Some Essential Features of Data Mapping
Modern data mapping tools use an artificial intelligence and machine learning-powered approach to help companies deliver insights and drive decision-making.
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The world is being ruled by data. In the current disruptive era, enterprises around the world are dealing with ever-increasing, highly complex, bidirectional data. To enable the smooth migration of data between myriad data sources and deliver value, organizations must employ effective data transformation strategies for better insight delivery and ultimately decision-making. This is easier said than done, however.
Data has multiple formats, languages, and schemas, and analyzing this highly complex data to extract insights and make decisions is challenging. For this, organizations need to integrate all the data sources – and thus all the data. Data mapping has a huge role to play here.
A data mapping tool enables business users to map data fields from a source format to a target format.
It not only enables users to map complex, bi-directional data but also ascertains meaningful relationships between separate data files/formats.
Organizations are handling different business applications hosted on a cloud, on-premises, or hybrid environment and it becomes essential for them to rely on a data mapping tool. This will help users streamline communication across myriad applications, and thus providing data in a unified format for businesses to take some actionable steps.
With a robust data mapping mechanism in place, one can map data hosted across business ecosystems. To drive data integration projects successfully, it is important to use the right data mapping solution that easily maps source and target fields with the support of a drag and drop feature. But the question is, how will you identify them?
Here are some common features that can help users map data faster and use the insights to swiftly make decisions.
Essential Data Mapping Features
- Low-Code: Modern data mapping tools have low-code interface features that help non-technical business users map data faster without difficulty. It enables users to execute complex integration tasks with minimal technical or IT intervention. Additionally, it enables users to map data faster as the need for executing complex EDI data mapping and custom coding gets eliminated. Intuitive screens and dashboards can allow users to create intelligent data mappings at the speed of business.
- Artificial Intelligence and Machine Learning -Powered Mapping: AI-enabled data mapping feature allows users to map different types of data with ease and precision. Users can rely on machine learning algorithms to predict and suggest data mapping rules automatically. Since decisions are being made by using the previously garnered information and implementing a certain set of machine learning algorithms, the possibility of errors like duplicities, missing values, etc. greatly reduces. So, not only AI can help users map different sources accurately but also maintain data integrity to transform decision-making and change the way they do business.
By using data mapping predictions, business users can create intelligent predictions - without putting in a lot of effort and time. As a result, users can easily integrate data - securely and easily. By reducing the manual effort and increasing deployment timeframes, organizations can easily increase productivity and generate a quicker return on investments.
- Complete Data Format Support: Any good data mapping tool provides support to a plethora of data formats including XML, EDI, IDOC, Flat files, CSV, emails, IoT streams, etc. Business users can map different data fields by creating output in the desired schema without the need for IT to intervene. Meanwhile IT teams can focus on more important tasks.
- Preview Functionality: Data mapping solutions may have to handle a multitude of data fields at one time. The preview feature helps users focus on mapping. Users can take the help of this functionality to make changes immediately in case some fields have been incorrectly mapped, and this reduces the chances of errors to a bare minimum. Consequently, mapping becomes easy and accurate.
As the world is getting closer to embracing connected devices and digital technologies in everyday life, it can prove counterproductive to overlook the role of data mapping in connecting as well as integrating customer data and applications correctly. Companies need access to a modern data mapping tool that helps them automate processes and ultimately support their digital transformation initiatives. By helping users to create intelligence data mappings, users can improve their ease of doing business and comprehend customer needs to deliver value faster.
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