Back in the day, data mapping was paper-based, and it didn’t really pose any problems for organizations as the amount of data was limited. However, in today’s modern digital era, companies have to handle and process large amounts of data. In order to keep up with the pace of data mappings and constant changes, organizations leverage efficient and robust automated data mapping solutions. They help you significantly reduce the risk of human errors and develop reports that help stakeholders make better decisions on the basis of organized data.
Now you may be wondering what data mapping is, how it works, and its process. Read this article to learn!
What is Data Mapping?
When data is taken from one source and connected to another source (called target), this process is known as data mapping.
Data mapping allows you to structure your data in a well-organized manner, so you can easily access it and understand it, while reducing the chances of human error in making well-informed decisions.
For example, your data source may contain the term “United States of America”, whereas, the target data may store it as “USA”.
Hence, it can be said that data mapping helps you in bridging the differences between two sets of data to make the final data more understandable and accurate.
How Does Data Mapping Help Other Data Management Processes?
Data mapping is the primary step toward other crucial data management processes. Premium quality data mapping allows for better data integration, migration, and transformation.
Data Integration – Data mapping for integration lets you match source fields with target fields while removing duplicate information and presenting one stream of information.
Data Transformation – Data transformation is also a part of data mapping as it transforms one format of data into another, like converting an XML file into a CSV file.
Data Migration – During the process of data mapping, the data is basically being migrated from one source to another and the old source is then removed. For example, you may migrate data to a cloud platform like Azure.
What Are the Steps Involved in Data Mapping?
Step #1: Define Data Fields
In order to do data mapping, you first need to identify which data you want to map. For this, you will have to determine which data needs to be migrated and which format you want for the target data.
Step #2: Define the Format of the Destination Data
Here, you will confirm the format of the target data. For example, if your source data has the format (California) and you’re integrating it to its destination (CA), the format CA will be followed in this case.
Step #3: Transformation
In order to establish data transformation rules, you will need to consider whether you’re mapping data according to an automated, semi-automated, or manual process.
Step #4: Test
It’s also essential to assess if your data mapping systems are working fine. For this, you will have to move a sample of data using a test system to see if the process works well and requires any tweaks.
For automated data mapping tools, this process is a breeze, whereas, for manual data mapping, you may need a help of a professional data scientist or developer.
Step #5: Deploy
Once you have confirmed the smooth data mapping process, you can finally deploy your integration, transformation, or migration.
You should also maintain and update your process on a regular basis accordingly.No tags for this post.No tags for this post.