Data mapping is a process used to transform data from one form to another while ensuring that usability and accuracy of the data is not compromised. Data mapping is sometimes confused with data migration, which is a subset of data mapping. But there are distinct differences between these two processes.

What is Data Mapping?

Data mapping is the process of defining the transformation of data from one form to another. It is a key step in the data migration process. Data mapping can be used to define the transformation of data from one form to another, or it can also be used as a tool for defining transformations used in ETL processes. The overall goal is to transform data into its final format for consumption by an application or system.

Data mapping ensures all aspects of source data are captured accurately and consistently while it is transformed into the target format. This lets you make decisions on how you can manage your source data input into the new system or process without worrying about missing values, improper formatting, or missing fields that may have been missed during the transformation.

Business Process Transformation

Business Process Transformation (BPT) is a process of transforming data from one form to another so usability and accuracy of the data is not compromised. Business Process Transformation can be categorized into:

Data mapping

Data extraction, transformation and loading (ETL)

Data mapping is a process of mapping data from one form to another. There are two types of data mapping:

Technical transformation

Logical transformation

Target Data Structure

A data map describes the target structure of a given dataset and the relationships between each piece of data. The Data Map presents all information about your dataset in a single place. This includes:

• What fields are included?

• Which fields are mandatory and which are optional?

• Do any fields contain special characters to be considered when analyzing or visualizing them (e.g., commas)?

• Is there any missing data, or have they been normalized via previous versions of the same dataset?

This example shows how we might answer these questions for an open government dataset called “State Population Counts”:

Business Rule and Guideline Documentation

Data mapping is the process of documenting business rules, guidelines and logics.

Business Rule Documentation: Documenting what rules govern a business process and how they interact with one another. For example, "If a customer’s order is above $200, they qualify for free shipping". The rule can be applied to every customer in the system or only some customers depending on their order value.

Business Guideline Documentation: Documenting best practices on how to achieve specific goals within the given environment (i.e., reporting). These guidelines typically cover processes like building reports or retrieving data from other systems (i.e., ERP).

Business Logic Documentation: Documenting how certain situations are handled by an application; for example, if a user has been inactive for more than 30 days, then their account is made inactive.”

User Acceptance Testing

User Acceptance Testing (UAT) is a crucial phase of Data Mapping. It takes place after data migration or cleanup has been completed and involves testing whether data provided by the source system is usable, accurate, secure, accessible, compliant with regulations and meets user needs.

Data mapping includes a set of steps to transform data from one format to another. The transformation process may include data cleansing and transformation. The transformation process may also include data migration, data loading, integration and quality assurance (QA).

Data mapping is the process of mapping two or more sets of information to determine the relationship between datasets at a high level and how they differ. Data mapping helps identify missing or duplicate records and inconsistencies between different sets of information before they are integrated into a system.

Conclusion

The ultimate goal of data mapping is to provide users with an accurate, clean, and consistent view of data. In today’s world, where data is becoming increasingly complex and more diverse, it is critical we have a sound understanding of data mapping techniques so that we can use the data to make better business decisions that can make huge differences to not just the bottom line, but also affect how customers perceive a company’s products and the brand itself.