My DM Review columns in October and December discussed the key functions and features that the optimal meta data tool would have. In order to categorize these functions and features, I am utilizing the six major components of a managed meta data environment (MME):
- Meta Data Sourcing
- Meta Data Integration Layers
- Meta Data Repository
- Meta Data Management Layer
- Meta Data Marts
- Meta Data Delivery Layer
Last month's column discussed the meta data repository and meta data management layers. This month, I will conclude this series by presenting the key functions and features of my optimal meta data tool in the MME categories of meta data marts and meta data delivery layer.
Meta Data Delivery Layer
The meta data delivery layer is responsible for the delivery of the meta data from the repository to the end users and to any applications or tools that require meta data feeds.
A Java-based, Web-enabled, thin-client front end has become a standard in the industry for presenting information to the end user, and it certainly is the best approach for an MME. This architecture provides the greatest degree of flexibility and a lower TCO (total cost of ownership) for implementation, and the Web-browser paradigm is widely understood by most end users within an organization.
In my optimal meta data tool, this Web-enabled front end would be fully and completely configurable. For example, I may want to provide options that my users could select, or I may want to put my company's logo in the upper right- hand corner of the end-user screen.
Impact analyses are technical meta data-driven reports that help an information technology (IT) department assess the impact of a potential change to their IT applications (see Figure 1 for an example). Impact analyses can come in an almost infinite number of variations; certainly, the optimal meta data tool would provide dozens of these types of prebuilt and completely configurable reports. Also, the tool would be able to "push" these prebuilt reports and any custom-built reports to the desktops of specific users (or groups of users), or even to their e-mail addresses. These pushed reports could be configured to be released based on an event trigger or on a scheduled basis.
Web Site Meta Data Entry
Most enterprise meta data repositories provide their business users with a Web- based front end so that the data stewards can enter meta data directly into the repository. In the optimal meta data tool, this front-end capability would be fully integrated into the MME, and it would be able to write back to the meta data repository. In addition, not only would this entry point allow meta data to be written to the repository, it would also allow for relationship constraints and drop-down boxes to be fully integrated into the end-user front end. Moreover, many of these business meta data related entry/update screens would be prebuilt and fully configurable to allow the repository administrator to modify them as required. The ability to use the Web front end to write back to the repository is a feature that is lacking in many of today's meta data tools.
The optimal meta data tool would also have the ability to publish graphics to its Web front end. The users would then be able to click on the meta data attributes within these graphics for meta data drill-down, drill-up, drill-through and drill- across. For example, a physical data model could be published to the Web site. As an IT developer looks at this data model, he/she would have the ability to click on any of the columns within the physical model to look at the meta data associated with it. This is another weakness in many of the major meta data tools on the market.
Meta Data Marts
A meta data mart is a database structure, usually sourced from a meta data repository, that is designed for a homogenous meta data user group (see Figure 2). "Homogenous meta data user group" is a fancy term for a group of users with like needs.
Figure 2: Meta Data Marts
This tool would come with prebuilt meta data marts for a few of the more complex and resource intensive impact analyses. In addition, we would have meta data marts for each of the significant industry standards such as Common Warehouse Meta Model (CWM), Dublin Core and ISO 11179.
This concludes my three-part series on the functions and features of an optimal meta data tool I hope you enjoyed it!
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