This column is adapted from the book Universal Meta Data Models by David Marco and Michael Jennings, John Wiley & Sons, 2004.
Before we can understand how meta data management provides value to an organization, we need to have a clear understanding of the specific applications that a managed meta data environment (MME) can provide to a corporation. In this series of columns, I will walk through several industry-specific applications for a MME. The following applications are covered throughout the examples in this series:
- Business meta data and knowledge management
- Data quality analysis
- IT impact analysis
- IT systems consolidation and migration
- Mergers and acquisitions
- Regulatory adherence
- Security analysis
Some of the applications will show meta data in standalone reports. In other examples, meta data will be directly integrated into a data warehousing environment. These applications are a good cross-industry selection, including:
- Banking and finance
- Healthcare insurance
- National defense organizations
You may find these application examples so familiar that it will seem as if they were taken right from your own company's history. Rest assured that all of our examples are based on actual MME implementations; however, the industries, data and specific applications have been changed.
Banking and Finance Industry Example
Banking and financial organizations were early adopters of MME technology. One of the reasons for this trend is that banking and financial institutions have additional complexities in their IT departments because of the extensive mergers and acquisitions that take place in this industry segment. As a result of these mergers and acquisitions, a typical large bank will have several versions of each of their core processing and strategic decision-making systems. This problem is compounded by the fact that most of these companies are not managed from an enterprise view. Instead, each line of business has a great deal of autonomy, including a separate budget and, in some cases, a separate IT department. This situation creates even more application, process, software, hardware and data redundancy.
In order to penetrate the Wisconsin market, BigCity Bank recently acquired Small Town Bank, which had a significant local presence. In fact, the majority of the services that BigCity Bank offers are only a part of Small Town Bank's core product offerings. This acquisition comes with some challenges, the first and foremost being Small Town Bank's set of redundant applications. Clearly, BigCity needs to migrate Small Town to the BigCity systems as soon as possible because the maintenance costs are significant. In addition, this migration will be critical for helping the Small Town employees adapt to the BigCity environment.
Fortunately for BigCity, several years ago it invested in building an enterprise-wide MME application. This application is targeted at capturing and managing technical meta data throughout all of their systems. Therefore, the first step in this migration was to bring the technical meta data from Small Town Bank into BigCity's MME environment. This was no small task; however, once it was completed, it allowed BigCity to significantly assist their Small Town IT migration team. The IT team doing the Small Town migration wanted an impact analysis that would take the core BigCity columns and tables and map them to the corresponding Small Town columns and tables (see Figure 1).
Figure 1: Banking MME Report: Systems Consolidation
This report is invaluable to the migration team because they need to know which Small Town systems they need to analyze in order to migrate them. For example, BigCity uses Cust_Nbr as its key attribute of record for unique customer numbers. Small Town has three attributes (CUSTNUM, Purchase_No, and Borwr_No) that need to be migrated to BigCity's Cust_Nbr. This type of information is absolutely critical to the migration team because it is fundamental to the process for retiring Small Town Bank's systems.
Once this analysis is completed, the migration team will want to conduct a more detailed study. They will want to know the specific transformation rules that will be necessary to move the Small Town columns into the BigCity systems. Figure 2 is a technical meta data-driven impact analysis that shows each of the key BigCity columns with the corresponding Small Town columns, along with any transformation rules.
Figure 2: Banking MME Report: Column Analysis Consolidation
Figure 2 illustrates a classic MME application. Notice how each domain value for Cust_Tbl is mapped to the corresponding transformation rule, keeping in mind that each of the values for Cust_Tbl has a slightly different transformation rule. The Cust_Tbl attribute is set to "1" when Small Town's customer record has attribute CUSTCDE equal to "3" and the CUSTBAL is greater than $500,000. This analysis has been made much easier because BigCity has a bank-wide (global) definition for affluent customers. For them, an affluent customer is one with a balance greater than $500,000. Now that the migration team has this meta data, the task for integrating all of the Small Town Bank's systems into theirs is much easier.
Next month I will provide healthcare insurance and manufacturing examples of how a MME can provide significant value.
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