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.
In my previous post, I suggested that if we were to better articulate how master data management (MDM) is typically used, we could develop the components of solution templates that can speed the integration process. In this post, we’ll start to look at some common ways that the capabilities that
A few years ago, I was presenting a morning course on master data management in which I shared some thoughts about some of the barriers to success in transitioning the use of a developed master data management index and repository into production systems. During the coffee break, an attendee mentioned
The conclusion from my last post was that entity resolution can indeed exist as a product that can remain segregated from master data management (MDM). However, the benefit of integration with MDM is that its utilization is directly embedded within the MDM application, which reduces the level of expertise the
In my last post, we explored the integration of entity resolution technology as a core component of a master data management (MDM) application, and I raised the question as to whether the rampant phase of acquisition and integration of entity resolution tools companies into MDM solutions providers implied that the
Master data management is an application framework comprising a number of different information management practices and services. And the core of most party-oriented (e.g. customer/employee/vendor, etc.) master data management systems is some mechanism for entity resolution, which fundamentally is intended to identify connections between data instances that refer to the
If you've been following this thread of posts on entity resolution, you'll recall that we have differentiated between the full integration of entity resolution within a master data management system from the other (perhaps operational) uses that do not require a master data index. While the examples we've looked at
In the past few weeks I have presented training sessions on data governance, master data management, data quality and analytics at three different venues. At each one of these events, during one of the breaks a variety of people in my course noted that the technical concepts of implementing programs
In my last post I introduced the term “behavior architecture,” and this time I would like to explore what that concept means. One approach is to start with the basics: given a business process with a set of decision points and a number of participants, the behavior architecture is the
The challenge of data/information management practitioners attempting to initiate an analytics program intended to benefit a target audience is that after the analysis is completed, there is no control over how the results are used, or if those results are used at all. In my last post, we considered modeling
Instituting an analytics program in which actionable insight is delivered to a business consumer will be successful if those consumers are aware of what they need to do to improve their processes and reap the benefits. As we have explored over the past few posts, success in the use of
