign key constraints or duplicate records.

Best Practices for Data Profiling.

Now that we understand what data profiling is, let’s talk about some best practices to ensure you’re data profiling process is as solid as it can be. DIH offers a tool (nLite) to automate data profiling, but here are eight (8) best practices when profiling your data…

  1. Define Clear Objectives: Before starting, determine what you want to achieve with data profiling. Whether it’s improving data quality, preparing for data migration, or integrating systems, clear goals will guide your profiling process.
  2. Profile Data Early and Often: Integrate data profiling at the beginning of any data project and perform it regularly. Early profiling helps identify issues before they affect downstream processes.
  3. Automate Where Possible: Use tools that automate the profiling process to save time and reduce errors. Automation can continuously monitor data quality and alert you to anomalies.
  4. Focus on Critical Data: Prioritize profiling on data that is most important to business processes or decision-making. Profiling every dataset might not be practical or necessary.
  5. Involve Business Stakeholders: Data quality affects business outcomes, so involve business users to understand context, expected values, and acceptable data thresholds.
  6. Document Findings: Keep a detailed log of profiling results. This documentation helps track improvements over time and supports transparency.
  7. Act on the Results: Profiling is only valuable if insights lead to action. Use findings to cleanse data, enhance data governance, or modify source systems.
  8. Maintain Data Privacy: When profiling sensitive data, ensure compliance with privacy laws and policies. Mask or anonymize data as required.

The Takeaway.

As you can see, data profiling is a foundational practice for managing high-quality data. By following best practices, you can gain valuable insights, improve data integrity, and make more informed business decisions.

What about you? How do you currently perform data profiling in your organization? Please comment – I’d love to hear your thoughts.

Also, please connect with DIH on LinkedIn.

Thanks,
Tom Myers