What is Data Mapping? Definition, Methods, and Tools

Data Mapping

Data mapping is critical to the success and efficiency of many data processes. A single mistake in data mapping could have a ripple effect throughout your organization, resulting in repeated errors and eventually, inaccurate analysis.

Nearly all enterprises will move data between systems at one point or another. And different systems store similar data in different ways. So to move and consolidate data to be used in analysis or other tasks to ensure that data arrives at its destination correctly, a roadmap is necessary.

For processes like data integration, data migration, data warehouse automation data synchronization, automated data extraction, or any data management project, the quality of data mapping will determine how the data is to be analyzed and interpreted for insights.

Data mapping is essential for modern enterprises

Data mapping refers to the process of matching data from one database to another. This is the first step in data migration, data integration, and other data management tasks.

Before data can be analyzed for business insight, it needs to be homogenized so that it is easily accessible to decision-makers. Many data sources are available today, with each source defining similar data points in a different way. The source system might show Illinois as “Illinois” but the destination could store it as “IL.”

Data mapping is a way to bridge the differences between two systems or data models. It allows data to be transferred from one source to another and is then accurate and usable at its destination.

Data mapping has been an integral part of business for a long time. However, as data sources and data increase, it has become more difficult to do data mapping. This has led to the need for automated tools that can be used for large data sets.

Also read: What is Data Lineage, Best Practices and Techniques

Data mapping is key to data management

Data mapping is an integral part of many data management processes. Data can become corrupted when it travels to its destination if it is not properly mapped. Data mapping quality is crucial to getting the best out of your data when it comes to data migrations, integrations and transformations as well as populating a data warehouse.

Data migration

Data Migration refers to the act of moving data one-time from one system to another. This is generally data that doesn’t change over time. The destination becomes the new source for migrated data after the migration. The original source is now retired. Data mapping is a tool that supports migration by mapping source fields and destination fields.

Data integration

Data Integration is a continuous process of moving data from one system to another. Integration can be scheduled (e.g., monthly or quarterly) or triggered by an event. Both the source and destination store and maintains data. Data maps for integrations, like data migration, match source, and destination fields.

Data transformation

Data Transformation is the act of converting data from one format to another. This includes cleansing data, changing data types, deleting duplicates, aggregating and enriching data, as well as other transformations. To match the destination format, one can transform “Illinois”, for example, to “IL”. These formulae are part of the data map. The data map uses these transformation formulas when data is moved to ensure that the correct format of the data for analysis is obtained.

Data warehouse

If you want to combine data from different sources for analysis or other tasks, the data is usually stored in a data warehouse. The warehouse is where you can run queries, reports, and analyses. The warehouse already has data that has been migrated, integrated, and transformed. Data mapping makes sure that data arrives at its destination exactly as it was intended.

What are the steps involved in data mapping?

  1. Define — Define the data to move, including the fields in each table and the format after it has been moved. Data integrations require that the frequency of data transfers be defined.
  2. Map the Data — Match destination fields to source fields.
  3. Transformation — A field that requires transformation, the transformation formula or rule is coded.
  4. Test — Use a test system with sample data from the source to run the transfer and see how it works. Make adjustments if necessary.
  5. Deploy — Once the data transformation has been completed as expected, you can schedule an integration or migration go-live event.
  6. Maintain and update — The data map is an ongoing entity that needs to be updated and changed as new sources of data are added or changes at the destination.

How the right data mapping tool can help

Enterprises can get more from their data with advanced cloud-based data mapping tools and transformation tools without spending too much. This example of data mapping shows how data fields are mapped from a source to a destination.

In the past, data mappings were done on paper by organizations. This was adequate at the time. The landscape has changed. Paper-based systems are unable to keep up with the increasing amount of data, maps, and constant changes. They are not transparent and can’t keep track of the changes in the data models. Mapping by hand means that you must code transformations manually, which can be time-consuming and prone to error.

Also read: Top 10 Data Modeling Tools You Should Know

Transparency for architects and analysts

Data quality is vital for data analysts and architects. They need to have a real-time view of the source and destination data. Data mapping tools give analysts and architects a common view of the data structures being mapped, allowing them to see their data flow and transformations.

Optimization of complex formats

Data compatibility can be a problem when there is so much data coming from different sources. Good data mapping tools simplify the conversion process by providing integrated tools that allow for the precise transformation of complex formats. This saves time and minimizes the chance of human error.

Fewer challenges for changing data models

Data maps aren’t a one-and-done deal. Maps need to be maintained due to changes in reporting requirements and data standards. Stakeholders no longer have to worry about losing documents related to changes with a cloud-based data map tool. Data mapping tools that are reliable allow users to monitor the impact of maps being updated and track their progress. Data mapping tools allow you to reuse maps so that you don’t have a new map every time.

What should you look for in a data-mapping tool?

Cloud-based software for data mapping is fast, flexible, and easily scalable. They can also handle complex mapping tasks without exceeding your budget. Although the functionality and features of a data-mapping tool will vary depending on the needs of an organization, there are some essential features that you should look out for.

Wide format support

Many tools can handle basic file types like Excel, delimited text files and XML, JSON, EBCDIC, and others. You should look for a tool that can handle common formats such as SQL Server, Sybase, Oracle, DB2, or other formats. Good mapping tools can also be used for enterprise management software like SAP, SAS, Marketo or Microsoft CRM, or SugarCRM, or data from cloud services such as Salesforce or Database.com.

Automated and intuitive

A cloud-based, intuitive tool that automates repetitive tasks is created to reduce time, tedium, and human error. Drag and drop functionality is important so users can quickly match fields and apply built-in transformations so no coding is required.

Workflow and scheduling

To enhance automation capabilities needed a tool that can be used to create a complete mapping workflow The ability to schedule mapping jobs that are triggered by an event or the calendar.

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