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LESSON - Start with the Business: Why a Data Model Is Key to Successful Data Integration

By Donna Burbank, Senior Director of Product Marketing, CA Technologies

Most organizations today are in some stage of developing large, cross-departmental data integration initiatives. In this information age, data is a strategic differentiator; a retail organization that has a strong vision around its customer information is a step ahead of its competitors, for example. Many of the buzzwords surrounding data integration initiatives are common in both business and IT circles: business intelligence, master data management, data governance, and so on. They all require data integration on the back end. With such a strong business focus on these initiatives, how can we ensure that the business and IT are communicating effectively? One solution lies in building a high-level data model as a communication mechanism.

What Is a Data Model?

A high-level data model conveys the core concepts and/or principles of an organization in a simple, graphical way, using concise descriptions. The advantage of developing the high-level model is that it helps determine common terminology and definitions of the concepts and principles that drive the business.

Everyone Knows What a Customer Is, Right?

The definition of “customer” may change based on a person’s perspective. To the billing department, a customer may be someone who owns a product or service sold by the company and to whom an invoice is sent. To a salesperson, a customer is someone who has not yet bought a product but to whom they hope to make a sale. There are more things we need to clarify, such as whether a customer can only be a person, or whether a company can be considered a customer. The list goes on. A data model helps document these important business distinctions so that systems and reports built off the data are providing the right information. For example, we don’t want to send renewal notices to customers who have not yet purchased. Although these definitions may seem simple, there are often complex distinctions and relationships around the simplest terms that a data model helps clarify.

Avoid a Siloed Approach

An important goal is to align on common terminology, business definitions, and rules, and create a high-level data model to describe core concepts and principles of an organization and what they mean. Identifying what data is used and by whom takes a lot of effort. It’s much easier to focus on your own project or department, or at least it seems that way on the surface. However, this siloed approach may lead to systems that don’t work well together. By involving other groups, it’s possible to leverage work that has already been done without reinventing the wheel.

Once the stakeholders are identified (people, groups, or organizations that can affect or be affected by an action or policy), it’s time to get them to talk to each other. For example, in a bank, the banking and consumer credit departments might consider a customer to be a person who has an existing account with the bank. On the other hand, the marketing department may also use the term to describe people who do not have an account with the bank.

Common terminology, definitions, and rules across departments and projects can break through silos, enabling the organization to operate as a single, powerful unit. Using a data model as the backbone of your data integration initiative helps ensure that business requirements align with the database implementations that support them. Refer to the Silotech case study, page 12, for a real-world example of how data models helped facilitate communication between business and IT stakeholders, resulting in a more efficient and effective data integration effort.

The above article is based on Donna’s recent book, Data Modeling for the Business, coauthored with Steve Hoberman and Chris Bradley. For more information about CA ERwin Data Modeler, visit

This article originally appeared in the issue of .

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