Designing a Data Mapping Plan

Data mapping is the process of matching fields from one data source or dataset to fields in another data source or dataset. This exercise helps identify how data in one system or format corresponds to data in another system or format.

As an essential part of data management, data mapping facilitates various data-related tasks, including data transformation, integration, migration and governance.

It is important to remember that the first step in data mapping is examining the structure and contents of data in multiple sources to understand how they relate to each other. Here are three common ways that data mapping is done:

1. Source-to-Target Mapping: One of the most common types of data mapping is source-to-target mapping, where data fields or columns from a source system or format are mapped to corresponding data fields or columns in a target system or format. This is essential in data integration and migration projects.
2. Business Logic Mapping: For large-scale data platform projects, data mapping usually involves mapping business rules or logic from one dataset or system to another. This is important when data transformations are needed during data integration or migration.
3. Data Transformation: Data mapping often goes hand-in-hand with data transformation, where data is converted, cleansed, or enriched as it moves from one source to another. Transformation rules can be defined as part of the data mapping process.

Choosing the right data mapping solution often depends on your specific needs and the scale of your data mapping project. There are various tools and software options available, ranging from simple spreadsheet-based solutions like Microsoft Excel to more advanced data solutions like Erisna *shameless plug*.

Advanced data mapping solutions typically include several key components that help organizations efficiently store, manage, transform and analyze their data. These components may vary depending on the specific software, but here are some common key components of a data mapping solution:

1. Data Source Connectivity: The ability to connect to various data sources, such as databases, spreadsheets, APIs, and flat files, is essential. This allows the solution to access and retrieve data from diverse sources.
2. User-Friendly Interface: A user-friendly data mapping interface where users can visually define and create data mappings. This component allows users to specify how data from different sources should be transformed and integrated. It should also give the user a simple and intuitive tabular view of the source-to-target data mappings.
3. Data Dictionary and Documentation: Metadata helps users understand and document the characteristics of the data being mapped, such as data types, relationships, and descriptions. Effective metadata management is crucial for keeping track of data that has been mapped.
4. Data Mapping Rules: A mechanism for defining rules and logic to guide the data mapping process. These rules ensure that data is mapped accurately and consistently.
5. Data Transformation Engine: This engine enables data manipulation, cleansing, and transformation. It should support various transformation functions, such as filtering, aggregating, joining, and formatting data.
6. Data Validation: Tools for validating and testing data mappings to ensure that the transformed data meets quality and accuracy standards. This may include data profiling, data quality checks, and debugging features.
7. Data Lineage and Impact Analysis: The ability to trace data lineage from source to destination and perform impact analysis to understand the consequences of changes in data mappings.
8. Compliance and Governance: Features to support data governance, compliance with regulations (e.g., GDPR, HIPAA, CCPA, CPRA), and data stewardship by providing tracking, auditing, and governance capabilities.

Final Thoughts

In summary, data mapping is a fundamental step in managing and utilizing data effectively. It enables organizations to bridge the gap between disparate data sources, ensuring data consistency, accuracy, and compatibility when integrating, migrating, or transforming data.

When selecting a data mapping solution, consider factors such as your organization's budget, the complexity of your data mapping requirements, the need for automation, scalability, and the level of support and training required. It's also essential to assess whether the solution integrates well with your existing data infrastructure and aligns with your data governance and compliance needs. Additionally, consider evaluating the solution through proof-of-concept or trial periods to ensure it meets your specific project goals.

Ultimately, a good data mapping platform should support an organization's efforts to achieve data consistency, quality, security, and compliance while facilitating data-driven decision-making. It should align with the organization's goals and regulatory requirements while being adaptable to changing business needs.

Erisna is an intuitive, single-view data governance platform that enables organizations to work with high-quality data. With tools for Data Discovery, Cataloging, Mapping and Validation that can be understood across the business, Erisna enables data teams to drive efficiency and cost savings when working with data.