When gathering data from different sources, there is the challenge of establishing relationships between disparate datasets, creating a comprehensive and coherent story from which insights can be gathered and decisions can be made. Thus there is a need to connect these disparate datasets together. Such is the job that falls under the function of data mapping.
The function of data mapping is to connect data fields coming from different sources together, providing a visual representation of data movement and transformation. It is often the first step in the process of executing end-to-end data integration which ultimately brings together data from one or more sources into a single destination in real time.
Components of data mapping
Data mapping is a complex process in itself. Thus, there are several elements that need to be in place to ensure its successful implementation. These elements are identified as follows:
Data Sources – These are applications or services where the data will be moved.
Data Targets – They can be any application, process, or service that is acting as a destination for the data.
Data Transformations – There are different transformations that can be applied to data mapping, with multiple transformations that can be applied to a single data mapping task. Among them are:
Joiner transformation combines data from different sources.
Filter transformation refines your data per your query. Then it pushes the selected information to the target.
Lookup transformation finds or looks for certain values 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.
Mapplets - They combine several transformation rules brought together so that they can be reused.
Data Mapping Parameters and Variables - These are constant value sets for transformation or mapping and can be changed manually or automatically.
User-Defined Functions – Data quality rules can be applied by the user to the mappings with these functions.
Why data mapping matters
The value of data mapping cannot be overstated enough. With data mapping, users are able to visualize and understand the data better as to how they relate to one another, leaving little to no room for errors. It also helps set the standards that will help the organization define its relationship with data and how to effectively utilize it. For business analytics, it provides a holistic view and context for the data. With such a granular-level understanding of the data readily available, users are able to gain deeper insights that can enhance the organization’s decision-making capabilities and provide users with a more competitive edge.
Data mapping is an essential part of many data management processes. If not properly mapped, data may become corrupted as it moves to its destination. Quality in data mapping is key in getting the most out of your data in data migrations, integrations, transformations, and in populating a data warehouse. It also supports data governance and makes it easier to apply use consent and other rights.
Finally, data mapping provides the ability to link all data about an individual’s attributes towards building a single source of truth, a critical element of any data privacy framework. Given today’s changing privacy regulations, automated, reliable data mapping addresses crucial data access and compliance requirements.