Mr. Marco is an internationally recognized expert in the fields of data warehousing, enterprise architecture and business intelligence, and is the world’s foremost authority on meta data. He is the author of the widely acclaimed books “Universal Meta Data Models” (John Wiley & Sons, April 2004) and “Building and Managing the Meta Data Repository” (John Wiley & Sons). These groundbreaking books have been broadly endorsed by many of the largest software companies in the industry and by several major magazines. In addition, he is a coauthor of “Impossible Data Warehouse Situations and Solutions From The Experts” (Addison-Wesley) and “Data Resource Management” (DAMA). Mr. Marco has published hundreds of articles, is a regular columnist for several technology magazines and has served as a judge in dozens of industry awards. In addition, in 2004 Mr. Marco was selected to the very prestigious Crain’s Chicago Business “Top 40 Under 40”.
Mr. Marco is a highly sought after speaker and has presented over 100 keynote addresses and courses at the major business, data warehousing, and meta data conferences throughout the world. Mr. Marco has taught at the University of Chicago and DePaul University, and is on the Advisory Council for DePaul University’s College of Commerce. In addition, he is the founder and President of EWSolutions, a GSA schedule and Chicago-headquartered strategic partner and systems integrator dedicated to providing companies and large government agencies with best-in-class knowledge-based solutions using enterprise architecture, data warehousing, and managed meta data environment technologies (866) EWS-1100 or visit www.EWSolutions.com
How are you addressing the single most difficult problem facing data warehouses today? Data Quality. When the quality of data is compromised, incorrect interpretation and use of information from your data warehouse can destroy the confidence level of its customers, YOUR users. Once the user's confidence in your warehouse is eroded it is a question of time before your system will no longer exist.
This data quality quandary often results from system architectures that fail to identify "bad" data before it is loaded into the data warehouse. This missed opportunity leads to a dramatic increase in the time and costs that companies expend to reconcile and audit information in the warehouse. Insertion of technical meta data "tags" directly into the data warehouse's dimensional data model design and the extraction, transformation and loading (ETL) processes corrects this situation by providing a practical means to measure data quality precisely at a table row level of granularity.
This article is the first portion of a two-part series on implementing data quality through meta data. This installment examines the role meta data can have in the data warehouse model and data acquisition designs for information content and quality. Part two of the series will examine the beneficial technical meta data tags that can be incorporated into an architecture to measure data quality and provide flexibility to the system design.