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What is the role of data mapping in data conversion?

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Data conversion is the process of transforming data from one format or structure to another, usually to make it compatible with a different system or application. Data mapping is a crucial step in data conversion, as it defines how the source data elements relate to the target data elements, and what rules or transformations apply to them. In this article, you will learn what is the role of data mapping in data conversion, and what are some of the tools and techniques that can help you with this task.

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1 Why is data mapping important?

Data mapping is important because it ensures that the data conversion is accurate, consistent, and complete. Without data mapping, you may end up with data that is corrupted, incomplete, or incompatible with the target system or application. Data mapping also helps you identify and resolve any data quality issues, such as missing values, duplicates, errors, or inconsistencies. Data mapping also helps you document and communicate the data conversion process, which can facilitate testing, validation, and maintenance.

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2 How to perform data mapping?

Data mapping can be performed manually or automatically, depending on the complexity and volume of the data, and the availability and quality of the metadata. Metadata is the information that describes the data, such as its name, type, format, length, and meaning. Manual data mapping involves creating a spreadsheet or a document that lists the source and target data elements, and their corresponding attributes and rules. Automatic data mapping involves using a software tool that can scan the source and target data structures, and generate a data map based on predefined or custom rules.

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3 What are some data mapping challenges?

Data mapping can be a difficult task due to the various differences between source and target data structures, elements, systems, and quality standards. These discrepancies may require aggregation or disaggregation of data elements, renaming or conversion of data values, additional calculations or transformations, and cleaning or filtering of data values. All of these processes must be completed in order to ensure that the source and target data are properly mapped.

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4 What are some data mapping best practices?

To overcome the data mapping challenges, you should understand the source and target data requirements, and gather as much metadata as possible. Utilizing a data mapping tool that can automate and simplify the process, as well as provide features such as data profiling, validation, testing, and documentation is recommended. Additionally, it is essential to define clear and consistent data mapping rules, documenting them in a data dictionary or a data map report. Lastly, testing and verifying the data mapping results to ensure that they meet the data conversion objectives and expectations is critical.

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5 What are some data mapping tools?

Data mapping tools are aplenty in the market, each with its own features, advantages, and disadvantages. Microsoft Excel is a popular option for simple and manual data mapping tasks like creating data maps, applying formulas, and performing basic data conversions. Altova MapForce is another option for complex and automatic data mapping tasks like mapping data from various sources and targets, applying functions and filters, and generating code or scripts for data conversion. For large-scale and enterprise-level data mapping tasks, Informatica PowerCenter is the go-to tool. It can be used for mapping data from multiple sources and targets, applying transformations and validations, and executing data conversion workflows.

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Guilherme Schneider

COO LATAM @ Stefanini | Business and Digital Transformation | Strategy | Innovation | AI | Doctorate Candidate | Leadership | Cybersecurity
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Some of the available tools are Informatica Powercenter, Sas, Tableau Prep, Erwin data modeler, between other examples. Talend Open Studio is an example of open source tool available.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What is the role of data mapping in data conversion?

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1 Why is data mapping important?

2 How to perform data mapping?

3 What are some data mapping challenges?

4 What are some data mapping best practices?

5 What are some data mapping tools?

6 Here’s what else to consider

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