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David Marco
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
Articles by this Author
Managed Meta Data Environment (MME): A Complete Walkthrough
- By David Marco
- Published 01/15/2007
- Meta-data Management , Implementation & Strategy
- Unrated
Almost every corporation and government agency has already built, is in the process of building, or is looking to build a Managed Meta Data Environment (MME). Many organizations, however, are making fundamental mistakes. An enterprise may build many meta data repositories, or “islands of meta data” that are not linked together, and as a result do not provide as much value (see “Where’s my meta data architecture?” sidebar).
Let’s take a quick meta data management quiz. What is the most common form of meta data architecture? It is likely that most of you will answer, “centralized”; but the real answer is “bad architecture”. Most meta data repository architectures are built the same way data warehouse architectures were built: badly. The data warehouse architecture issue resulted in many global 2000 companies rebuilding their data warehousing applications, sometimes from the ground up. Many of the meta data repositories being built or already in use need to be completely rebuilt.
Implementing Data Quality Through Metadata - Part 1
- By David Marco
- Published 06/5/2008
- Implementation & Strategy
- Unrated
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.
Meta Data Architecture Fundamentals
- By David Marco
- Published 07/3/2008
- Introductory , Implementation & Strategy
- Unrated
Over the next few years many companies will have the unenviable task of completely rebuilding their decision support systems. This is occurring because many of these systems were built with flawed architectures. The architecture used to build the meta data repository is every bit as critical to its long-term viability as the architecture for the decision support system is. By taking the time to build a sound architecture your repository effort will be able to grow and mature over time to support all of your company’s meta data needs.