What is data mapping? 101 guide

Data mapping connects and transforms data so that you can share it from one system or database to another. It’s like creating a map that outlines how data should move from its source (system A) to its destination (system B).

Let’s say you must transfer customer data from an outdated customer relationship management (CRM) platform to a new one. The source CRM may have a field called “client_name” while the destination uses “customer_name.”

Data mapping connects these two fields, specifying that data from “client_name” maps to “customer_name.”

Without data maps, integrating or migrating data would be messy, time-consuming, and prone to mistakes. It would require piecing together how fields relate between systems manually. Data mapping streamlines this process using an organized and reusable set of mappings.

The data mapping process is relatively straightforward and includes several steps that ensure you integrate correctly.

• Identify data sources: What systems need to share data? These might be databases, applications, APIs, files, and others. Understand the type of data each source contains
• Analyze data structures: Examine the data models, schemas, fields, and data types in each source so you understand how to map fields.
• Map fields: This is the core mapping task. Connect fields from your source to appropriate destinations based on your analysis. Define the ideal data flow.
• Transform data: Cleanse, convert formats, and perform other transformations to make the data compatible with the destination structure.
• Test and validate: Run sample data through the mapping to confirm it works as expected without errors. Refine as needed.
• Create specifications: Document the final mappings for teams to reference during integration.
• Automate integration: Use ETL (Extract, Transform, Load), APIs, or scripts to implement the automated, recurring data transfer based on the mappings.

These data mapping steps enable structured data mapping while maintaining quality. The validation stage also helps you catch bugs before the integration goes live. The result should be an accurate and sustainable database mapping between systems.

Most organizations must integrate, migrate, and report on their data to support business processes and strategic initiatives. Data mapping helps you achieve this.

Without data mapping, data integration often involves tedious manual work that creates weak or inaccurate systems. The mapping process lets you model continuous data flows between systems and create an error-resistant data handling environment.

You can use data mapping to integrate cloud apps, merge databases after acquisitions, comply with reporting requirements, and more.

Data mapping provides countless benefits. It’s a fundamental process that supports reliable and automated data sharing.

Data mapping is crucial for coherently moving data between systems—here’s why.

Seamless data integration

Data mapping ensures different systems can seamlessly share and sync data via automated transfers. You integrate once, and then data flows between systems logically and without manual effort.

Consistent data

Accurate data field mapping ensures consistency across various systems. This consistency is vital for maintaining a reliable source of truth and keeping all views up-to-date and aligned.

Improved data quality

Mapping reduces errors from misaligned data and enables you to transform data to the format you need. The result is higher-quality data in destination systems for better decision-making and analysis.

Supports data migration

Data mapping is essential for migrating data from legacy systems to new platforms. Once you complete the initial migration, data mapping helps with ongoing updates, ensuring a smooth transition and preventing disruptions.

It's critical to data transformation

Mapping enables you to transform data to your desired structure when you transfer it. This gives you flexibility when integrating diverse data sources and systems so you can adapt data as needed.

Like all tasks involving lots of data and intricate processes, data mapping can have challenges.

It’s important to understand and address these hurdles when integrating your data and selecting a data mapping tool.

Here are common pain points to be aware of:

Diverse data

Mapping can be tricky when systems use different formats, standards, and schema. For example, connecting XML to relational data requires careful field matching because of their distinct structures.

Data volume

Handling large amounts of data can be challenging. Mapping tools need to be robust, efficient and have the capacity to support significant volumes. Without proper throttling, mapping gigabytes of data might be impossible.

Sophisticated transformations

Beyond simple field matching, some integrations require enriching data or extensive transformations. This complex logic strains some data mappers and ETL tools, demanding more robust and proven solutions.

Data privacy and security

Mapping involves consolidating data, which raises privacy and security concerns. Access control and de-identification are critical to addressing these issues to ensure you handle sensitive information securely.

Essentially, any business that generates and uses data requires solid data mapping capabilities. Data mapping helps you organize and connect your data, ensuring everything runs smoothly and making it easier to deliver personalized, engaging, and helpful customer experiences.

Let’s look at how these industry examples rely on flexible data mapping tools to support their management and integrations.

Online retail

Online retailers, like Amazon, use data mapping to ensure their buying and selling process is smooth.

This includes connecting product details with inventory, linking customer reviews to the right products, and providing accurate order processing.

Mapping customer data also helps retailers offer personalized recommendations and manage user accounts across its services.

Software platforms

Many enterprise technology platforms, like Microsoft, offer various software offerings and services and rely on data mapping to connect user data across products.

For example, mapping user profiles, permissions, and preferences ensures a consistent experience as users navigate different Microsoft apps.

Data mapping also helps manage enterprise data and supports features like single sign-on.

Social media platforms

Social media platforms, Facebook use data mapping to bring together user-generated content, connections, and engagement metrics.

Mapping user profiles to friend lists, posts, comments, and likes helps create a personalized and engaging social media experience.

It’s also useful for targeted advertising, as it involves mapping user preferences and behavior to optimize ad delivery.

Streaming services

Streaming services, like Netflix, rely on data mapping to blend data about content, user preferences, viewing history, and ratings.

This mapping enables streaming services to deliver personalized recommendations, ensuring users discover content tailored to their tastes. It also helps manage the company’s library of content from various sources.

There are three main approaches to mapping data between systems: manual, semi-automated, and automated. The technique you choose depends on your dataset's size and complexity, your desired level of precision, and your available resources.

Some situations call for more meticulous, manual mapping, while others need an efficient and speedy automated approach.

As data systems become more complex, many organizations use metadata-driven mappers instead of rigid, hand-coded ETL routines. This is where flexible or hybrid mapping tools can offer a competitive advantage.

Let's detail the different data mapping techniques to help you pick the most suitable type.

Manual data mapping

In manual data mapping, data analysts or experts manually identify, match, and define how data from a source system corresponds to fields in a destination system.

It’s best suited for smaller datasets or situations where a high level of understanding or precision is needed, like in complex data structures.

Semi-automated data mapping

Semi-automated data mapping uses a combination of human input and automated tools. The system suggests mappings based on predefined rules or similarity algorithms, and analysts then validate or adjust the suggested mappings.

These systems use an interface for modeling mappings visually without coding and then generate scripts and ETL behind the scenes.

This approach accelerates mapping, balancing efficiency and accuracy. Non-technical users can map systems, while human oversight ensures quality.

Semi-automated data mapping is useful for medium-sized datasets where some automation can enhance the process without sacrificing precision.

Automated data mapping

Automated mappers use algorithms and tools to match and link data fields between source and destination systems without human intervention. They spot patterns and relationships using predefined rules, machine learning, or artificial intelligence.

An automated data mapping system removes most of the manual work but should still have a human validate it. However, these systems are helpful for large datasets where speed is a priority or data structures are relatively straightforward.

Adopting best practices helps make your data mapping efforts more successful and effective, resulting in more accurate, reliable, and scalable integration efforts.

Here are some things to keep in mind when mapping data.

Plan your approach

Before diving into data mapping, develop a comprehensive plan. Understand the business objective, identify data sources, and define mapping requirements.

A thoughtful approach ensures the mapping process aligns with organizational goals and minimizes potential challenges. Mapping without planning typically leads to disorganized projects.

Focus on quality assurance

Prioritize data quality throughout the mapping process. Implement quality assurance checks to validate data accuracy, completeness, and adherence to predefined rules.

Address discrepancies or errors quickly to maintain integrity. Flawed mappings can jeopardize your integration.

Choose the right tool

Selecting an appropriate data mapping tool is essential. Think about the complexity of your data, scalability requirements, and the level of automation you need.

Choose a tool that meets your organization’s mapping needs. For example, Excel can work for simple cases, but more robust ETL tools are better for large projects.

Amplitude offers data mapping capabilities that blends power, flexibility, and ease of use into one handy tool.

Going beyond traditional data mapping, Amplitude is a complete analytics platform that empowers you to understand user behavior, optimize your products, and drive growth.

With enterprise-grade mapping capabilities for managing complex data workflows, you can integrate your data sources into a unified view.

With Amplitude, you’ll benefit from:

• An intuitive visual interface: Non-technical users can map data easily without coding.

• Scalability: Map significant data volumes without throttling or delays.

• Flexible transformation tools: Manipulate and enrich data during mapping with built-in functions and logic.

• Automated mapping suggestions: AI and machine learning recommend potential field mappings to accelerate your projects.

• A unified dataset: Consolidate all customer and business data into a single source of truth.

Integrating data at scale is critical for data-backed organizations. Use Amplitude’s data mapping capabilities to combine data from across your technology stack, unearthing deeper insights that drive better business outcomes.

Reshape your strategies and make more informed decisions. Get started with Amplitude today.