C ONCLUSIONES GENERALES

§ Scalable

§ Robust

§ Customizable

§ Open

It is important to understand that if a company purchases meta data access and/or integration tools, those tools define a significant portion of the meta data architecture. Companies should, therefore, consider these essential

characteristics when evaluating tools and their implementation of the technology.

Integrated

Anyone who has worked on a decision support project understands that the biggest challenge in building a data warehouse is integrating all of the disparate sources of data and transforming the data into meaningful information. The same is true for a meta data repository. A meta data repository typically needs to be able to integrate a variety of types and sources of meta data and turn the resulting stew into meaningful, accessible business and technical meta data. For example, a company may have a meta data requirement to show its business users the

business definition of a field that appears on a data warehouse report. The company probably used a data modeling tool to construct the physical data models to store the data presented in the report's field. Let's say the business definition for the field originates from an outside source (i.e., it is external meta data) that arrives in a spreadsheet report. The meta data integration process must create a link from the meta data on the table's

field in the report to the business definition for that field in the spreadsheet. When we look at the process in this way, it's easy to see why integration is no easy feat. (Just consider creating the necessary links to all of the various types and sources of data and the myriad delivery forms that they involve.) In fact, integrating the data is probably the most complex task in the meta data repository implementation effort.

Scalable

If integration is the most difficult of the meta data architecture

characteristics to achieve, scalability is the most important characteristic. A meta data repository that is not built to grow, and grow substantially over time, will soon become obsolete. Three factors are driving the current proliferation of meta data repositories:

§Continuing growth of decision support systems. As we discussed in Chapter 1, businesses are constantly demanding greater and greater functionality from their decision support systems. It is not unusual for both the size of a data warehouse database and the number of users accessing it to double in the first year of operation. As these decision support initiatives continue to grow, the meta data repository must be able to expand to address the increasing functional requirements.

§Recognition of the value of enterprise -wide meta data. During the past three or four years, companies have begun to recognize the value that a meta data repository can bring to their decision support initiatives. Companies are now beginning to expand their repository efforts to include all of their information systems, not just decision support. I am aware of two Fortune 100 firms that are looking to initiate an enterprise-wide meta data solution. As soon as one of these major companies builds a repository to support all of its

information systems, many others are likely to follow suit. Chapter 11, The Future of Meta Data, addresses the value of applying

enterprise-wide meta data to corporate information systems.

§Increasing reliance on knowledge management. Knowledge management is a discipline that promotes the application of technology to identifying, capturing, and sharing all of a company's information assets (e.g., documents, policies, procedures, databases, and the inherent knowledge of the company's workforce). The

concept of knowledge management is a good one: Capture the information assets and make them available throughout the enterprise. However, knowledge management is generating mixed reviews in the real world. Companies are just now beginning to understand that a meta data repository is the technical backbone that is necessary to implement a knowledge management effort. Software vendors and corporations alike are now expanding their meta data solutions to provide a real-world approach to knowledge management.

(Once again, Chapter 11, The Future of Meta Data, offers a detailed discussion of this topic.)

Meta Data: It's Not Just for Decision Support

A number of years ago I was speaking at a conference in Chicago about the value that meta data can bring to a decision support system. After the talk, a member of the audience approached me and asked why I limited my meta data discussion to only those topics under decision support, since meta data can support all of a company's IT systems. I agreed that meta data can significantly aid a corporation's IT systems, but explained that I did not address it during the talk because it was difficult enough to convince people that meta data can help a decision support system, let alone provide value to every information system in the company. My stance on this topic and my presentations have changed dramatically in the past few years. Now that people understand the value, they're looking for the specifics of how to use

enterprise-wide data most effectively and leverage it to their information systems.

Robust

As with any system, a meta data repository must have sufficient

functionality and performance to meet the needs of the organization that it serves. The repository's architecture must be able to support both

business and technical user reports and views of the meta data, as well as providing acceptable user access to these views. Some of the other functionality required from the meta data architecture includes:

§ Ability to handle time- or activity-generated events

§ Import/export capability

§ Support for data lineage

§ Security setup and authorization facilities

§ Archival and backup facilities

§ Ability to produce business and technical reports

Customizable

If the meta data processes are home-grown (i.e., built without the use of meta data integration or access tools), then customization is not a problem since the entire application is tailored for the specific business environment. If, however, a company uses meta data tools to implement the repository architecture (as most do), the tools need to be customized to meet the specific current and future needs of the meta data initiative.