Best Practices in Data Profiling, Integration, and Quality
Data profiling, data integration, and data quality go together like bread, peanut butter, and jam, because all three address related issues in data assessment, acquisition, and improvement. Because they overlap and complement each other, the three are progressively practiced in tandem, often by the same team within the same data-driven initiative. Hence, there are good reasons and ample precedence for bringing the three related practices together. The result is an integrated practice for data profiling, integration, and quality (dPIQ).
You will learn:
- Relationships among the related dPIQ practices of data profiling, integration, and quality.
- The iterations and cycles of dPIQ practices, and possible ways to align these.
- Why you should tightly coordinate projects that involve the related dPIQ practices of data profiling, integration, and quality.
- Ramifications for staffing, project management, and release cycles.
Philip Russom, Ph.D.