The world is ruled by data. Currently, all organizations are dealing with ever-increasing enterprise data as data sources are expanding rapidly. For allowing smooth migration of data and getting maximum business value out of it, enterprises must use robust data transformation strategies to extract insights and streamline decision-making. However, this data has different formats, schemas, and language, and this brings the need to integrate all data sources. Here the role of data mapper comes into play.
A data mapping tool helps in creating data element mappings between two different data models. It also ascertains meaningful relationships between separate data files or formats.
Organizations today are handling myriad business applications hosted on the cloud, on-premises, or hybrid environments, and it becomes imperative for them to use a powerful data mapping tool to allow communication amidst applications and drive decision-making. Being the first and integral step of data management, data mapping allows users map data hosted at different environments. However, not all data mapping tools are effective. Truth is, for data integration projects to be successful, companies must deploy the right tool that maps the source and target fields using a simple drag and drop function with ease and precision.
A powerful data mapping tool has a significant number of features from low-code functionality, preview functionality to AI-powered data mapping. Let’s take a closer look.
Low-Code Functionality: A data mapper must have low-code interface functionality to enable non-technical users create maps easily. Moreover, complex integration tasks can be carried out easily with minimal technical intervention. In addition, data mapper with a low-code functionality carries out mapping tasks of any level of complexity. In doing so, mapping is done on the dashboard using simple drag and drop option in a fast and secure manner.
Supports Structured/Unstructured Formats: A good data mapper must provide support to a multitude of structured formats such as XML, EDI, IDOC, Flat files, CSV, etc as well as unstructured ones such as emails, pdf, social media, IoT streams, and more. Users can map corresponding data fields (between same or different formats) by creating output in the desired schema.
Preview Functionality: Data mapping mechanism requires mapping of a number of fields that can be at times hundreds or even thousands. Preview functionality helps business users monitor the progress of mapping in such cases prior to testing mode. With this feature, users can make changes immediately in case some fields have been incorrectly mapped, reducing the chances of errors to a bare minimum. This saves time and enhances efficiency.
AI-Powered Mapping: AI-assisted data mapping functionality reduces the effort required to create mappings and increases accuracy. It makes the transformation process easier than ever by automating integration steps so that even non-technical business users can create data integrations with a few clicks. By reducing the manual effort and accelerating deployment timeframes, companies can easily generate a quicker return on investments and increase productivity.
As the world is getting closer to embrace connected devices and digital technologies in day-to-day life, overlooking the role of data mapping in connecting as well as integrating applications can prove costly. Companies need to have a proper data mapping mechanism in place to facilitate automation and support their digital transformation journey. Schedule a quick demo to find how Adeptia’s data mapper can cater to your business needs.