What are your must-have features for EDI software?

It's not specified here, but from my perspective, both on-premise middleware and EDI service provider tools can be considered in this context. In any case, data validation is a beneficial feature that could be implemented as part of the EDI provider's service, middleware, or even as a standalone tool. I can identify three layers of validation: 1. Syntax control: checks if the message meets the requirements of the declared standard. 2. Customer-related control: ensures the message complies with the rules of a specific trading partner. 3. Mapping control: verifies if the data can be successfully mapped (though this could be considered part of Data Control).

When it comes to Data Validation, I have always believed there are two main aspects involved - First being ability of Middleware tool to support certian types of standard Data validations based on different EDI standards like x12 or TRADACOMS or EDIFACT. Second being ability of EDI developer to design maps and introduce different data validation gates so that Custom requirements of Data structure is always validated. So, This Data Validation hinges upon standard Data validations right out of Tool and Custom data validations designed by developer using Middle-ware Tool's functionalities.

Data validation means checking the accuracy and quality of source data before using, importing or otherwise processing data. Data validation is the practice of checking the integrity, accuracy and structure of data before it is used for a business operation

For Peppol BIS 3, a key XML format for EDI in the EU, there are two levels of validation to ensure data integrity: 1. Schema Validation. Validates the XML document structure against the defined schema to ensure proper format and organization. 2. Schematron Validation. Further checks the values within the EDI messages using Schematron, implemented with XSLT, to verify compliance with specific business rules and standards. These steps are vital for reducing errors and enhancing data quality in business communications.

When it comes to Data Mapping, I Always prefer that Regular standard Data mappings which are pretty much unchanged should be automated. This has recently been attempted with Crowd sourced Integration advisor functionality in SAP Integration Suite's Cloud Integration Middleware tool. That has Made Standard EDI format Data mapping very time and code efficient. MIGs are consumed to generate MAGs which later can be consumed in Integration Flows iFlows. When it comes to A2A mappings, It's always complex as there needs development from scratch to get mapping between application to application. Still I would love to make use of Industry standard AI and ML capabilities to conduct more effective data mapping in Middleware layer.

EDI data mapping is crucial for efficient exchange between diverse formats like ANSI X12, EDIFACT, XML, or JSON. While automation streamlines standard mappings, complex A2A mappings demand tailored solutions. Leveraging advanced mapping tools like Altova MapForce, Axway B.O.M., and Oxygen is key. Altova offers an intuitive drag-and-drop interface, Axway excels in enterprise-level integrations, and Oxygen specializes in XML transformations. These tools empower users to efficiently manage data transformations and ensure smooth interoperability.

Data mapping is crucial to the success of many data processes. One misstep in data mapping can ripple throughout your organization, leading to replicated errors, and ultimately, to inaccurate analysis.Data mapping bridges the differences between two systems, or data models, so that when data is moved from a source, it is accurate and usable at the destination.

Data mapping is crucial when dealing with a variety of XML and non-XML document types, such as Peppol BIS 3, UBL, Swefaktura, EHF, and EDIFACT. Converting data formats efficiently is essential to facilitate trading across different systems, countries, and standards. Challenges in this process include handling formats that may be specific to certain vendors, navigating the often sparse documentation available, and addressing parts of data standards that are not compatible with one another. These issues underscore the need for EDI software equipped with robust and adaptable data mapping tools capable of managing complex and dynamic data transformations and integrations across diverse systems.

Data security is the practice of protecting digital information from unauthorized access, corruption or theft throughout its entire lifecycle.It is the practice of safeguarding digital information from unauthorized access, accidental loss, disclosure and modification, manipulation or corruption throughout its entire lifecycle, from creation to destruction.

Given that the majority of EDI formats are XML-based and rely on XSLT for validation, it's crucial to ensure your EDI software is safeguarded against XML vulnerabilities. These vulnerabilities can include XML External Entity (XXE) attacks, XML bombs, DTD retrieval, and XPath injection, XSL Transformation among others. Each of these potential security risks can lead to unauthorized data access, data loss, or service disruptions, compromising both your business operations and compliance with regulations like HIPAA, GDPR, or PCI DSS.

Data visibility is the ability to see, monitor, and manage data across your entire organization. This includes understanding where your data is stored, who has access to it, and how it's used. Whether you realize it or not, data visibility is critical to your success.It is the difference between being able to see the journey your customer or potential customer goes on, and to understand the hazards and roadblocks that they encounter. It provides you the opportunity to analyze the full process from initial contact to fulfillment.

It is capability of a system to handle a growing amount of work, or its potential to perform more total work in the same elapsed time when processing power is expanded to accommodate growth. A system is said to be scalable if it can increase its workload and throughput when additional resources are added.

Data support can simply be explained as providing the support from the collection, recording and recovery of your data. A data supporter studies source documents electronically, validate the correctness of the information, make reports and manage data storage and retrieval systems.

From my experience, a very beneficial option is to edit and resend messages. For instance, if a customer sends a message with a simple error, such as an unsupported qualifier or a reference number longer than allowed, and it fails in your middleware or ERP system, I would duplicate, edit, and resend the message to create an order for future processing as soon as possible. This approach gives you time, without pressure, to analyze the case and adjust the systems if needed. Also, sometimes a customer needs something that can't be provided by the ERP, but is mandatory for some exceptional case. Manually editing the message might help or gain time for a holistic solution.