There is no doubt that data profiling is the technology that has had the greatest impact in transitioning organizations from reactive data correction to proactive data quality assurance than data profiling. And no data quality management program can be complete without blending the use of data profiling techniques with well-defined processes to review data, identify potential anomalies, engage business users to assess the criticality of data flaws, and formalize business rules for preemptive data validation.
As data governance moves into the mainstream, it is critical to establish a fundamental understanding of what data profiling does, how data profiling tools are used, and preparing the environment for analysis. In turn, data quality practitioners can leverage data profiling techniques to contribute to the specification of data quality dimensions, corresponding metrics, and integration within operational processes for ongoing data quality assurance.
In this tutorial, attendees will learn about:
- How data profiling tools work
- Preparing the environment for profiling
- Data profiling for discovery – metadata, data anomalies, and business rules
- Processes and templates for data quality assessment
- Embedding data validation within the business application
- What to look for in data profiling products
- Governance and stewardship considerations