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Meta Data and Data Administration: Meta Data ROI: Making Your IT Department Better, Stronger, Faster

by David Marco

When selling the concept of meta data to your corporation's senior management, there are only two things that they understand: increasing revenues or decreasing expenses. If you are not talking about increasing revenues or decreasing expenses, you are the teacher on the old Peanuts cartoon: "Blah, blah, blah, blah, blah." This column marks the first in a series of columns on specifically defining how meta data can increase revenue and decrease expenses in your corporation's information technology (IT) department.

Figure 1 lists the value that impact analysis (both decision support and enterprise- wide) can provide to a corporation.

A meta data repository significantly reduces the costs of decision support systems development (decrease expenses) and the speed to market for new/modified decision support systems (increase revenues). Meta data accomplishes this through the use of technical impact analysis reports. These impact analysis reports significantly aid the decision support developers as they examine the impact of proposed changes in the decision support system's environment. This type of functionality is critical for any company looking to manage their decision support system over time.

Business/Technical Value ROI Measures
Reduction of IT-Related Problems IT staff is much less likely to make programming errors when making system enhancements, since all impacted programs, tables/files and fields are identified.
Reduce IT development Life Cycles and Costs IT development life cycles are greatly reduced, since all impacted programs, tables/files and fields are identified.
Reduce Redundant Data Impact analysis allows an IT department to identify redundant data in their systems. In addition, this functionality greatly reduces the likelihood of building redundant systems containing redundant data in the first place.
Reduce Redundant Processes Impact analysis allows an IT department to identify redundant processes in their systems. In addition, this functionality greatly reduces the likelihood of building redundant systems containing redundant processes in the first place.
Reduce Impact of Employee Turnover By documenting the knowledge that is currently only known by the developer who built the programs, it is made available to the entire IT staff.
Improved System Performance As redundant data and processes are removed, the performance of the system is vastly improved.
Figure 1: Impact Analysis Benefits

Decision support systems collect their data from the operational systems of a business. It is quite common that these operational systems will undergo changes to their business rules and the data structures that can directly impact the decision support systems that they feed. Impact analysis reports meet this challenge. Let's suppose that the table used to store customer data in the order entry system of a corporation was going to be modified. I could use the meta data in the meta data repository to run an impact analysis showing all of the decision support tables/files, programs and fields that may be impacted by this change (see Figure 2). The "Table Type" field on the report in Figure 2 will equal one of three values: S, I or T. "S" indicates that the table is a source table/file from the operational system to the decision support system. "I" signifies that the table is an intermediate table/file between the operational system and the decision support system. "T" indicates that the table/file is the target decision support table.

Figure 2: Decision Support Impact Analysis

The decision support development team could then use this data to gauge the impact that this change to the operational system will have on the decision support system. This information would reduce the amount of time spent by the decision support team to manually analyze the impact of these changes (reduce expenses), thereby reducing the development time to modify the decision support system (increase revenues). In addition, the likelihood of development errors is significantly reduced as all impacted programs are identified.

The decision support team will need the option to limit the amount of information on the impact analysis; therefore, they will need to be able to perform record selection on the following report attributes: source system, source system table, source system field, decision support system table, decision support system field and table type.

Enterprise- wide impact analysis expands the scope of the decision support impact analysis to include all of a company's IT systems, not just the ones involved in the decision support process. We have kept these two topics separate because it is much easier for a corporation to build a meta data repository that stores meta data on the decision support system. This is because these systems are relatively new and, as such, have a much more advanced design and technology compared to the older operational systems. However, meta data is every bit as important to these older systems as it is to our newer systems.

Understanding the system impact of a major IT change requires a careful analysis of the current operational and decision support systems. A meta data repository significantly reduces the cost of development and the time frame needed by capturing the data transformation rules, data sources, data structures and the context of the data in the IT systems. This is critical because without the repository, the transformation rules would only be contained in the staff's memory. The meta data significantly aids the analysts as they examine the impact of proposed changes in the system's environment. This benefit will reduce the costs of future releases and help to reduce the propensity of new development errors. For example, let's suppose that a company needed to expand the field length of their customer number from a 20-byte alphanumeric value to a 30-byte alphanumeric value throughout all of their systems. An enterprise-wide impact analysis report would show all systems, tables/files, fields and their domains impacted by a change to the length of all occurrences of the customer number field. The report would clearly identify those systems and fields that cannot handle a 30-byte alphanumeric value.

These reports can be more technical in nature as they will be used by the IT staff that supports the corporation's IT systems. The technical team will need the options to limit the amount of information on the impact analysis. They will need to have record selection on the following report attributes: system, system table, system field and table type.

Business executives realize that knowledge is what differentiates corporations in the information age. Meta data is all about knowledge as well as and the capturing and accessibility of it. With meta data and a meta data repository, corporations will move from the "crawling" stage of information technology development to the "walking" stage.

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

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