In the integration phase, more repetitive and time-consuming tasks are being carried out for requirements, development, support, and testing phases. Business intelligence powered productivity tools with historical data and exposing it as services make perfect sense to help the project team to efficiently integrate applications/businesses and quickly on board the trading partners.
As more and more integrations are being built and deployed, the mapping specifications that are used to develop the maps are left in lurch and for all the new interfaces between applications and partners, the specifications are recreated again and again. Immaterial of the domain (Logistics, Manufacturing, MRO, etc) that the clients are operating on, the logic behind mapping the business data exudes similarity across industries.
Having a central repository of mapping specifications allows to build intelligence around the mapping data and further helps in recommending the closest match of mapping specifications for any new interface. This gives a jump start of 60% and the remaining map data can be addressed by the business team. Intelligence built around all possible data structures, industry standards (for EDI) and applications help in narrowing down the suggestions with the right specification. Feeding more real-time data to the intelligence platform allows an effective, cleaned, versioned and organically grown mapping data and helps in the accuracy and success rate over the period of time.
With less/no code development platform preferred by the integration community, having an intelligence tool to generate industry standard or tool specific transformation code like XSLT, eSQL or Data Weave, from the mapping specifications, allows users to reap the maximum benefits and reduces the development time by 60% or 70%.
Most of the iPaaS platforms are providing Crowd-sourced intelligence by harnessing the collective intelligence of the global integration community and help in simplifying the data mapping process.
Mappings that are recommended are based on the ranking algorithms that weigh suggestions based on the similarity and relationship of the data fields.
Integrating applications within an enterprise or across boundaries involve dealing with multiple endpoints and that in turn leads to possible multiple failure scenarios. Having a single integration platform within an enterprise allows to monitor and manage the failures centrally and more efficiently as compared to managing the failure scenarios in point-to-point integrations.
Irrespective, of the integration platform/method, used, most of the effort is spent in troubleshooting the repeated scenarios and narrowing down the root cause.
Building intelligence around the historical data of problems and solutions that came across till now and allowing machines to self-train using ML-based algorithms are driving businesses.
Categorizing and arranging knowledge for it to be searchable and reusable and recommending solutions is the next right step.
With more interfaces being built and deployed, there is an increasing demand to test those interfaces with multiple business use cases. End-to-end testing starting from generating all the test files for each interface covering all the business use cases is a labour-intensive process. Again, test cases have to be validated based on the actual output and compared with the expected ones.
Automating this process of dynamically generating all the test files based on the logic in the mapping specifications and validating the output is very crucial in reducing the interface testing timeline.
With more test automation tools available in the market for web/windows applications, having an intelligence powered tool for testing the integration interfaces is the need of the hour.
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