David Loshin, president of Knowledge Integrity, Inc., is a recognized thought leader and expert consultant in the areas of data quality, master data management and business intelligence. David is a prolific author regarding data management best practices, via the expert channel at b-eye-network.com and numerous books, white papers, and web seminars on a variety of data management best practices. His book, Business Intelligence: The Savvy Manager’s Guide (June 2003) has been hailed as a resource allowing readers to “gain an understanding of business intelligence, business management disciplines, data warehousing and how all of the pieces work together.” His book, Master Data Management, has been endorsed by data management industry leaders, and his valuable MDM insights can be reviewed at mdmbook.com . David is also the author of The Practitioner’s Guide to Data Quality Improvement. He can be reached at loshin@knowledge-integrity.com.
A few of our clients are exploring the use of a data lake as both a landing pad and a repository for collection of enterprise data sets. However, after probing a little bit about what they expected to do with this data lake, I found that the simple use of
Operationalizing data governance means putting processes and tools in place for defining, enforcing and reporting on compliance with data quality and validation standards. There is a life cycle associated with a data policy, which is typically motivated by an externally mandated business policy or expectation, such as regulatory compliance.
In recent years, we practitioners in the data management world have been pretty quick to conflate “data governance” with “data quality” and “metadata.” Many tools marketed under "data governance" have emerged – yet when you inspect their capabilities, you see that in many ways these tools largely encompass data validation and data standardization. Unfortunately, we
