4 Best Practices For Data Mapping

4 Best Practices For Data Mapping

It is important for an enterprise to have a mechanism that matches fields from data sources to the

Published By - Jason Hoffman

It has become more important for companies to maintain data as they get burdened with more information each day. They have maintained an accurate record of the enterprise’s assets. Data mapping is a starting point for a company’s important aspect of critical success.

Data Mapping is the process of mapping target fields to their source files. It is used in various data integration processes like:

  • Data Transformation
  • Data Lineage Analysis
  • Finding out hidden sensitive data
  • Consolidating Data, i.e., combining multiple data into a single, more effective whole.

Data Mapping transforms the data between the source and the destination by looking for any redundancy and helps to find out any private data. It is a type of data dictionary which helps in avoiding potential issues that may surface in the later part of projects and lead to unavoidable delays

A Data mapping specification may contain:

  • Attributes of the source data.
  • Characteristics of the destination file.
  • Translation rules to ease out data migration.

WHAT IS THE IMPORTANCE OF DATA MAPPING?

Identity transform:

This process of data mapping creates a transformation library which provides a base for any future data that has to be maintained.

Data Refinement

Data mapping replaces the old data with a new and more efficient one. It systematically substitutes data from another.

Data conversion

Data Mapping converts the format of one data to another so that a different computer can use it, or operating system.

Data Migration

This helps in moving the private data from one data model to another.  Data mapping software creates a path between the source data to the destination.

Data Integration

Through this process, the data from the source and the destination is connected. It makes it easier to access the data from both the sources.

BEST PRACTICES TO FOLLOW FOR DATA MAPPING

General Data Protection Regulation

According to privacy professionals, data mapping is an essential part of GDPR compliance. According to the compliance, organizations must have a record of each data processing activities. Through data, map companies have full documentation. It also acts as a proof for the supervisory authorities.

Transparent Disclosures and Process Improvements

Data mapping provides transparency to the consumers and improves the business processes and IT systems. It also controls and analyzes the risks and planning the annual budget. The company must have all the records on why the data was processed and segmentation of the personal data, contact information and the destination where the data is transferred.

American Health Information Management Association

AHIMA ensures that the data mapping is done with allegiance to the integrity of data. Healthcare sector nowadays has become more attentive and integrated towards data mapping. AHIMA has listed out pointers that must be ensured during data mapping.

      • A written document on how the data was developed.
      • A testing program that must have the development process, which checks the validity and productibility of the mapping.
      • Creating a compliance system that must be followed in the future.

Maintaining a Specific Data Pattern

Data mapping helps developers to fill the null area with a default value by analyzing any wrong data pattern. It also identifies any personal data and maintains security while diligently handling it.

TOP DATA MAPPING TOOLS

Pentaho

The platform is highly flexible and easy to use. It requires no coding and accelerates the flow of data through the pipeline.

IBM InfoSphere DataStage

This tool modernizes, collects, organizes, analyzes, and infuses solutions for large scale enterprises. The issues here can be resolved quickly, and the mapping features are fit for every need.

Adeptia Integration Suite (AIS)

It is versatile and ensures efficiency in the integration process. It is affordable where one pays for only the integration functionalities which a company needs.

Altova MapForce Platform

This tool has various automation options. It is cheap and has a low-risk integration functionality. It facilitates chained data mappings and provides automated mapping execution.

Talend Data Integration

It is flexible and provides an interface that is simple and understandable. It provides a scalable architecture that responds very fast. It also has an open-source license.

CONCLUSION

Data mapping allows the organizations to store and transfer large amounts of data in a quick period and analyze the root causes of any abnormality. It lowers the cost of business operations and shares files, data, news, and resources. It helps to customize the data collaborating effortlessly and securely with team members.

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