A complete guide to data mapping


A data map. Image: Tartila/Adobe Stock Data mapping is a vital step in uncovering essential insights from data. It helps develop relationships between different information entities while reducing information redundancies for more reputable data analysis.

SEE: Data migration screening list: Through pre- and post-migration (TechRepublic Premium)

Data mapping is likewise an essential early step in a number of data motion and transformation projects. In this guide, learn about the various types of data mapping, how it works and some of the best information mapping tools on the market.

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What is information mapping?

Data mapping is the procedure of connecting or matching fields from one source to another source in the information field. The objective behind data mapping is to minimize mistakes in data, make information more accessible and streamline information management.

In addition, data mapping allows information from numerous sources to be combined into one, which could be a central repository. The most typical reasons for data mapping consist of data migration, electronic data interchange, data change and data combination.

SEE: How to produce an information integration technique for your company (TechRepublic)

To comprehend the need for information mapping better, picture having a consumer’s details in multiple databases. In order to prevent software application platforms from counting the exact same consumer multiple times, data mapping is used to map out all entries of that customer to prevent data duplication and optimize data effectiveness and effectiveness. In practice, data mapping is normally a lot more intricate, requiring advanced software to complete the mapping procedure.

Types of data mapping methods


The intricacy and vastness of contemporary business information make it challenging to perform manual data mapping, as manual coding is time-consuming, resource-intensive and susceptible to mistakes. However, manual data mapping uses the benefit of completely personalized maps that abide by the organization’s specific needs.

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Automated data mapping is a method that needs less technical knowledge, uses implementation flexibility and is less time-consuming than manual information mapping. However, it needs users to have information mapping tools, which often include a hefty cost.

If your budget plan permits, a fully automated data mapping process might be worth it, due to the fact that it takes care of the entire data mapping procedure without any need to code.


In semi-automated information mapping, companies take advantage of a balance between versatility and efficiency. Some parts of the information mapping process are automated, while others are by hand coded. It prevails for semi-automated data mapping to use graphic or visual representations of data.

Despite its numerous advantages, it is very important to note that semi-automated data mapping requires coding understanding and technical skills to navigate between automatic and manual data mapping.

Actions in the data mapping procedure

Determine data for transfer, migration and improvement

The initial step in a lot of information mapping procedures is determining which information requires to be transferred to a brand-new location. This is a simple yet crucial action in the process, as it assists specify which tables and fields require to be moved and what format information needs to follow after the relocation. The frequency of information transfer and the strategy utilized for mapping can also be defined in this step.

Map the data

Carrying out information mapping consists of establishing the data circulation, mapping out information from source to destination and keeping procedure logs. For greater frequencies or volumes of information, an automated or semi-automated technique is more effective than manual data mapping.

Transform the information

In this action, the data format can be changed to match the requirements and format restrictions of the location. Changing data at this point makes it much easier to save and use the data field effectively.

Test and release

When information is transformed, it should go through validation and testing through manual, automated or semi-automated techniques. With larger data volumes, a lot of companies choose to follow automatic screening procedures and/or usage automated testing tools.

After testing is complete, information can be deployed to its application. For example, this might involve moving data to a database for business intelligence or analytics.

Maintain information maps with time

Data maps will need routine upkeep and updates to stay up to date with the evolving requirements of business. As datasets and related apps and source systems alter with time, these modifications should be notated and included into the information map.

Advanced information mapping tools and templates can help companies get the most from their data without assigning many resources. Paper-based data mapping still exists; nevertheless, with the huge amount of data and increased frequency of data modifications in a lot of businesses, most of individuals rely on information mapping tools to finish the process.

SEE: Finest information visualization tools and software application (TechRepublic)

Information mapping software uses better transparency, efficiency and dependability than paper-based or manual techniques. It likewise allows services to deal with real-time information while evaluating data flow, material and improvement objectives.

Another key advantage of using data mapping tools and design templates is they enable you to optimize all kinds of data, even in more complex formats. Advanced data mapping tools can assist streamline information mapping while minimizing human error, saving time and optimizing data precision.

Why is data mapping important to information migration projects?

The procedure of moving data from one system to another is known as data migration. Data mapping assistances migration by mapping data source tables and fields within the tables to location fields and tables.

Data migration is one of the most common usage cases for information mapping and most of the times, it is the first step of data migration. As information mapping bridges the gap between source and destination fields, it helps prepare data for the migration procedure and minimizes the danger of information mistake.

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