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Industry Specific MME Applications, Part 4

Metadata Management & Enterprise Architecture

This article is adapted from the book Universal Meta Data Models by David Marco & Michael Jennings, John Wiley & Sons, 2004.

In the first three parts of this series on industry-specific managed metadata environment (MME) applications, I walked through examples in the banking, healthcare insurance, manufacturing, national defense and pharmaceutical industries. This month I will conclude this four-part series by addressing the retail and telecommunications industries.

Retail Industry Example

Retail companies were early adopters of data warehousing technology. They have been lagging somewhat on the adoption of MME compared to industries such as healthcare and banking, which were also early data warehouse adopters. The need for MME applications in the retail industry is absolutely critical because retail companies have inflated IT budgets, and there is a great deal of decision-making and client interfacing that occurs among various stores.

MegaMart is a large nationwide retailer with more than 1,000 stores. A few years ago, MegaMart was wise enough to build an MME that would manage both technical and business metadata around their systems. MegaMart has a corporate goal of consolidating three of its current systems into a new enterprise resource planning (ERP) system. In order to accomplish this task, MegaMart needs to know where its customer data resides within these systems. Figure 1 shows where customer data exists by attribute, with the attribute definition and the estimated number of records.



Figure 1: Customer Data by Attribute

If the ERP implementation team didn't have the MME, they would need to manually do the impact analysis shown in Figure 1. Typically, this degree of manual impact analysis takes months to accomplish. An impact analysis generated from the MME takes only hours or days.

Telecommunications Industry Example

A great number of regulations have an impact on the telecommunications industry. It is quite common for firms in this space to pay literally tens and even hundreds of millions of dollars in penalty fees for not adhering to these regulations.

NoTeleCo is a large telecommunications company that provides long-distance and local phone service. NoTeleCo has an extensive MME that targets business and technical metadata. In addition, NoTeleCo has a data warehouse that focuses on phone usage. When a customer places a call through a telephone service provider, the phone lines used typically are not owned by one service provider. Usually, the phone call will go across several telecommunications companies' equipment. As a result, NoTeleCo needs a report (see Figure 2) that shows the amount of phone usage by their customers that is occurring on other carriers' (telecommunications service providers) lines.


Figure 2: Usage Report

A knowledge-worker at NoTeleCo would be working with this report and might want to better understand the Discounted Usage column. Figure 3 shows the worker hotkeying on the Discounted Usage column to get a business metadata definition for the field.


Figure 3: Business Metadata Definition

As the knowledge-worker is looking through the business metadata definition for "Discounted Usage," he sees that discounted usage is defined as "non-prime, holiday and rate specials." These fields are underlined. This formatting lets the user know that business metadata definitions exist for these fields and that the end user can click on these fields to receive a business metadata definition. This type of functionality is common in well-designed MMEs.

In our example, this worker may want to learn when non-prime phone usage rates apply by clicking the non-prime field to get a business metadata definition for this field. Figure 4 shows the definition for "non-prime." The user discovers that non-prime time is defined as phone usage between 8:00 p.m. and 7:00 a.m.


Figure 4: Non-Prime Definition


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.

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