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.
I sometimes refer to reference data as a “celebrity orphan” within an organization because reference data sets are touched by many business processes and applications, yet remain largely unowned and unmanaged. Few organizations have a truly formal methods for management and authority for reference data. This poses a conundrum: a
David Loshin defines reference data and sets up a working definition for his next set of posts.
Over the past few weeks I have been discussing the use of graph models for analyzing interconnectivity and how entity characteristics can be inferred in relation to links and connections. While we looked at the social network domain for identifying influential individuals within a social community, there are numerous other
In our previous posts along this thread, I have suggested that graph analytics provides benefits in identifying actionable knowledge inherent in the relationships between and among entities, as opposed to typical analyses that focus on characterizing individual entities. I have to admit, that suggestion is a little bit misleading. What
In my previous post I started a discussion of graph analytics in which connections and links among different types of entities can be analyzed to find patterns that lead to actionable intelligence. But before we can explore the details of the types of analyses to be performed, we must first
One of the benefits of the disruptive nature of emerging big data platform technologies is that the combination of scalable performance and lowered costs for high-speed memory opens the door for addressing business problems in ways that used to be too computationally-intensive to roll out on a broad scale. One good example
Periodic synchronization of your master data environment presumes batching up new entries to be processed all at once. Full synchronization means that any new entity brought into one of the enterprise systems will immediately be added to the master index. There are benefits and drawbacks to both of these approaches,
What really happens when a new entity is added to a master data environment? First of all, this is only done when it is determined that the entity is not already known to exist within the system. Abstractly, a significant amount of the process involves adding the new entity into
In my last post, I introduced a number of questions that might be raised during a master data integration project, and I suggested that the underlying subtext of synchronization lay at the core of each of those issues. It is worth considering an example of an application to illustrate those
A general paradigm for a master data management solution incorporates three operational components: An identity resolution engine. A master index. A master entity data repository. Conceptually, the identity resolution engine satisfies two core capabilities: the creation and management of unique identifiers associated with uniquely identified entities, and a matching capability
