What Is Data Mapping?

Data mapping is the process of linking data fields from one source (such as a database) to data fields in another source. It is a foundational task in data management and the first step in facilitating data integration and migration.

“Data mapping ensures that data can move from a source system to a destination system while preserving its meaning and context,” said Jay Mishra, chief operating officer of data management software company Astera. “It’s like moving into a new home — the key is to place everything in the right spot so it serves its intended purpose.”

Why Is Data Mapping Important? 

Mapping is a vital first step in data management and governance. If data is funneled into a new database without first being organized, named consistently and routed to a unified data set, analysts and artificial intelligence won’t be able to read it or understand its context properly. This can cause analytical chaos — skewing results or causing data loss. 

Even minor incongruencies, like duplicate fields or mismatched names, can cause confusion and diminish data quality.

“Data mapping is one technique that can help data-driven organizations [make reliable] decisions and generate faster insights,” said David Scroggins, director of software engineering at data science company 84.51°.

Without the clear definitions afforded by data mapping, data can be rendered useless, according to Rogers Jeffrey Leo John, chief technology officer at generative AI company DataChat.

“If you get your definition wrong, you can’t do any analytics, machine learning or AI,” John said. “The critical first step is to identify what data you want and define your map in a systematic way.”

In short, data is only as valuable as the quality of its data map.

“Data mapping [helps] you see who is accessing which part of the data in your organization and how is that being used by the business,” John said. 

How Does Data Mapping Work? 

In its most basic form, data mapping is the act of connecting data fields from a source data set to those of a target data set. In a simple example, the fields are mapped on a one-to-one basis.

For instance, in a simple map, “client_code” in the source database corresponds to “client_code” in the destination. In a more complex map, “client_code” might correspond with “client_id.” In a multi-source map, three “client” fields from multiple data sets are funneled into a single destination field. 

Creating a data map involves recording and organizing a piece of data’s source, location, format, destination and relationship to other data. Designed to standardize data across an organization, the process requires data scientists to decide how data should be named and stored.  

For example, a company might collect data across multiple social media platforms and web pages. To effectively analyze this data, the company must map it — connecting each field to its corresponding field in the consolidated destination. 

What Is Data Mapping Used for?

Mapping is used when data from one source must converse with or be moved to a second source. Common use cases for data mapping include:

Data Warehousing 

Much like its physical counterpart, data warehousing refers to the act of storing all data in a single source. This unified source might be used to generate reports, queries or analyses. 

Data Migration

Data migration refers to a one-off transfer of data, typically from a legacy system to an updated destination. In this case, the original data source is often sunset. 

Data Transformation

In the data transformation process, data is converted from its raw format to the destination’s format. Examples include updating the data type, enriching data or removing redundancies. 

Data Integration

Usually occurring on a schedule or triggered by a recurring event, data integration is the ongoing process of transferring data from one system to a destination. 

Data Mapping Steps

Mapping is a multi-step exercise, often carried out by database administrators.

1. Identify the Data

The first step in the mapping process is identifying the data that needs to be organized or moved. The type of project will determine how the data should be formatted.

2. Define Rules 

Data relationships, significance and hierarchy must be defined. At this stage, naming conventions should be clearly identified in the source database and standardized in the destination database. Schema logic — a system for recording how data is organized within a relational database — helps map data sources to their targets.

3. Map the Data  

Next, alignment is created between data fields. Source fields should be matched with their destination using code, a visual interface or a natural language processing system. Logs should be kept to help catch errors or latent bottlenecks. 

4. Test the System

Before running full data sets through the newly mapped system, a test should be completed using a small sample of data. Results should be checked manually by a data scientist.  

5. Deploy the Mapped System 

Once the map has been adjusted according to the test results, the full migration or integration can be scheduled.

6. Maintain the Mapped System

Data maps are dynamic — they will need to be updated when new data sources are changed or added.

Data Mapping Techniques

Different projects call for varying data mapping strategies, each defined by the level of human intervention. Techniques include:

Manual Data Mapping

As its name suggests, manual data mapping is carried out entirely by humans. This technique involves the heaviest lift, requiring data scientists to connect data sources via code. Typically, the map is created using coding languages like Java, C++ or SQL. It may call for extract, transform and load (ETL) skills.

While manual mapping is completely customizable to a specific project, it is time-consuming and prone to errors. It also precludes the inclusion of real-time data and makes change difficult. 

Semi-Automated Data Mapping

Semi-automated mapping elevates the manual process with software or coded systems. In this technique, data scientists may utilize a visual interface to create the schema map. The data mapper may match fields using a drag-and-drop or line-drawing function. Despite the automated elements of this technique, the process may still require code, depending on the complexity of the project. 

Automated Data Mapping

Many contemporary data mapping platforms are fully automated. This technique does not require coding or manual mapping; instead, it relies on natural language processing to match data fields and define schemas. Automated data mapping is an appropriate choice to support real-time analytics. 

Whether data mapping is fully automated or less so, it always requires the supervision of a domain expert.

 “There will always be times when maps need to be manually generated,” Scroggins said. “But the more we can automate the creation of data maps, the more time we can focus on delivering [data] and [its] corresponding insights.”

Frequently Asked Questions