Designing the Optimal Meta Data Tool
By David Marco
In my last EIMInsight column 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
Last month I walked through the Meta Data Sourcing, Meta Data Integration and Meta Data Repository layers. 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
Figure 1: Meta Data Management Layer
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 serious 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 certainly it 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 organization.
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 IT department assess the impact of a potential change to their IT applications (see Figure 2: “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 prebuilt and completely configurable. Also the tool would be able to push” these prebuilt 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.
Figure 2: Impact Analysis: Column Analysis for a Bank
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 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 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 3: “Meta Data Marts”). “Homogenous meta data user group” is a fancy term for a group of users with like needs.
Figure 3: 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 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 management tool would have.
About the Author
Mr. Marco is an internationally recognized expert in the fields of enterprise information management, data warehousing and business intelligence, and is the world’s foremost authority on meta data management. He 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” and is the chairman of the Enterprise Information Management Institute (www.EIMInstitute.org). 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 solutions using data warehousing, enterprise architecture, data governance and managed meta data environment technologies (www.EWSolutions.com). He may be reached directly via email at DMarco@EWSolutions.com