In my last article I started walking through the key functions and features that my 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
Previously I walked through the Meta Data Sourcing Layer, Meta Data Integration Layer and the Meta Data Repository components. This month I will present the key functions and features of my optimal
meta data tool in the MME categories of Meta Data Management, Meta Data Marts and Meta Data Delivery layers.
Meta Data Management Layer
The purpose of the meta data management layer is to provide the systematic management of the meta data repository and the other MME components. This layer includes many functions, including (see
Figure 1: Meta Data Management Layer):
- Archiving – of the meta data within the repository
- Backup – of the meta data on a scheduled basis
- Database Modifications – allows for the extending of the repository
- Database Tuning – is the classic tuning of the database for the meta model
- Environment Management – is the processes that allow the repository administrator to manage and migrate between the different versions/installs of the meta data repository
- Job scheduling – would manage both the event-based and trigger-based meta data integration processes
- Purging – should handle the definition of the criteria required to define the MME purging requirements
- Recovery – process would be tightly tied into the backup and archiving facilities of repository
- Security Processes – would provide the functionality to define security restrictions from an individual and group perspective
- Versioning – meta data is historical, so this tool would need to version the meta data by date/time of entry into the MME
The optimal meta data tool would also have very good documentation on all of its components, processes and functions. Interestingly enough too many of the current meta data vendors neglect to
provide good documentation with their tools. If a company wants to be taken seriously in the meta data arena they must “eat their own dog food”.
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 to them.
A java based, web-enabled, thin-client front-end has become a standard in the industry on how to present information to the end user and it certainly is the best approach for an MME. This
architecture provides the greatest degree of flexibility, lower TCO (total cost of ownership) for implementation and the web browser paradigm is widely understood by most end users within an
This web enabled front-end would be fully and completely configurable. For example, I may want 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 analysis reports are technical meta data driven reports that help an IT department assess the impact of a potential change to their IT applications (see Figure 1: “Impact Analysis: Column
Analysis for a Bank” for an example). Impact analysis can come in an almost infinite number of variations, certainly the optimum meta data tool would provide dozens of these type of reports
pre-built and completely configurable. Also the tool would be able to push” these pre-built reports and any custom built reports to specific users or groups of users desktops, or even to their
email address. These pushed reports could be configured to be released based on an event trigger or on a scheduled basis.
Website Meta Data Entry
Most enterprise meta data repositories provide their business users a web-based front-end so that the data stewards can enter meta data directly into the repository. 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
pre-built 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 website. As an IT developer looks at this data model they 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: “Meta Data Marts”). “Homogenous meta
data user group” is a fancy term for a group of users with like needs
This tool would come with pre-build meta data marts for a few of the more complex and resource intensive impact analysis. In addition, we would have meta data marts for each of the significant
industry standards like Common Warehouse Meta Model (CWM), Dublin Core and ISO 11179.
This concludes my two part series on the functions and features that an optimal meta data tool would have.
© 2004 Enterprise Warehousing Solutions, Inc. All Rights Reserved