September 8, 2021 By Matthew Kosinski 4 min read

You may have heard people talk about closed-loop integration. In fact, IBM has recently said that every integration should be a closed-loop integration. But what does that mean?

Closed-loop integration is a method of creating and managing application programming interfaces (APIs) in which a company’s real-time and historical operational data is used to improve future integrations and refine existing ones.

Closed-loop integration generally requires an integration platform equipped with artificial intelligence (AI) and machine learning capabilities. The AI gathers data from previous and current application integrations, analyzes that data and uses the results of the analysis to make informed recommendations for creating new integrations, optimizing existing integrations, testing APIs for maximum security and more. In this way, closed-loop integration grants enterprise teams increased operational visibility into the whole ecosystem of application integrations. As a result, teams can get integrations up and running in less time, and they can consistently adjust integrations to ensure continued optimal performance.

Closed-loop integration can be understood as a translation of the principles of closed-loop performance management to the realm of API management. In closed-loop performance management, organizational leaders continuously monitor an enterprise’s key performance indicators. The insights gleaned from this monitoring are used to update organizational plans and goals in real-time. Rather than making a plan, executing the plan and then assessing the outcomes, closed-loop performance management creates an ongoing, self-sustaining cycle of continuous performance improvement.

Closed-loop integration creates a similar self-sustaining cycle of continuous performance improvement for software integrations. Rather than creating integrations, putting them into action and revisiting them at a later date, teams can constantly feed operational data into AI and machine learning models. AI then uses this data to streamline new and existing integrations. Additionally, teams no longer must make each integration from scratch — the AI can draw upon data from previous integrations to guide the creation of new APIs that are efficient and secure.

How closed-loop integration works in practice

Integration is at the core of enterprise automation efforts. Today, organizations rely on a range of disparate applications — both within and outside of the enterprise — to power their operations. Effectively automating workflows and user experiences within these vast ecosystems depends on getting all of these applications to seamlessly share data with one another.

For example, a simple lead processing workflow might require integrations between a customer relationship management (CRM) system, a marketing automation platform and multiple outreach channels like Slack and email clients. To facilitate the flow of lead data between these systems, a team must construct a series of APIs to ensure each application can talk with the others. If the team establishes and manages these APIs within an integration platform with closed-loop integration capabilities, the embedded AI could assist them in some of the following ways:

  • Workflow suggestions: When a new API is needed, the integration platform could suggest previous workflows that could be reused to establish the integration. For example, the same workflow used to connect the CRM and Slack might be used to connect the marketing automation platform and Slack. That way, the team doesn’t have to create a brand-new workflow for each integration; they only need to adjust the existing workflow to fit the new integration context.
  • Data mapping: Two APIs may not use the same categorizations to represent the same data. For example, one API might refer to a customer’s “zip code,” while another might refer to a customer’s “postal code” — two different ways to refer to the same data point. Before two APIs can talk to each other, data fields and representations from one API must be mapped to the fields and representations of the other. In effect, the language of one API must be translated to the language of the other. Closed-loop integration can facilitate this translation by making connections between data fields based on previous data maps. As the AI encounters a variety of mappings, it can apply the concepts it learned from earlier integrations to guide new ones.
  • API testing: Each new API needs to be tested to ensure it functions effectively and securely. Creating a comprehensive battery of tests for every API can be incredibly time-consuming but closed-loop integration can automate this process. Using data from previous tests and the OpenAPI specification, AI can recommend a suite of tests to assess the new API’s functionality. AI can even detect gaps in existing test suites and suggest new tests to close those gaps.

The benefits of closed-loop integration

When enterprise digital transformations fail, it is often because the integrations between the applications powering these transformations are inefficient, cumbersome and unsecured. The closed-loop approach supports more effective integrations by using a company’s own operational data to continuously refine APIs and workflows. This enables the following:

  • Faster automation and integration: Closed-loop integration arms enterprise teams with informed guidance on creating and maintaining APIs that meet their organizations’ particular needs. APIs don’t need to be made from scratch every time, and the integration platform tracks API performance for continuous improvement. As a result, it requires less time and effort to get functioning integrations up and running, and automation efforts can get up to speed more quickly.
  • Empowered teams able to build their own integrations: AI can guide less technically proficient team members through the creation and operation of effective, secure integrations. Agile teams no longer need to wait for a centralized IT authority to create every integration; they can deploy their own APIs as needed, doing away with the bottlenecks of a monolithic enterprise architecture.
  • Operational visibility: The data collected in closed-loop integration offers teams more insight into how their APIs work and how they can be improved. Teams can identify inefficiencies in real-time, predict problems before they happen and use company-specific operational data to inform their responses.
  • Safer APIs: Closed-loop integration facilitates more secure APIs, as the AI can use data from previous tests to ensure that every aspect of the integration is tested for functionality and security.

Closed-loop integration and IBM

As noted above, closed-loop integration is one of the core tenets of IBM’s cloud integration solutions. To execute successful digital transformations, enterprises need the ability to create efficient, reliable integrations between applications. Toward that end, IBM cloud integration solutions deliver integrations that are:

  • Automated, using AI, repeatable formats and low-code tools to facilitate faster integrations
  • Closed-loop, using real-world operational data, machine learning and AI to drive continuous integration improvement
  • Multi-style, uniting a variety of integration capabilities within a single platform

Take the next step

Learn more about IBM Cloud Pak® for Integration, a hybrid integration platform with an embedded AI that automates integrations based on your company’s specific operational data. This closed-loop approach provides flow and field-mapping recommendations, creates smarter API test cases and helps uncover inefficiencies in your current environment. IBM Cloud Pak for Integration creates seamless application connections across the entire technology ecosystem. It supports multiple integration styles, real-time event interactions, cloud-native architecture and shared foundational services — all with end-to-end enterprise-grade security and encryption in a single, unified experience.

More from Cloud

Hybrid cloud examples, applications and use cases

7 min read - To keep pace with the dynamic environment of digitally-driven business, organizations continue to embrace hybrid cloud, which combines and unifies public cloud, private cloud and on-premises infrastructure, while providing orchestration, management and application portability across all three. According to the IBM Transformation Index: State of Cloud, a 2022 survey commissioned by IBM and conducted by an independent research firm, more than 77% of business and IT professionals say they have adopted a hybrid cloud approach. By creating an agile, flexible and…

Tokens and login sessions in IBM Cloud

9 min read - IBM Cloud authentication and authorization relies on the industry-standard protocol OAuth 2.0. You can read more about OAuth 2.0 in RFC 6749—The OAuth 2.0 Authorization Framework. Like most adopters of OAuth 2.0, IBM has also extended some of OAuth 2.0 functionality to meet the requirements of IBM Cloud and its customers. Access and refresh tokens As specified in RFC 6749, applications are getting an access token to represent the identity that has been authenticated and its permissions. Additionally, in IBM…

How to move from IBM Cloud Functions to IBM Code Engine

5 min read - When migrating off IBM Cloud Functions, IBM Cloud Code Engine is one of the possible deployment targets. Code Engine offers apps, jobs and (recently function) that you can (or need) to pick from. In this post, we provide some discussion points and share tips and tricks on how to work with Code Engine functions. IBM Cloud Code Engine is a fully managed, serverless platform to (not only) run your containerized workloads. It has evolved a lot since March 2021, when…

Sensors, signals and synergy: Enhancing Downer’s data exploration with IBM

3 min read - In the realm of urban transportation, precision is pivotal. Downer, a leading provider of integrated services in Australia and New Zealand, considers itself a guardian of the elaborate transportation matrix, and it continually seeks to enhance its operational efficiency. With over 200 trains and a multitude of sensors, Downer has accumulated a vast amount of data. While Downer regularly uncovers actionable insights from their data, their partnership with IBM® Client Engineering aimed to explore the additional potential of this vast dataset,…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters