What is Data Mapping?

A Data Mapping Specification is a special type of data dictionary that shows how data from one information system maps to data from another information system. Creating a data mapping specification helps you and your project team avoid numerous potential issues, the kind that tend to surface late in development or during user acceptance testing and throw off project schedules, not to mention irritating your stakeholders.

(By the way, we cover data mapping specifications in more detail in Data Modeling for Business Analysts – a virtual class covering the most critical data modeling techniques you need to know.)

Data Mapping Specifications are particularly valuable in the following types of projects:

  • Data Migration – When source data is migrated to a new target data repository.
  • Data Integration – When source data is sent to a target data repository on a regular basis and the two data sources do not share a common data model. The integration can happen hourly, daily, weekly, monthly, or even in real-time as is typically required for a system integration.

These might sound similar, and they are. The primary difference between the two is that after a data migration project is complete, the original source data is no longer used or maintained while after a data integration project is complete, both data sources are maintained.

The Key Elements of a Data Mapping Specification

Essentially a data mapping specification will analyze, on a field-by-field basis, how to move data from one system to another.  For example, if I were to orchestrate a data feed from the Bridging the Gap article repository to a search engine, I would want to map key attributes of the article, such as the title, category, and content to the attributes specified by the search engine. This analysis exercise would ensure that each piece of information ended up in the most appropriate place in the target data repository.

To achieve this goal, a Data Mapping Specification contains the following elements:

  • List of attributes for the original source of data (often with additional information from the data dictionary)
  • A corresponding (or “mapped”) list of attributes for the target data repository (again, with additional information from the data dictionary)
  • Translation rules defining any data manipulation that needs to happen as information moves between the two sources, such as setting default values, combining fields, or mapping values

Data Mapping is About Resolving Potential Issues

Creating a Data Mapping Specification requires discovering and resolving potential issues prior to the data mapping being implemented. In data migrations and integrations, any number of differences between how data is stored can cause data to be lost or mis-represented.

For example, it may be that your source data has a text field and your target date repository uses an enumerated list. Without analyzing the data and providing logic for mapping the text values to the allowable list values (or initiating appropriate data clean-up efforts), you are likely to experience unexpected errors during the system migration.

A Data Mapping Sample Template

Here’s a simple data mapping template and example you can use to see how this works in action. It’s a hypothetical example assuming that we’re sending a data feed from the Bridging the Gap article repository to a search engine.

As you can see, even a simple data mapping exercise encounters potential data mapping issues that would best be handled proactively in a business-centered way than retroactively in a technically-focused way.

Solid business analysis through data modeling will prevent these issues before they happen, by discovering them in advance, collaborating with business and technical stakeholders to find feasible solutions, and initiating any appropriate data clean-up and normalization efforts.

>>Learn More About Data Modeling

We’ve published a 10-article series on getting started with data modeling, where we are covering the most frequently asked questions business analysts have about applying data modeling techniques.

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