| Moving a meta data repository into production requires preparation, as shown in Figure 14.3. This preparation should start early, not when all the coding and testing have been completed. Novice meta data repository teams often underestimate the time it takes to prepare the basic aspects of the production environment outlined below. -
Server platform: The meta data repository should reside on a production server and not on the development server. Therefore, the production server platform has to be installed and tested , which includes the hardware components , operating system, monitoring utilities, and network connectivity. -
Production DBMS : If the meta data repository is installed on a new production server, an instance of the DBMS has to be created, and parameters for it have to be set and tested under the operating system. If a meta data repository product is being licensed, all product components including the meta data repository database have to be installed and tested on the production server. -
Program and query libraries: All meta data migration programs (including the tool interface programs) and all meta data application programs (including the access interface programs, the online help function, the reports , and the queries) will reside in a version-controlled library. A library management product has to be installed and tested before the meta data repository programs can be moved into production. -
Security: The production server, the DBMS product, the meta data repository database, and all programs need to have the proper levels of security implemented. Security levels in a production environment are much stricter than in the development environment. Developers who could change the meta data repository database structures and its content at will in the development environment should not be granted the same authority in the production environment. -
Instruction guides and manuals: Once the meta data repository is in production, some of the meta data programs will be scheduled to run automatically with every ETL process cycle to capture the load statistics and data quality metrics and to load them into the meta data repository. Certain reports or queries may also be put on a schedule. Operations staff will monitor the scheduled runs and notify the meta data administrator if something goes wrong. Operating procedures should be prepared for them, listing the scheduled jobs, the order in which the jobs should be run, and what to do if a job fails. The help desk staff will mentor and support the business people with their meta data questions. Therefore, the help desk staff will need a reference guide to fall back on in case they get inquiries they need to investigate. This reference guide could also be given to business people since it contains helpful hints about where to locate specific meta data components and where to get additional help. -
Meta data repository training: Business people and technicians need to be trained differently on how to use the meta data repository either through the access interface or directly and interactively. Business analysts need to know how to access the meta data repository to help them select BI data for ad hoc queries. Technicians need to know how to use meta data to help them maintain the BI applications and how to retrieve meta data in order to deliver it as an integral part of the BI application reports and queries. Figure 14.3. Preparation for Meta Data Repository Rollout  A best practice is to provide "just enough" training "just in time." The first training session, no longer than one day, should provide a review of the meta data repository, how it is organized, and how to extract some meta data components, as well as an introduction to one or two basic functions. Tell the trainees that the first training session is only an introduction to the meta data repository and that they will need additional training. After one or two weeks of hands-on practice while performing their job duties , the trainees should return for another one- or two-day training session to learn about more advanced features of the meta data repository. Use the training session as an opportunity to introduce the business people to the help desk staff who will support them. Mention to the business people that the help desk staff will be mentoring them as they become proficient in navigating through the meta data repository and through the entire BI decision-support environment. Encourage the business people to establish their own network. In this network, they could help each other not only with using the meta data repository but also with learning about the business data and the application features of the BI decision-support environment as a whole. Meta Data Repository Directory Some thought should be given to the best way to organize and present the meta data repository contents to the business people and technicians. The meta data repository contents could be organized into a directory that serves as a map for easier navigation through the meta data repository. For example, the contents of a general meta data repository directory could be organized into three major groupings: business directory, information navigator, and technical directory, as shown in Table 14.2. These three groupings could list the meta data components contained within each grouping, or they could show a layer of lower-level subgroupings. A meta data repository directory could be designed as fancy as a Web site map or as simple as a basic context-sensitive matrix. The directory could also be expanded and included as part of the online help facility of the BI decision-support environment as a whole to serve as a potential entry point into the meta data repository. If a meta data repository directory is developed, its use should be covered during the training sessions. Table 14.2. Example of a Meta Data Repository Directory | | Business Directory | Information Navigator | Technical Directory | | Audience | Business staff | Business staff | Technical staff | | Meta Data Components and Functions | Business terms | Aggregations | Security | | Source of data | Transformations | BI target databases | | Currency of data | Query library | Indices | | Ownership | Drill-down functions | Data mapping | | Data quality | Roll-up functions | Quality metrics | |
