Data Mapping
Why You Should Be Using This Process to Streamline Your Data
Data mapping is the process of linking a data field from one source to a corresponding data field in another source. This practice reduces the potential for errors, standardises data, and enhances data comprehension. Data mapping offers a visual representation of data movement and transformation, often serving as the initial step in comprehensive data integration. Data integration consolidates data from multiple sources into a single destination in real-time. Data visualisation through data mapping merges data from multiple sources into data models, simplifying the amalgamation of dispersed data sources.
How Does Data Mapping Work?
The data mapping process commences with a comprehensive understanding of the data associated with a specific subject. A data mapping instruction set identifies data sources, targets, and their relationships.
Why Use Data Mapping?
Different data systems store information uniquely, making data mapping pivotal for standardising data across an enterprise. This standardisation is vital for complex data management processes, including data migration, data integration, and master data management, ensuring the production of high-quality data insights.
Three Data Mapping Techniques
Automated Data Mapping
Automated data mapping involves the use of specialised software to match new data with your existing data structure. These tools often employ machine learning to continually monitor and enhance data models. Automating data mapping offers several advantages, including:
- Seamless extraction of data from numerous sources.
- Empowering non-technical staff to perform complex data processes through a user-friendly interface.
- Visual representation of data flows, enhancing data comprehension.
- Prompt notifications of issues as they arise.
- Streamlined troubleshooting for targeted repairs.
Semi-Automated Data Mapping
Semi-automated data mapping, often referred to as "schema mapping," is a hybrid approach that combines elements of both automated and manual data mapping. Developers work with software that establishes connections between different data sources and their targets. Once the mapping process is set up, a team member manually verifies the system and makes necessary adjustments. This approach is particularly effective for handling smaller data volumes and basic integrations, migrations, or transformations.
Manual Data Mapping
Manual data mapping involves a developer coding rules to transfer or copy data from one source field to another. While manual data mapping is still utilised, it has become less practical due to the vast amount of data available. It is best suited for one-time processes, such as data warehousing, when dealing with relatively small databases.