Companies are dealing with more data sources than ever – sales figures, customer profiles, inventory updates, you name it. Data professionals say, on average, data volumes are growing by 63% per month in their organizations. Data teams are struggling to ensure all that data hangs together across systems and is accurate and consistent. 

Bad data is bad for business: Gartner says poor-quality data costs organizations $12.9 million annually; IBM’s oft-cited study says the yearly cost is $3.1 trillion in the U.S. alone! 

Beyond the immediate impact on revenue, poor-quality data increases the complexity of data ecosystems over the long term and leads to poor decision-making. Fortunately, data mapping can help organize that chaos into a structured, meaningful format. 

Below is part one of a three-part article series in which I’ll dive into what, why, and how of data mapping. 

Data Mapping Defined

Data mapping is the process of associating data attributes from one data source to another. It enables data teams to integrate, transform, and use data effectively. 

Data mapping defines how data in one format corresponds or relates to data in another and involves mapping data structures, fields, and relationships between source and target data sets. It’s commonly used in various fields, such as data governance, data integration, data migration, and data transformation.

For instance, one system may call a customer’s age “Age” and another may use “Birth Year.” Simple mapping would just map “age” to “birth year,” and the values wouldn’t change. But if someone is 52, they weren’t born in ’52! 

This is where data transformations come in and where mapping is very powerful. Taking “Age” (field) and 52 (value) and creating a mapping rule that subtracts the age value from the present year will give the value of 1972, which can be put into the “Birth Year” field.

Data Mapping Components

• Source data is the original data that can be stored in databases, files, APIs, or other data repositories. Understanding the source data’s structure, format, and content is critical for effective mapping.
• Target data, also known as destination data, is where the mapped data will be loaded or transformed. 
• Data elements are the data fields and their types. Armed with the schema of the source and the schema of the target, an important part of data mapping is identifying specific attributes between source and target data sets.
• Mapping rules define how each of the data elements from the source maps to a corresponding element in the target and answer the question, “What needs to happen?” These rules cover data transformations, validations, default values, and the business logic applied during the mapping process.
• Data transformations are where the magic happens to achieve the ultimate goal of clean and well-structured data. It is all too often done manually, but should include as many automated tasks (transformations) as possible that convert, clean, or aggregate data as it moves from the source to the target. Transformation rules specify how data should be modified, formatted, or calculated during the data mapping process.

7 Reasons Why Data Mapping Is Important

1. Data integration and consolidation
   Data mapping enables data integration from multiple sources (like databases, applications and APIs) into a coherent data model, helping create a single source of truth and eliminate data silos.
2. Data migration and system upgrades with ease 
   Data mapping helps maintain data integrity, consistency, and compatibility between the old and new environments. It also helps map old data structures to new ones, ensuring data compatibility and integrity. 
3. Data transformation and cleaning
   Data mapping facilitates data transformation processes by defining how data should be converted, formatted, and manipulated to meet specific requirements, improving an organization’s data quality and reliability.
4. Business intelligence and analytics
   Mapping data elements allows organizations to identify inconsistencies, redundancies, and errors, and implement corrective measures to enhance data accuracy and consistency. 
5. Data governance and compliance
   Data mapping contributes to data governance practices by establishing clear data lineage, metadata management, and data ownership. It supports regulatory compliance by ensuring data mappings adhere to industry standards, privacy regulations, and data security protocols. 
6. System interoperability and integration
   Data mapping facilitates data exchange between different systems to optimize workflows and supports API integrations, data-sharing agreements, and collaboration between internal and external stakeholders.
7. Decision-making and strategy
   Better data leads to better business decisions, and by analyzing mapped data, it is easy to identify trends, patterns, and opportunities that will help measure an organization’s key metrics, track business performance, and assess the impact of decisions made.

Part two of this series will explain how data mapping works and address common techniques. I’ll also map out key challenges and considerations that should be top of mind.