Published in DM Review in December 2000.|
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Meta Data and Data Administration: Evaluating Meta Data Toolsby David Marco and by Michael G. Needham
A special thanks to Mike Needham for his invaluable contribution to this month's column.
Today with so much emphasis being placed on empowering the business users to do more with less information technology (IT) help and enabling the IT organization to produce better results faster, it is no surprise that meta data has become one of the hottest issues of the day. In order to leverage all of the meta data from the disparate sources that exist in your organization, you need a way to gather it into a central location (repository). This is where meta data integration tools come into the picture. Utilizing these tools, you can integrate your enterprise meta data into the repository and make it available to all the different user groups in your company. But, how do you determine which tool is right for your organization? There is a wide variety of tools on the market that claim to solve all of your meta data problems; but, as we all know, the vendor hype may not be a reality. This column will help you to objectively evaluate meta data tools.
Meta Data Sources
Within any organization there are going to be different sources of meta data. Meta data integration tools can integrate meta data from three broad source categories, each of which requires varying levels of integration complexity. It is important to identify all sources of meta data that you need to integrate into the repository. Figure 1 lists various meta data integration tool vendors.
The three classifications of meta data sources are certified, generic and non-supported sources.1 A certified source is a source that the meta data integration tool can directly read, properly interpret and load into the correct attributes of the meta model without changing the model. An example of a certified source is a CASE (computer-aided software engineering) tool such as ERwin. Most meta data integration tools are certified for several vendor tools. A generic source is a source in a common format (i.e., tab delimited, space delimited or comma delimited) that the tool can read, cannot interpret and may require a meta model change. Most tools support one or more generic meta data sources. An example of a generic meta data source is data that is stored in databases and spreadsheets. The last source of meta data is the non-supported source which is neither certified nor generic and may require extensive analysis to produce an in-house solution to access. The non- supported meta data source cannot be read or interpreted by the meta data integration tool.
Once you have classified the meta data sources available, you will be able to quickly determine the complexity of your project and if the tools you're evaluating support all of your sources. Figure 2 lists some of the more common meta data sources and how they are typically classified. (Your specific implementation may differ.)
Vendor Tool Interview Process
After determining your requirements, you are ready to begin the tool vendor interview process. By conducting some research using product information that is readily available on the Internet and in industry magazines and journals, you can narrow the field of potential vendors and products to those vendors that have tools that meet your general criteria. Once you've prepared a preliminary list of vendors, you're ready to start interviewing and evaluating. Try to have the vendor come on site and use your existing meta data sources rather than evaluating on the basis of the vendor's demos.
To objectively look at all of the criteria, you should use a weighted vendor checklist to perform a tool analysis. The vendor checklist allows you to come up with a numerical score based on the criteria you specify. Figure 3 is a small excerpt of a completed checklist outlining the kinds of questions that need to be answered when evaluating meta data tools. By using an in-depth checklist, you be able to get an unbiased view of how the tool fits your company's needs as well as the tool's strengths and weaknesses.
Once the checklist is complete, you can look at the scores to see just how well or how poorly a product performed. The higher the score, the better that tools fits your needs. Always remember that every company has its own unique meta data requirements and that nothing is perfect. Be prepared to compromise along the way; but if you keep your requirements and priorities clearly in mind and manage to ignore the bells and whistles that vendors will wave in front of you you can't go wrong.
David 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 meta data. He is the author of Universal Meta Data Models (Wiley, 2004) and Building and Managing the Meta Data Repository: A Full Life-Cycle Guide (Wiley, 2000). Marco has taught at the University of Chicago and 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 Enterprise Warehousing Solutions, Inc., 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 and meta data repository technologies. He may be reached at (866) EWS-1100 or via e-mail at DMarco@EWSolutions.com.Michael G. Needham is a data warehouse architect and contributing author to Building and Managing the Meta Data Repository. In his current role as data architect at Enterprise Warehousing Solutions, he is responsible for the design and implementation of decision support system architectures for the data warehousing and business intelligence areas. Needham has more than 10 years of experience in the IT industry. He can be reached at MNeedham@EWSolutions.com.
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