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.
The real questions revolve around approaches to ESP system implementations, predictive models and quality of information being streamed, says David Loshin.
David Loshin describes three sets of policies required for ensuring compliance with data protection directives for health care.
Health care fraud prevention is a sticky topic. David Loshin discusses what's needed to balance prompt claims payments with fraud prevention efforts.
I've been working on a pilot project recently with a client to test out some new NoSQL database frameworks (graph databases in particular). Our goal is to see how a different storage model, representation and presentation can enhance the usability and ease of integration for master data indexes and entity
As the application stack supporting big data has matured, it has demonstrated the feasibility of ingesting, persisting and analyzing potentially massive data sets that originate both within and outside of conventional enterprise boundaries. But what does this mean from a data governance perspective?
In my last post we started looking at the issue of identifier proliferation, in which different business applications assigned their own unique identifiers to data representing the same entities. Even master data management (MDM) applications are not immune to this issue, particularly because of the inherent semantics associated with the
I was surprised to learn recently that despite the reams of laws and policies directing the protection of personally identifiable information (PII) across industries and government agencies, more than 50 million Medicare beneficiaries were issued cards with a Medicare Beneficiary Number that's based on their Social Security Number (SSN). That's
One aspect of high-quality information is consistency. We often think about consistency in terms of consistent values. A large portion of the effort expended on “data quality dimensions” essentially focuses on data value consistency. For example, when we describe accuracy, what we often mean is consistency with a defined source
We often talk about full customer data visibility and the need for a “golden record” that provides a 360-degree view of the customer to enhance our customer-facing processes. The rationale is that by accumulating all the data about a customer (or, for that matter, any entity of interest) from multiple sources, you
In my prior posts about operational data governance, I've suggested the need to embed data validation as an integral component of any data integration application. In my last post, we looked at an example of using a data quality audit report to ensure fidelity of the data integration processes for
