TDAN: The Data Administration Newsletter, Since 1997


Subscribe to TDAN

Business Analysis Conference Europe 2014
Data Governance Financial Services Conference
Enterprise Dataversity
Data Modeling Zone
Data Governance Winter Conference

   > home > newsletter > article
 E-mail to friend

Advanced Metadata Architecture

by David Marco
Published: July 1, 2000

Published in July 2000

Corporations are demanding more and more functionality from all of their IT (information technology) systems, and meta data repositories are no exception to this rule. In this article I will address the more complexed architectural challenges that arise with implementing a meta data repository that requires more advanced functionality. While most repository initiatives are not attempting to implement these features, they are certainly the type of functionality that is being demanded from corporations. It is also important to note that these concepts can be implemented separately or in-conjunction with one another.

Bi-Directional Meta Data

Typically the architecture of a meta data repository is one directional. Meta data sources (i.e. data modeling tool, extraction/transformation/load tools, front-end tools, etc.) flow into the repository and are integrated together to satisfy the various needs of the business (business meta data) and IT (technical meta data) department. A bi-directional meta data architecture allows for meta data to be changed in the repository and then be feed back from the repository into it’s original source. For example, a user could go through the repository and change the name of an attribute (field) in one of the decision support system’s data marts. This change would then be feed back into the supporting data-modeling tool to update the physical model for that specific data mart.

This architecture is highly desirable for two key reasons. First, it allows vendor tools to share meta data. This is especially desirable in the decision support marketplace. Most corporations that have built a decision support system did so with “best-of-breed” tools, as oppose to integrated tool sets that are supplied by one vendor. However, these tools are not integrated with one another and do not easily communicate. Even those tools that can be integrated traditionally require a good deal of resource intensive manual programming in order to share meta data. Second, bi-directional meta data is attractive to corporations that want to implement a meta data repository on an enterprise-wide scale. By enterprise-wide meta data I mean companies that want their meta data repository to store information on all of their IT systems (both decision support and operational systems). A bi-directional architecture would allow a corporation to make global changes in the meta data repository and have those changes sweep throughout all of the tools of their enterprise.


Figure 1: Bi-Directional Meta Data Architecture

As we can see a bi-directional meta data architecture provides a corporation tremendous value in allowing their disparate tools to communicate with one another. This need is why both the Meta Data Coalition (MDC) with the Open Information Models (OIM) and the Object Management Group (OMG) with the Common Warehousing Metadata (CWM) have looked to resolve this situation by providing a industry standard meta model. The meta model is the physical database model that will store the meta data. By creating an industry standard meta model third party vendor tools can plug their applications into this model to share meta data with one another. Bi-directional meta data will become a reality as a global meta model standard emerges from either these groups. It is important to understand that even once we have a meta model standard it will take time for the various software vendors modify their applications to work with this standard. This will enable these applications to have the ability to share data, which creates tool interoperability. Each vendor’s tools have unique internal designs to them. As a result, even after we have a global meta model standard for decision support, and the vendors modify their tools to support it, we will not have 100% meta data sharing. I do believe that it is possible to have a good 75% of the meta data be sharable, which is a much better situation than what we have to deal with today.

Bi-Directional Architectural Challenges

There are three obvious challenges to implementing bi-directional meta data. First, is it forces the meta data repository to contain the latest version of the meta data source that it will feed back into. Second, someone can be making a change to the meta data in the repository and at the same time someone else can be making a change to the same meta data at its source. These situations must not only be systematically trapped but also resolved. Third, additional sets of program/process interfaces need to be built to tie the meta data repository back to the meta data source.

Closed Loop Meta Data

Corporations want to be able to integrate their customer relationship management system, decision support system, e-business solution to their operational systems to provide one integrated business intelligence system. By integrating these systems together information, especially customer information can be shared throughout the organization. This enables corporations to provide new and higher levels of customer service. In addition, service intensive industries (e.g. banking, financial services, retail) corporations want to have their systems make certain business decisions for them, without manual intervention. For example, a retail company that sells consumer electronics would want an e-business system that would allow a customer to go through the internet to search for and order whatever components they wanted. When the customer would select these components, a program interface would fire off to the customer relationship management system and other products that the customer has previously ordered could be offered to the customer. Maybe if the customer hasn’t ordered the items in awhile a discount would be offered. Once the customer has finished their shopping another interface would be sent to the corporate decision support system. This system would then update the customer’s credit rating and return this new rating to the e-business system. It is this type of integrated, closed loop systems that are the mark of excellence.

Of course this desire for closed loop systems will directly impact our meta data repositories. A closed loop meta data architecture allows for the repository to feed its meta data back into a corporation’s operational systems. This concept is similar to the bi-directional meta data architecture in that the meta data repository is feeding it’s information (meta data) into other applications, or in this case operational systems. The closed loop meta data architecture is gaining more and more notoriety in corporations that want to implement a meta data repository on an enterprise-wide level. This would allow a corporation to make global changes in the meta data repository and have it sweep throughout the operational systems of an enterprise.


Figure 2: Closed Loop Meta Data Architecture

Closed Loop Architectural Challenges

The closed loop meta data architecture adds some additional complexity to the meta data repository initiative. First, if the meta data that will be feed from the repository to the operational system can also be maintained in the operational system this would require that the meta data repository contain the latest version of that operational system’s meta data. If not than the user of the repository could not be sure of updating the latest copy of the meta data. Second, someone can be making a to the meta data in the repository and at the same time someone else can be making a change to the operational system. These conflicts must not only be systematically trapped, but also resolved. Third, program/process interfaces need to be built to tie the meta data repository back to operational systems. Currently few companies are attempting this architectural technique, however it is a natural progression in the architecture of meta data repositories.

Go to Current Issue | Go to Issue Archive

Recent articles by David Marco

David Marco - Mr. Marco is an internationally recognized expert in the fields of enterprise architecture, data warehousing and business intelligence, and is the world’s foremost authority on metadata.  Mr. Marco is the author of several widely acclaimed books including “Universal Meta Data Models” and “Building and Managing the Meta Data Repository: A Full Life-Cycle Guide”.  Mr. Marco has taught at the University of Chicago, DePaul University, and in 2004 he was selected to the prestigious Crain’s Chicago Business "Top 40 Under 40". 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 business intelligence solutions using data warehousing, enterprise architecture and managed metadata environment technologies (