Data mapping is a crucial design step in data migration, data integration, and data transformations projects. Modern-day solutions use artificial intelligence to map data fields from a source format to a target format. As a consequence of that, business users can establish relationships between separate data models from disparate sources or systems. This makes an impact on business analysis, forecasting and business decision making. So, data mapping is not only important for data integration processes but also the growth of business.
Every company deals with massive amounts of data coming from myriad sources. The data may reside in different formats and so organizations find it immensely difficult to integrate it into a unified database for the data analysts to garner insights. Data mapping has a big role to play here. It supports business users map data faster, which can be integrated for further usage.
Data mapping in its simplest term is to map source data fields to their related target data fields. For example, the value of let’s say a source data field A goes into a target data field X. Data Mapping tools allow developers to code these conversion rules to achieve the expected target output.
Applications consist of underlying metadata that provides information on the individual data objects, attributes, fields and business or semantic rules on how this data is persisted in its data repository. For example, Salesforce.com has a data object called Accounts and its schema consists of fields, attributes, enumerations, data integrity and dependency rules with other data objects. Therefore if there is a need to add or update a new data record from another application into Accounts data object then there is a need to create a data map between the incoming data into the Salesforce.com Accounts format.
The complexity of data map varies from the type of hierarchical data structure that source or target schema represents to the complexity of conversion rules that the target application requires for successful data integration. Also the mapping can be between multiple sources and targets where the data from two or more sources need to be merged or joined prior to mapping the result to the target.
In this article I’m going to present Adeptia’s AI-powered data mapping capabilities which I think is unique in the market in terms of the breadth of features it supports out-of-the-box and the ease of implementing the mapping rules without having to write custom code. It uses machine learning for inferring data mapping predictions from existing library of tested data maps, reducing the effort and time to create intelligent data mappings. Its transformative features like improved strength, browser-based access, drag and drop mapping, superior built-in functions, and more have made this data mapping tool as the front-runner. You can see demo of Adeptia Connect to try these steps on our live software platform
So let’s first begin by discussing the basic feature strength which is that it is completely browser-based. All you need is a browser to invoke the mapper interface and it opens up on your machine. No need to install a thick client in your desktop to access this interface. Now the advantage for its browser-based access is also that you can access it from anywhere through your secure cloud or on-premise Adeptia login. And if you are part of a user group with sharing rights with rest of your team, you can collaborate with other users to contribute or assist in your data mapping activity. Speed of creating data maps is no longer restricted to a single developer, now with this collaborative platform your team of business users and developers can work together and create data maps quickly and speed up the time it takes to onboard data into your applications.
With its drag and drop mapping the mapper interface can be used by non-technical users. Simply click and drag a source field onto a target field and your data mapping is done. And if there is a need to apply additional rules on the map then use the built-in functions to transform the data as per your business rules. Built-in functions include math, string, conditional, code conversions and database or reference lookups. Users can also call external programs, database stored procedures and web services.
For example, here is a video on how to use an auto-map function in converting the source to a target data format.
Below is a shortlist of key features which I think is important in understanding the type of features you should be looking for when evaluating a data mapping tool:
With enterprise's data becoming more diverse and voluminous, the need for business to leverage data and transform it into valuable insights has become more important than ever. Prior to extracting value out of such diverse data, organizations need to unify and transform it into a format suitable for the operational and analytical processes. This relationship-building between various data models is accomplished through AI-enabled data mapping, which is an integral step of data management.
There are many additional features that we would like to show you in a live demo and also walk you through your use case and build out a map in a live session.