What is Data Mapping?
Data mapping is a crucial design step in data migration, data integration, and data transformation projects. Modern-day data mapping solutions leverage artificial intelligence (AI) to map data fields from a source format to a target format. Consequently, business users can establish relationships between separate data models from disparate sources or systems. This enables companies to facilitate improved business analysis, forecasting, and decision-making. So, data mapping is not only important for data integration processes but also for the growth of a business.
Why Is Data Mapping Important for Business?
Every company deals with massive amount of information coming from myriad data 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. The data mapping application has a big role to play here. It supports business users to 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 software tools enable 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 the 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.
What Are the Capabilities and Features of Data Mapping Tools?
In this article, I’m going to present Adeptia’s AI-powered data mapping capabilities which I think are 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 an 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 features, superior built-in functions, and more have made this data mapping tool the front-runner. You can see a demo of Adeptia Connect to try these data mapping 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 on your desktop to access this interface. Now the advantage of 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 the rest of your team, you can collaborate with other users to contribute or assist in your data mapping activity. The 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.
Below is a short list of key features which I think are important in understanding the type of features you should be looking for when evaluating a data mapping tool:
- Supports drag and drop mapping
- Classifies essential mapping suggestions into high, medium and low confidence
- Provides a holistic review and accept/reject options for data mapping suggestions
- Supports all data formats including Text, CSV, Fixed Length, Database tables, XML, EDI X12, JSON etc
- Works with complex, hierarchical data structures, not just flat formats
- Supports graphical functions for non-technical users
- Web UI interface
- Syntax and business rules validation
- Ability to show errors during validation
- Supports testing, debugging and run tests with sample data
- Preview transformation results inside a map
- Ability to work with large files > 1MB efficiently
- Supports multiple sources and targets in a single map
- Ability to run Java inside a map
- Ability to run SQL and stored procedures inside a map
- Ability to make database calls inside a map
- Encoding support for Unicode, EDIX12EDIFACT, UTF-8/16 or other
- Supports many data formats such as XML, Database, Cloud Apps, ERP, CRM, CSV, Excel or other
- Supports conditional and rules-based mapping
- Interface auto-generates a Mapping Document in PDF
- Ability to write custom XSL
- Supports Axis functions
- Supports value maps for cross-reference lookups
- Supports versioning with check-in and check-out
With enterprise data becoming more diverse and voluminous, the need for businesses 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 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.
Steps of Data Mapping
Data mapping is a crucial process in ensuring that data is accurately and consistently transferred from one system to another. The steps involved in data mapping include understanding the source and target systems, identifying the data elements that need to be mapped, determining the mapping rules and transformations, creating a mapping document or tool, and validating the mapped data.
Firstly, it is important to thoroughly understand the source and target systems, including their data structures and formats, to identify any potential mismatches or incompatibilities. Next, the specific data elements that need to be mapped are identified, considering the differences in naming conventions and data types. Mapping rules and transformations are then determined, specifying how the data will be converted or manipulated during the mapping process. A mapping document or tool is then created to document the data mapping rules and provide a clear reference for developers and analysts involved in the process.
Finally, the mapped data is validated to ensure its accuracy and integrity, and any discrepancies or errors are resolved before the data is transferred to the target system. Overall, the steps involved in data mapping are essential in ensuring a successful and reliable data transfer process.