What is data mapping? (2023)

data transformations

There are several data transformations that you can apply. You can use multiple transformations in a single mapping. Mappings include transformation tasks. Data is processed in the order defined in the workflow. A typical data pipeline has source and destination. Transformation tasks and other custom functions are also included to complete the process. There are hundreds of transformations that can be applied to data mappings.

Here is a list of some common data transformations:

  • carpenter makeovercombines data from different sources.
  • Filter Transformationrefines your data according to your query. It then sends the selected information to the destination.
  • Lookup-Transformationfinds or searches for specific values ​​in rows, tables, flat files, or other formats.
  • router transformationhelps channel the data depending on the direction of the data or the defined target criteria.
  • data masking transformationhelps hide or encrypt sensitive data as it flows through the data pipeline.
  • expression transformationcalculates values ​​from data.


A mapplet is a combination of several transformation rules put together in a way that allows them to be reused.

Data mapping parameters and variables

Mapping parameters are constant sets of values ​​to transform or map. You can change them manually or automatically. Use parameters to store values ​​whenever you run a mapping. You can create reusable maps with parameterized values.

custom functions

You can also apply data quality rules to mappings. For example, setting up an email notification system falls under custom features.

Main features of data mapping

A data mapping tool is designed to recognize common patterns, fields, or patterns. This helps match the source data with the best possible options on the target. Data mapping techniques or functions to consider in a solution include:

Simple GUIs

When mapping data, a simple graphical user interface (GUI) can reduce design time. A digital canvas with drag and drop options makes it easy to create the data flow. Data users are generally non-technical, and a visual representation of data that fits the data flow can help them create the right mapping. The alternative to a simple GUI is coding. However, coding often introduces design delays and human errors.

simple connections

The data source and destination must be able to connect to each other with minimal configuration. The data mapping tool must provide easy access to connectors for a variety of applications and services.

Broad coverage of formats and data types

Different systems and applications generate data in different formats, styles and languages. Depending on the requirements, the definition of data/fields varies from system to system. A data mapping tool must be equipped to read and understand the different types of data representations and their relationship to source and destination.

The transformation option

Data must be modified and suitable for consumption by subscribers. The data mapping tool must be able to perform the basic transformation and standardize the data based on a common definition.

Reusable data integration templates

Parameterization helps to create reusable templates that can be replicated with similar use cases. This, in turn, helps standardize data pipelines, empowers non-technical users, and saves integrators time.


Once the data is mapped, you can use the tool to automate and schedule the flow of data. This can increase your team's productivity, as you only intervene during an anomaly.

Advanced data mapping features

dynamic mapping

Consider changes to sources, targets, and transformation logic at runtime. Withdynamic mappingyou can manage frequent schema or metadata changes or reuse mapping logic for data sources with different schemas.

schedule detour

The blueprint that defines how the data will be structured. Data mapping is also known as schema mapping - when the source schema matches the target schema. Advanced data mapping features can read the mismatch in the source schema and make the necessary changes to the target schema. This prevents the system from crashing when there is a schema change.

AI/ML enabled

Modern businesses collect data in increasing volume, variety and speed. As a result, it's difficult to identify and manage data – confidential or otherwise. Today's data-driven companies use data lake analytics to uncover customer insights. When migrating workloads to the cloud, they need to be able to move quickly.

artificial intelligence andmachine learningare suitable for the task. According to a recent report, 56% of respondents said their organizations are embracing AI and that AI is already having a notable financial impact across all business functions. Additionally, the average proportion of respondents reporting an increase in sales with AI adoption is 67%.1

Training the AI ​​to recognize personal data in accordance with data protection regulations allows it to quickly and comprehensively scan, combine and join millions of enterprise-grade datasets. Only then can data be compared with sufficient speed and reliability to accelerate the visibility of mapped data. This delivers faster, more meaningful business intelligence and analytics, including use in new applications.

Benefits of data mapping

Benefits of using data mapping tobig data managementto contain:

Improved data quality

The success of business initiatives depends on finding and fixing quality issues. Data management errors need to be identified and corrected. Failure to do so could result in lost revenue and unnecessary risk.

To ensure discovery of data quality issues, you need to address all of your organization's domains, applications, and databases. Data mapping is the critical first step to achieving the data quality you need to successfully manage the data in your environment and deliver actionable data analytics.

data office

Data governance initiatives require your teams to work together. Then they can define, discover, measure, and monitor data about a single source of truth. Data mapping during integration helps ensure that you can provide end-to-end governance. This allows your stakeholders, executives and regulators to efficiently access data.

As enterprise environments grow, it's more important than ever to minimize data errors. Equally important is maximizing actionable insights. Data mapping helps you manage the volume and variety of data and sources in your data environment.

Use cases for data mapping

Data mapping allows companies to extract value from data. Here are some common use cases for data mapping:

data migration

Transfer data between storage systems and computing environmentsdata migration. Database mapping improves the ability to move data from one database to another. Companies expect no-code data mapping software to perform error-free migration and streamline business. For example, with data mapping, you can move data from on-premises to the cloud in an agile and scalable way.

data integration

Gather your data in a single destinationdata integration. Data mapping is critical to successful data integration. Effective data integration is only possible if the data source and target repository structures can be mapped together. When data schemas work together, you can enjoy the performance and reliability of enterprise-grade cloud computing.

data transformation

Convert your data from one format or state to another withdata transformation. Data mapping is an important first step in transforming repository data into the required format. Advanced Data Transformation is a comprehensive, enterprise-class solution for any type of data, regardless of format or complexity.

Electronic Data Interchange (EDI).

How organizations moveElectronic Data Interchange(EDI) To improve processes and communication, data mapping is critical for converting EDI files. It helps to convert files into specific formats like XML, JSON and Excel. The data user can extract data from different sources and transform it when data mapping makes it more intuitive and understandable.

Privacy Policy

Companies today deal withPrivacy Policysuch as GDPR and CCPA. These regulations help them control the storage and use of consumer information. Businesses are required to responsibly inventory and manage all data they hold about individual consumers. The ability to link disconnected information to a specific individual gives companies the insights they need. Data attribution means they can balance the need to properly enforce privacy policies with the need to make the data available for legitimate business purposes.

user privacy

Neuprivacy mandatesGiving individuals control over their own data. These mandates allow users to request full reports of any information a company has about them. This allows them to get specific information about which apps they consent to approved use. You can also claim rights to your personal data. This may include the ability to opt out of having your information sold to third parties. It also allows them to control the ability to erase their data (the right to be forgotten) and move their entire record elsewhere (data portability).

A company can only deal with these requests effectively if it has reliable knowledge of what data it holds and how it relates to the individual. Data mapping with automation supports data protection compliance by enabling efficient and effective assignment, consolidation and management of individual data subject requests and consents at scale.

Furthermore, by enabling centralized management of personal data from a single location linked across all applications, data mapping facilitates consistent processing of data subject rights - to protect customer data, reduce the risk of non- accidental compliance due to data misuse and safely remove risky application users with sensitive information based on identity-driven policies.

As data protection regulations become more widespread, it will be impossible to comply with each new regulation individually; Organizations need to address them at scale by operationalizing data privacy compliance as a repeatable function. By correlating and connecting data subject recordsmetadata, data mapping supports the operationalization of data protection and makes it an integral part of automated data management as a whole, enabling secure and reliable use.

learn more abouthow data attribution fits into broader privacy compliance requirements.

Examples of data mappings

computer Science®Intelligent data management cloud™ (IDMC) provides end-to-end AI-driven data management capabilities, including data mapping. With Informatica IDMC, a unified SaaS-based platform, you can bring business and technical stakeholders together on a foundation of purpose and trusted data intelligence.

Informatica IDMC combines powerful capabilities including policy and stakeholder management, data discovery, data lineage, and data governance. Informatica's IDMC Data Mapping Service provides enterprise-class AI-driven data management, enabling organizations to deliver petabyte-scale data mappings to their data consumers. Powered by Informatica's AI engine,CLAIRE®IDMC offers the only modern metadata-driven intelligent data mapping service that works at any scale across multiple cloud ecosystems.

With IDMC, you can align your business and technical stakeholders. Then they can lay a foundation for purpose and intelligence from trusted data. Informatica IDMC combines powerful capabilities including policy and stakeholder management, data discovery, data lineage, and data governance. With the Informatica Data Map, you can provide data consumers with a petabyte-scale data map.

Coop Allianz 3.0uses the Informatica IDMC data mapping service in your company. As Europe's largest consumer cooperative, it has 2.7 million members and 430 branches. Five smaller Italian cooperatives formed the company through a merger. To create a 360-degree view, customer, product, and sales data needed to be combined. And they had to do this without compromising compliance. For example, GDPR requirements were essential to protect customer personally identifiable information (PII).

They provided Informatica's Master Data Management Service with data mapping. This helped identify and manage customer data in many internal and external systems. This allows them to protect customers' PII. This minimizes risk while personalizing the customer experience.


Organizations see data mapping as the first step in their data modernization journey. Data stewards use data mapping techniques to balance data. This is an important stage before you can analyze data for business insights and decision making. With the Informatica IDMC data mapping service, you can modernize to a diverse multi-cloud ecosystem and democratize data for the business outcomes you desire.

Resources for Data Mapping

  • GDPR Compliance for Dummies: eBook on turning compliance into a competitive advantage
  • Classification and assignment of data for data protection: Data mapping video
  • Rethinking Data Governance: Webinar on GDPR
  • Prepare for CCPA with Privacy Governance at Scale: CCPA webinar
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