Data is the new oil. These days every organization, small or big, is being inundated with data from various sources. The ultimate objective remains to combine all these data sources into one single source that can further be analyzed by users, data scientists, and analysts into meaningful information. In digital times, an organization must have top-notch control over its data to stay relevant in the market. Data integration plays a significant part in routing data from multiple sources (applications, devices, databases, etc.) into a single source/platform. The key is to collect and deliver the data from various sources to the end user to drive collaboration and consequently efficiency within an enterprise.

There are multiple types of integration such as application integration, B2B integration, IoT integration, EDI Integration, etc. Data mapping plays a major part in all these forms of integration. It not only enables the mapping of two distinct elements but also governs the rule as to how the data would be mapped with each other. In a way, data mapping requires an understanding of the semantics of data schemas to ascertain relationships between source and destination fields. Thus, data mapping is a crucial foundation for the smooth integration between two different applications hosted on different environments (cloud, on-premise, or hybrid). There are multiple sophisticated data mapping tools available in the market, such as DX Mapper, which provide an easy-to-use graphical interface to do data mapping with a mere drag and drop functionality as compared to traditional ETL (Extract -Transfer-Load) methods that extracts data from the sources and load them into a data warehouse for the purpose of data integration. So yes, all integration projects need a good amount of data mapping and, in this post, we will be highlighting a few points as to how data mapping forms a key part in data integration pursuits of an organization.

Helps in Data Transformation

As stated earlier, data mapping is the first step in a range of data integration tasks, one of them being data transformation between the source and destination. A data mapping tool or data mapper connects the distinct applications and governs the way the data from source application will look like when it is mapped to the destination application. It also supports the application of multiple data manipulation functions that are applied to data when it is transformed from source to destination. Along with data, a data mapper should handle multiple structured and unstructured files and formats to map the corresponding fields, creating the output in the desired schemas. Thus, it should support complex data integration tasks.

Integration Is Faster

An organization needs a mammoth number of man-hours and IT expertise to optimize the data operations on its systems. An efficient data mapping tool can solve this challenge by providing a simple interface and functionality for data mapping that offers speed, flexibility, and scalability so that even a user with minimal technical expertise, like business analysts, can do the mapping quickly. This not only saves time but also reduces errors, as it is possible to get a real-time preview of all the mapping activities.

Data Integrity

While dealing with integration between various applications, it becomes imperative to produce true and accurate data after the completion of the integration process. Here, data quality is of paramount importance, as it ensures there are no mismatches in data and all the fields have the correct information in the specified format. A data mapping tool makes sure that there are no gaps in mapping and the destination data is getting populated in the right format/schema. It ensures output is free of errors, inconsistencies, and any kind of duplication, thus preserving the integrity of data integration.

Collaboration

One of the primary tasks of an integration process is to centralize the data at one single platform so that any user can access or use it as per their needs. The presence of centralized data makes it feasible to retrieve and analyze data in real-time. A data mapper helps in centralizing the data as it collates the information from one or multiple sources and maps it into the destination in the desired format. Hence, the integrated data is readily accessible in the desired format, which promotes collaboration and sharing between multiple teams and leads to innovation and knowledge sharing.

Scalability

As business requirements continue to evolve, data volume and complexity will grow too. New technologies will come into action and an integrated solution will be expected to connect all these. A data mapping tool comes in handy when it comes to increasing databases, sources, and types, as it can map anything and everything and eventually helps in extracting the most relevant insights from the data. In other words, data mapping makes it easy for an organization to scale up with the help of an integration solution in the growing influx of data and data sources.

Conclusion

It is safer to say data mapping lays out the foundation of data integration. The high-performance mapping operations pave the path to carry out the complex integration processes and give the flexibility to have the live design-time previews of results. As more and more organizations are opting for specialized integration solutions, data mapping tools would continue to get better and faster to deliver on the promise of an integrated world.