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Simplifying Integration with AI

By Tony Curcio posted Mon August 02, 2021 01:31 PM

  

Many of the enterprises we speak with are focused on decentralization of their IT functions – including integration.  For some of these companies, that has to do with budget shifts from a central IT organization out to a line of business.  In other cases, this may be a results of executing a global strategy that requires specific IT processes to remain in a country where they operate.   Whatever the case, the intent is to improve business outcomes through faster time to value, lower costs and increased innovation.

 

To achieve this decentralization, business leaders are seeking smarter alternative technologies that can help them scale across their enterprise.   They must avoid large upfront learning costs in training and mentoring before their teams can be successful.  At the same time, these toolks must still be powerful enough to tackle the complex business challenges that everyone faces.

 

So, what is the secret ingredient for such tools which can cater to both experts as well as less skilled line of business users?   There's no guessing here - it’s Artificial Intelligence.  AI is the fuel powering smarter integration, helping organizations reclaim upward of 50% of their project time while reducing the expertise barrier and achieving higher-quality outcomes.

 

IBM has introduced the Cloud Pak for Integration (CP4I - a complete integration offering which supports multiple styles of integration within a single platform.  Along with a consistent user interface, a shared asset repository and a unified governance framework, it also provides state of the art, AI-powered tools to acceleratethe speed of development, shorten time to value, and improve overall user experience.  

 

Let’s explore 4 of the key areas where AI-powered tooling will help.


1. AI-powered Data Mapping

When building integrations, data mapping is the process by which a user specifies how data elements from a source application correspond to elements in a target application. Data mapping has long been a point of frustration for developers since mapping fields between different endpoints is complex and time-consuming, slowing the speed at which businesses can respond to changes and remain competitive.

 

Mapping Assist uses a pre-trained AI algorithm to securely provide users with intelligent, customized data mapping suggestions while building integration flows.  It lets you automatically map high confidence suggestions with a single click and provides a set of curated mapping suggestions in case the user wants to review and map them manually.


 

 

Highlights of Mapping Assist –

  • Ready to use - AI/ML algorithm is pre-trained to start working as soon as you do, you don’t have to train it to get going
  • Comprehensive - Mapping Assist considers all previous applications in a flow when offering mapping suggestions not just the immediately previous node. It also provides suggestion for nested complex objects, arrays, and custom objects along with flat objects.
  • Closed-loop AI - It learns from your flow's mappings, retrains AI model, and offers customized suggestions relevant to your mapping patterns. So, the next time you are trying to build a similar mapping for a different application, it would offer these tailored suggestions based on your past usage pattern.
  • Private and secure - What AI learns working on your technologies is your intellectual property and is securely stored. Even in the cloud, this stays private to your org – unless you specifically want to share it

 

2. AI-powered Data Transformation

After data mapping, data transformation is the next immediate challenge users face while building integration flows. Data from source systems often needs to be restructured (or transformed) into another format which target systems can understand and process.  It’s a time-consuming exercise which requires moderate technical skills for building the transformation expression.

 

Take for example the illustration below where the user needs to reformat the mobile phone.  Rather than write custom logic to do that, he can provide sample values that illustrate his intent - what he wants the output to look like.  At that point, the AI automatically inspects the example and generates the transformation logic.

Generate transformation panel with JSONata expression

 

In addition, transformation generator can also auto-generate the logic for how data values (or code sets) are mapped between heterogeneous applications – as shown in the following example.

Generate transformation panel with JSONata expression

 

Highlights of Transformation Generator-

  1. Optimized expressions - It generates the optimized expression which an expert would have generated manually with years of expertise.
  2. Variety of use cases – It addresses a variety of common scenarios like transformation of date formats, telephone format, address format, string case changes, mapping sets
  3. Accelerates learning – The user doesn’t need to know any expression language to get started, but as they work, the AI illustrates the result. Not only does this provide transparency for what is being done on their behalf, but it also presents a learning opportunity in context.

 
3. AI-powered Flow Generation

Over the past two years, we’ve made significant progress in teaching computers to understand what a user is asking for in natural language.  It’s now becoming increasingly common to be able to interact with a system from a hotel, insurer, bank, or government through a chat interface.  We can now apply the same technologies to help users build their integrations.

 

Natural Language Integration enables users to describe what they want to achieve.  In response, the system leverages semantic understanding to interpret the user intent and then provides suggested flows that can be instantiated from that request. 

 

In the below example, the user describes a scenario in English and Watson powered AI recommends flow templates closely matching the described scenario.  It automatically reconciles the notion of a “lead” and the systems where leads can be worked with.   It also gives a matching score showing the AI confidence in the recommended templates and the reason for the recommendation.  As we continue to build on this AI capability, we anticipate the ability to auto-generate that flow even when no similar templates have been pre-built.

 

 

 

4. AI-powered API Testing

With many new integration solutions using APIs to address business needs, it is critical that these APIs are thoroughly tested to reduce the chance for users to encounter errors.   Unfortunately, many organizations do not feel that their existing API and Integration landscape has a sufficient level of testing.  The API test generation capability can help provide better quality solutions, with lower impact on development skills and time.  It analyzes real production data about how the APIs are being used to determine whether additional test cases are required.

 

The AI ingests OpenTracing data from the API traffic to find unique scenarios in the data.  This trace data includes not only the API request and response details, but also the details from the underlying integration or application components that also record trace information.  These traces form unique “fingerprints” that define a test scenario and are then compared against existing test cases.  Where scenarios are not already covered as part of the test suite, the API test generation capability then uses automation, AI, and machine learning to generate potentially hundreds of new tests that reduce the potential for API users to encounter errors.   These tests can also be customized by the user through a no code interface where needed.

 

 

Conclusion

Any organization that is looking to scale out their integration platform beyond the walls of their center of excellence should consider the benefits of AI-powered tooling.  Such capabilities reduce expertise requirements, speed time to value and improve outcomes.   IBM Cloud Pak for Integration platform provides state of the art, AI-powered low-code, no-code automation tools which can drastically simplify the end to end integration lifecycle.

 

Please email me directly at tcurcio@us.ibm.com if you’d like to discuss.  Or, consider signing up for a free trialnow.

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