The digital world has a wide range of languages, which can confuse us, especially when data transfer happens from one place to another. Fortunately, a procedure known as Data Mapping acts as a translator to guarantee your databases can interact more efficiently from one nation to another. Data mapping is assigning or mapping one piece of data, known as the source, to its destination, known as the target. The objective is to make the data in your business more organized, consistent, and accessible to your team or customers.

Assume you're gathering consumer data from desktops, mobiles, and servers. This information can be used for many purposes, including sponsored advertising, email marketing, push notifications, and more.

The only issue is several of these platforms have their language. It might make this process difficult to use all information. Data mapping is a bridge between many data transfer processes. This bridge also allows your data to travel effortlessly, merge or alter from its origin to its destination. And all this data also needs Privacy Management

How does data mapping fit into your overall data strategy framework?

Data mapping has several applications and an ultimate aim. Instead, data mapping is the initial stage in carrying out several data-related tasks, such as:

Data Integration 

Data Integration refers to coordinating your data and standardizing two into a single stream. Consider a marketing and sales team merging their lead lists with contact information. Data integration removes duplicate information and consistently formats the data.

Data Migration 

It is the process of moving data from one place in the form of storage type, format, or IT system, to another that is comparable but fundamentally different. Moving data from an on-premises data center to a cloud platform is one of the most typical forms of data migration for modern enterprises, such as DataBench.  

Data transformation 

It refers to converting unorganized data from one platform to another. The most typical example is translating data from an XML file to a CSV file.

Data Mapping Techniques

1. Automated 

Automated data mapping needs the use of specialist software to fit fresh data to your current structure or format. These solutions frequently depend on machine learning to update and monitor your data models constantly. There are several benefits to automated data mapping and DataPrivacy, including:

  • Data extraction from hundreds or thousands of sources in real-time.
  • Allowing non-technical personnel to perform efficient information operations through a user-friendly interface.
  • Seeing your data flow displayed with visually appealing graphics.
  • Alerts when problems arise.
  • Troubleshooting those issues in preparation for targeted remedies.

Some companies don’t invest in these kinds of software, but if you find the right software, it will save you a lot of time. 

2. Semi-Automated Data Mapping 

Semi-automatedData Mapping is a procedure that combines the benefits of entirely automated and manual data mapping. Developers deal with software that links various sources and their objectives.

After mapping the process, someone from your team will personally check the system and make any necessary adjustments. When working with small volumes of data for simple connectors, migrations, or transformations, this is a useful method for smaller teams on a tight budget.

3. Manual

Manual data mapping involves a developer who can write rules to move or inject data from one source field to another. Because of the vast volume of data available to modern organizations, developing a solid data management plan without the assistance of automated technologies is becoming difficult. Instead manual data mapping is a solution for a one-time activity such as data storage when the database isn't too broad.


Data Mapping is important for the success of many data operations. A single failure in data mapping may resonate across your business, resulting in repeated mistakes and, eventually, inaccurate analysis. Almost every business transfer data between systems at some time and comparable data is stored in various ways by different systems. So to transport and consolidate data for analysis or other purposes, a road map is required to ensure that the data arrives at its destination appropriately.