How is data mapping done

Skip to main content. Fulltext search. What is Data Mapping? Data Mapping Important for Business? Adeptia Connect. Adeptia News. API Integration.

Application Integration. B2B Integration. Customer Data Onboarding. Data Integration. EDI Software. Enterprise Service Bus. Hybrid Integration Platform. Self-Service Integration. Carlos Escabalzeta. Gaurav Sachdev. Mange Ram Tyagi. Steve Medeiros. Data mapping empowers your business intelligence platform so it can understand everything and deliver the best possible insights for your company.

It does this through the most basic of data functions: taking the information from a data set or data sets and "mapping" it for a target output. Integrate Your Data Today! Try Xplenty free for 7 days. No credit card required. Get Started. Let's go through a real-world example. To understand data mapping, imagine three databases with data on popular movies and actors.

Each organizes the information into columns and fields, and each has a different organizational strategy. Take a look at the three databases here:. Merging the three databases above into a data warehouse lets you query them or search for information in them as if it were a single database. That could be valuable for a business intelligence system that needs a bird's eye view of all the data from a company.

Bringing the databases together requires a data map to clarify where the information intersects. Also, you need to define which database's data to use in cases of duplicate data as well as how to treat new information. Below is an illustration of a basic data map for the movie and actor databases. The connecting lines show how we mapped the data sources to the target schema:.

In summary, data mapping creates instructions that merge the information from one or multiple data sets into a single schema table configuration that you can query and derive insights from. The above example is a simple one, but data mapping becomes exceedingly more complicated depending on the following factors:.

Ultimately, the goal of data mapping is to normalize diverse and incongruent data sets, so BI systems can seamlessly access and analyze the information. When done correctly, this can yield game-changing insights. Data mapping for a data warehouse begins with an analysis of the source information and the schemas that apply to it.

For example, where do the databases intersect with the same information? The process also begins with the definition of rules to govern the mapping and integration process.

For example, if duplicate data gets found in two different databases, which data should the system prefer? Data mapping is the process of extracting data fields from one or multiple source files and matching them to their related target fields in the destination.

Data mapping also helps consolidate data by extracting, transforming, and loading it to a destination system. The initial step of any data process, including ETL, is data mapping.

Businesses can use the mapped data for producing relevant insights to improve business efficiency. During the data mapping process, the source data is directed to the targeted database.

The target database can be a relational database or a CSV document — depending on the use case. In most instances, companies use a data mapping template to match fields from one database system to the other.

Here is a data mapping template example to clarify how the mapping process works from an excel source. Source to target mapping in Astera Centerprise using a graphical data mapping UI.

Source-to-target mapping integration tasks vary in complexity. The level of intricacy depends on the data hierarchy and the disparity between the data structure of source and target. Whether on-premise or cloud, every business application uses metadata to explain the data fields and attributes that constitute the data and semantic rules.

These rules govern how data is stored within that application or repository. The goal is to ensure a seamless transfer process from source to destination without any data loss. The application also has a defined schema along with attributes, enumerations, and mapping rules. Therefore, if a new record is to be added to the schema of a data object, a data map will need to be created from the source fields to the Microsoft Dynamics CRM account. Data mapping is used in a range of use cases and industries to streamline data processes.

For example, in the healthcare industry, source-to-target mapping helps achieve interoperability for EHR electronic health record by matching the data between a source and target. It also helps healthcare professionals share critical patient information and combine healthcare data from the various databases, data sources, and systems, such as EHR and EMR.

The mapped data is further used for other critical processes, such as data migration and data integration. Mapping can have a varying degree of complexity, depending on the number, schema, primary keys, and foreign keys of the data sources. For instance, in the following example of database mapping, data from three different database tables, Lead, Lead History, and Lead Status is joined and data mapping in SQL Server is carried out to an Excel destination.

Database mapping is used to accomplish a range of data integration and transformation tasks, depending on the data management needs of an enterprise and the capabilities of the data conversion mapping software. Common known use cases of mapping business data include database schema mapping for pre-integration, data cleansing from disparate data stores, and data conversion from legacy systems.