Learn How Data Mapping Supports Data Transformation and Data Integration

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Learn How Data Mapping Supports Data Transformation and Data Integration

One error in data mapping can cause ripples in the organization — learn what data mapping is and how it can be done.

· Big Data Zone ·
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Data mapping is an essential component of data processes. One error in data mapping can cause ripples in the organization, bringing it to ruins through replicated errors and inaccurate analysis. So, if you fail to understand the significance of data mapping or how it’s implemented, you are minimizing the chances of your business becoming a success. 

In this article post, you’ll become aware of what data mapping is and how it can be done.

You may also like: What Is Data Mapping?

Data Mapping Explained

Data mapping involves the process of matching data fields from one database to another database. It is an essential segment of an ETL process that facilitates data migration, data integration, and other significant data management tasks.

Business data is analyzed for business insights after it is homogenized in a way that allows decision-makers access and uses it properly. Currently, data now comes from a lot of sources, and each source can define similar data points in myriad ways. 

With the support of ETL data mapping tools, you can bridge the difference between two systems or data models, to allow data to move from a source in an accurate and precise manner. 

Though data mapping tools are extremely useful for the business, with the amount of data as well as sources increasing, the data mapping process becomes highly complex. And, so one needs access to an automated ETL data mapping tool to make it feasible for large data sets.

Data Mapping Streamlines Data Management

Data management’s most important part is data mapping indeed. In case data is not mapped properly, it can become corrupted as it reaches its destination. Ensuring data mapping’s quality can help organizations receive most out of your data in data migrations, transformations, integrations, and more. 

Data Migration

Data migration involves the process of moving data between systems as a one-time event. Followed by the migration process, the destination is the new source of migrated data, and consequently, the source gets eliminated. Data mapping tools can be used to perform a migration process by mapping source data fields to destination fields. 

Data Integration

Data integration involves the process of integrating various types of applications across the business ecosystem. It can either be scheduled or can be triggered by an event. Similar to data migration, data maps for integration must also match fields such as source and destination. 

Data Transformation

Data transformation involves the process of transforming data from a source format into a destination format. Companies can use data transformation software to cleanse data, eliminating nulls or duplicates, collecting data, enriching the data, or other transformations.

Data Warehousing 

If the objective is to transfer all the data into one source called warehouse for further purpose. As the users run a query or analysis, data is fetched from a warehouse. Data that is present in the warehouse has already undergone migration, integration, and transformation. Data mapping tools allow organizations to ensure that as data comes into the warehouse, it gets to its destination the way it was intended.

Data Mapping Steps

Data mapping can be performed using a set of steps such as:

Step 1: The data that has to be moved is defined here. For data integrations, in this step, the data transfer frequency is also defined. 

Step 2: Next, the data is mapped and source data fields are matched with destination data fields. 

Step 3: After data mapping, transformation is done. 

Step 4: In this step, a testing system is used to sample data from the source, run the transfer to see how it works and make adjustments as necessary.

Step 5: After the data transformation, migration or integration even is scheduled. 

Step 6: In the last step, maintenance is the priority. Data maps are updated as new data sources are appended or the sources change or the requirements at the destination change.

Set Path for Future

Data mapping allows organizations to analyze large streams of data with ease and precision. It helps companies maintain data quality and use real-time data for making better, accurate business decisions. It streamlines the data transformation process, saving time and reducing the possibility of human error. In short, data mapping not only improves data quality but also enhances decision-making.

Further Reading

Data Mapping Tools

Data Mapping: Why It's Important for Integration

Decoding the Unknown Facts About Data Mapping Software

data mapping ,data transformation ,data integration ,data migration ,etl ,data warehouse ,data management ,big data

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