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.
At this point in time, many medium to large organizations have a history of developing systems for reporting and analysis. Simultaneously, these organizations have evolved a set of basic processes that “pump out” a series of data marts used by specific business functions for generating reports.
As we discussed in an earlier post in this series, one of the intents of data federation and virtualization is to layer some degree of opacity over accessing heterogeneous data sources by using a canonical model and a semantic layer for user queries. There are two additional benefits we expect
A master data repository can be created using the data combined from the variety of data sources. A unified, consolidated representative record can be created for each identified entity and presented by selecting the union of all source data attributes into a carefully-crafted master data model.
In my last post I discussed aspects of data virtualization and at the end suggested that while the structural differences can be smoothed out via a typical federation/virtualization scheme, the mechanism can be enhanced to incorporate semantic consistency within the federation framework. A master data repository is often perceived to be
In a number of recent posts, we have discussed the issues that surround big data, largely looking at the need to access data from a number of sources of variant structure and format. From the perspective of the analytical environment, this has not only complicated the population of data warehouses
Last time we began to look at the opportunity for integrating a newly developed master data repository with existing business applications supported by a data warehouse. That meant understanding the requirements for integration and verifying that those requirements are met.
David Loshin explains how to integrate MDM with your data warehouse.
David Loshin discusses master data model requirements.
David Loshin explains the technical requirements for MDM/DW integration.
The latest in David Loshin's blog series: "Applying a Modern Programming Model to Data Inte-Aggregation"
