What Is Data Mapping?
Data mapping is the process of connecting a data field from one source to a data field in another source. This reduces the potential for errors, helps standardize your data and makes it easier to understand your data. Data mapping helps us visualize and connect data fields much like maps can help us visualize the best way to get from point A to point B. And just like taking the wrong turn can mean trouble when you’re travelling, data mapping errors can negatively impact your mission-critical data management initiatives.
Data mapping provides a visual representation of data movement and transformation. It is often the first step in the process of executing end-to-end data integration. Data integration brings together data from one or more sources into a single destination in real time. You need data mapping to understand your data integration path and process. Given the complexity and volume of data in today’s enterprise, data mapping has become more critical than ever, and it requires intelligent, automated solutions for success.
Why Does Data Mapping Matter?
With a backdrop of exploding data volume and variety in the modern enterprise, it’s important to decrease the potential for data errors while increasing the ability to deliver actionable data insights. The data visualization process integrates multiple data sources into data models so you can simplify and combine dispersed data sources.
Data systems each store data in their own way. To analyze and understand your data, use data mapping to standardize data across your enterprise. Data mapping helps ensure that complex data management processes — like data migration, data integration and master data management — yield quality data insights. To automate business processes, you need to integrate data from one application to another. Data mapping bridges the gap by synching data from one format to another.
Business analytics benefit from data mapping. Combining data sets from different sources gives you a holistic view and context for your data. Data mapping can identify subject records across all your data sources. Then, it matches and links records across sources and systems to create a 360-degree view of each individual data subject. Understanding data at such a granular level enables you to achieve deeper insights that can enhance your organization’s decision-making capabilities, giving you a competitive edge.
Data mapping provides the ability to link all data about an individual’s attributes. This helps you establish a single source of truth. Data mapping enables the smooth flow of data through different systems, applications and services. It is a critical element of any data privacy framework. Given today’s changing privacy regulations, automated, reliable data mapping helps you address crucial data access and compliance requirements. Data mapping provides visibility into end-to-end data lineage. It also supports data governance and makes it easier to apply use consent and other rights.
How Does Data Mapping Work?
Data mapping begins with knowing exactly what data you have about any given subject. A data mapping instruction set identifies data sources and targets and their relationships. In today’s complex and growing data enterprises, it’s important for your data mapping capabilities to be part of an intelligent data management platform. That way, you can easily integrate capabilities including data mapping, data integration, data quality and data governance across all enterprise workloads at scale.
Data mapping defines the process for you when integrating data into a workflow or a data warehouse. It helps you connect cloud and on-premises data and applications so you can effectively manage and transfer data between them. Data mapping helps ensure that your data users gain the most value from your data. It paves the way for your data transformation initiatives, including data integration, migration and governance.
Data Mapping Challenges
Here’s a list of four primary challenges that companies may face in implementing data mapping initiatives:
Complex, Manual Data Mapping Processes
In today’s complex world, companies struggle to keep up with the scale of their data environments. It’s critical for data stewards to define data maps that are both strategic and systematic. IT teams need careful planning, the right tools and a clear data mapping roadmap.
Without these capabilities, the data mapping process can overwhelm your team. Automated software solutions are part of the modern approach that's needed. They help you break through the clutter and create data maps with agility and accuracy.
It’s not likely that you will be able to convert data into the actionable insights you need without the ability to handle the four “Vs” of modern data:
- Volume – Amount of data being generated
- Velocity – Speed at which data is being generated
- Variety – Various types of data being generated, which can largely be grouped into three categories: structured data, semi-structured data, and unstructured data
- Veracity – Trustworthiness of the data
Once you can map your data addressing the four “Vs,” you can unleash the fifth “V” — data value. With data value, you can drive deep analysis, accurate reporting and confident decision-making.
If your data mapping is incorrect, your processing time can increase and so can your costs. You can better understand a use case and business outcome with data integration. But you need an intelligent recommendation engine to avoid unnecessary steps and tasks. Your systems and staff may not be able to keep up with data mapping challenges and errors can occur. This slows down processing time and causes issues during data transfer. It also impacts data testing and implementation. You can even lose data, costing more time and resources. Intelligent, automated data mapping can help you get the results you want.
Lack of Trust
You need visibility into end-to-end data movements and changes. Without it, your team may not be able to trust your data or build a data mapping plan. Applications generate their own data formats and definitions may be different. That means you won't be able to trust your data transformation.
Key Components of Data Mapping
To create a data mapping, there are several elements that are needed to map in sequence or in parallel. These elements can be either sources or targets.
Data sources are applications or services where the data will be moved. In data mapping, the first step in managing data sources is to make sure that the configuration you need to access is present. Confirm that your data integration tool has enough visibility into the data sources and data sets you are working with.
Data targets can be any application, process or service that is acting as a destination for the data. Be sure your source type, source object, target type and target object fields are well-defined. For your data to successfully flow from source to target, they must match up with each other.
Fig. 1: Informatica IDMC makes it easy to design data mapping such as in this example where order status fields are mapped.
There are several data transformations you can apply. You can use multiple transformations in a single mapping. The mappings include transformation tasks. The data is processed in the sequence as defined in the workflow. A typical data pipeline has both source and destination. Transformation tasks and other user-defined functions are also included to complete the process. There are hundreds of transformations that can be applied in data mappings.
Here’s a list of some common data transformations:
- Joiner transformation combines data from different sources.
- Filter transformation refines your data per your query. Then it pushes the selected information to target.
- Lookup transformation finds or looks for certain value in a row, table, flat files or other formats.
- Router transformation helps channel the data depending on the data direction or target criteria set.
- Data masking transformation helps hide or encrypt sensitive data as it flows through the data pipeline.
- Expression transformation calculates values from data.
A mapplet is a combination of several transformation rules brought together so that they can be reused.
Data Mapping Parameters and Variables
Mapping parameters are constant value sets for transformation or mapping. You can change them either manually or automatically. Use parameters to hold values each time you run a mapping. You can create reusable mapping with parameterized values.
You can also apply data quality rules to the mappings. For example, setting up an email alert system falls under user defined functions.
Data Mapping Key Features
A data mapping tool is designed to recognize common templates, fields or patterns. It helps match the data from the source to the best possible options at the destination. The data mapping techniques or features you should consider in a solution include:
During data mapping, a simple graphical user interface (GUI) can reduce design time. A digital canvas with drag-and-drop options makes it easier to create the data flow. Data users are often non-technical, and a visual data representation matching the data flow can help them create the right mapping. The alternative to a simple GUI is coding. However, coding often introduces project delays and human errors.
The data source and target should be able to connect with each other with minimum configuration. The data mapping tool should provide easy access to connectors for a wide variety of applications and services.
Wide Coverage of Data Formats and Types
Different systems and applications produce data in different formats, styles and languages. Depending on requirements, the data/field definition varies from system to system. A data mapping tool should be equipped to read and understand the different types of data representations and how they relate to source and destination.
The data needs to be modified and made suitable so that it can be consumed by the subscribers. The data mapping tool should be able to do the basic transformation and standardize the data based on a common definition.
Reusable Data Integration Templates
Parameterization helps in creating reusable templates that can be replicated with similar use cases. This in turn helps in standardizing data pipelines, empowering non-technical users and saving time for integrators.
Once the data is mapped, the tool should let you automate and schedule the data flow. This can increase your team’s productivity because you intervene only during an anomaly.
Advanced Data Mapping Features
Accommodate changes to sources, targets and transformation logic at run time. With dynamic mapping, you can manage frequent schema or metadata changes or reuse the mapping logic for data sources with different schemas.
The blueprint that defines how the data will be structured. Data mapping is also referred to as schema mapping — when the source schema is matched to target schema. Advanced data mapping capabilities can read the drift in source schema and make the necessary changes to the target schema. This prevents the system from breaking when schema change occurs.
Modern enterprises collect data with increasing volume, variety and velocity. As a result, it's difficult to identify and manage data — sensitive or otherwise. Today's data-driven businesses leverage data lake analytics to gain customer insights. When they migrate workloads to the cloud, they need to be able to move fast.
Artificial intelligence and machine learning are well-suited to the task. According to a recent report, 56% of respondents said their organizations were adopting AI and across business functions, AI has already made notable financial impact. In addition, 67% is the average share of respondents reporting a revenue increase via AI adoption.1
Training AI to recognize personal data, as defined by privacy regulations, allows it to scan, match and link millions of records at enterprise scale — quickly and comprehensively. This is the only way to match data at sufficient speed and reliability to accelerate visibility into the mapped data. This delivers faster, more authoritative analytics and business intelligence including use in new applications.
Data Mapping Benefits
Benefits of using data mapping for big data management include:
Enhanced Data Quality
The success of enterprise initiatives depends on finding and fixing quality issues. Data management errors must be identified and addressed. Otherwise, you may face missed revenue opportunities and unnecessary risk.
To ensure that you uncover data quality issues, you must address every domain, application and database in your enterprise. Data mapping is the critical first step to achieve the data quality needed to successfully manage data in your environment and deliver actionable data analytics.
Data governance initiatives demand that your teams collaborate. Then they can define, discover, measure and monitor data through a single source of truth. Data mapping during integration helps ensure that you can deliver holistic governance. That enables your stakeholders, leaders and regulators to effectively access data.
With the growing size of enterprise environments, it’s more important than ever to minimize data errors. It's just as important to maximize actionable insights. Data mapping helps you overcome the volume and variety of data and sources in your data environment.
Data Mapping Use Cases
With data mapping, businesses can extract value out of data. Here are some common data mapping use cases:
Transfer data between storage systems and computing environments with data migration. Database mapping enhances the ability to move data from one database to another. Companies expect code-free data mapping software to perform error-free migration and at the speed of business. For instance, data mapping lets you move data from on-premises to the cloud with agility at scale.
Bring your data together into a single destination with data integration. Data mapping is critical to successful data integration. Effective data integration is possible only when the data source and target repository structures can be mapped together. When data schemas work together, you can leverage the enterprise-grade performance and reliability of cloud computing.
Convert your data from one format or state to another with data transformation. Data mapping is a critical first step to transform data from repository to required format. Advanced data transformation is a comprehensive, enterprise-class solution for any data type of data, regardless of format or complexity.
Electronic Data Interchange (EDI) Exchange
As organizations move to electronic data interchange (EDI) to improve processes and communications, data mapping is key to EDI file conversion. It helps in the process of converting files into specific formats like XML, JSON and Excel. The data user can extract data from different sources and transform data when data mapping makes it more intuitive and understandable.
Companies today are dealing with privacy regulations such as GDPR and CCPA. These regulations help them control the storage and use of consumer information. Companies are required to inventory and responsibly manage all the data they have about individual consumers. Being able to link disconnected bits of information to a specific individual gives organizations the insights they require. Data mapping means they can balance the need to properly enforce privacy policies with the need to make data available for legitimate business uses.
New privacy mandates put individuals in control of their own data. These mandates allow users to demand a full reporting of all the information a company has about them. It lets them get specific information about which applications they consent to with approved use. They can also assert rights over their personal data. This can include the ability to refuse to allow their data to be sold to third parties. It also lets them control the ability to have their data erased (the right to be forgotten) and take their entire data record elsewhere (data portability).
A company can effectively process these requests only if it reliably knows what data it has and how it relates to the individual. Data mapping with automation supports data privacy regulatory compliance by making it efficient and effective to associate, consolidate and manage requests and consents from individual data subjects at scale.
In addition, by enabling central management of personal data from a single location linked to all applications, data mapping makes it easier to apply subject rights processing in a consistent way — protecting customers' data, reducing the risk of accidental noncompliance from data abuses and safely removing users from risky applications of sensitive information based on identity-driven policies.
As privacy regulations become more pervasive, it will be impossible to comply with each new regulation one by one; companies need to address them at scale by operationalizing privacy compliance as a repeatable function. By correlating and connecting data subject records through metadata, data mapping helps support the operationalization of privacy, making it an integral part of automated data management as a whole by enabling safe and trusted use.
Learn more about how data mapping fits into broader privacy regulation compliance requirements.
Data Mapping Examples
Informatica® Intelligent Data Management Cloud™ (IDMC) delivers end-to-end, AI-led data management capabilities including data mapping. A unified, SaaS-based platform, Informatica IDMC lets you align business and technical stakeholders around a foundation of trusted data intelligence and purpose.
Informatica IDMC combines powerful capabilities — including policy and stakeholder management, data discovery, data lineage and data governance. The Informatica IDMC data mapping service delivers enterprise-grade AI-led data management so organizations can provide petabyte-scale data mapping to their data users. Powered by Informatica’s AI engine, CLAIRE®, IDMC provides 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 build a foundation of trusted data intelligence and purpose. Informatica IDMC combines powerful capabilities — including policy and stakeholder management, data discovery, data lineage and data governance. With Informatica data mapping, you can deliver petabyte-scale data mapping to data users.
Coop Alleanza 3.0 leverages Informatica IDMC data mapping service in their enterprise. As Europe's largest consumer cooperative, they have 2.7 million members and 430 stores. Five smaller Italian cooperatives formed the company in a merger. To create a 360-degree view, they needed to combine customer, product and sales data. And they needed to do this without compromising compliance. For example, GDPR requirements were critical to protecting the customer's personal information (PII).
They deployed the Informatica master data management service with data mapping. That helped identify and manage customer data across many internal and external systems. As a result, they are able to protect customer PII. This minimizes risk exposure while personalizing customer experiences.
Companies look to data mapping as the first step on their data modernization journey. Data stewards leverage data mapping techniques to equalize data. It’s an important phase before you can analyze data for business insights and decision making. With Informatica IDMC data mapping service, you can modernize to a diverse multi-cloud ecosystem and democratize data for desired business outcomes.
Resources for Data Mapping
- GDPR Compliance for Dummies: eBook on turning compliance into a competitive advantage
- Data Classification and Mapping for Data Privacy: Video on data mapping
- Reimagining Data Governance: Webinar on GDPR
- Preparing for the CCPA With Privacy Governance That Scales: Webinar on CCPA