Digital businesses necessarily need to be supported by highly integrated set of business applications. Integration is a key enabler of competitive advantage and usually ends up creating a massive backlog of work for integration specialists. However, it is critical to recognize that integration occurs throughout the enterprise and is not just for specialists. Hence, you will need to create an integration strategy that accommodates various integration personas within the organization. Additionally, you will also need to select appropriate tools for each persona as part of your integration strategy implementation.
By 2021, at least 50% of large organizations will have incorporated citizen integrator capabilities into their strategic integration infrastructure.
However, making independently designed systems work together is extremely challenging due to incompatible technologies, inconsistent data models, use of many different interaction styles, multiple use patterns, multiple user personas, countless endpoints, continuous change, globalization, etc.
Enterprise Integration Platform as a Service is a lightweight mix of application and data integration features. They are typically model-driven, support no-code development tools and come with prepackaged integrations. They are increasingly providing API management features. Such platforms are best used when there is a clear objective to derive value in a short time period, are cloud / mobile centric, involve simple to medium complexity integrations, need to support “good-enough” QoS.
Common integration patterns and tools used include Cloud-to-Cloud (application integration with other cloud solutions – iPaaS), Cloud-to-Ground (application integration with on-premises solutions, iPaaS, Existing ESB or similar platforms) and Orchestration (application integration and orchestration; iPaaS, Existing ESB or similar platform). Custom integration solutions can work at first, but organizations are strongly considering iPaaS for quicker development and centralized administration.
Market dynamics are shifting towards dynamic recognition of data diversity coupled with machine learning of data types. Data Integration Tools combine application and data integration capabilities, shared services management for partner networks with enhanced registry and orchestration capabilities. Shifting from perpetual, unit to modular / modality-oriented models to adaptive pricing models.
The data Integration tasks can run “anywhere”. For example, they can divide processing and distribute data transformation workloads in Hadoop, cloud and IoT ecosystems. In the near future, the demand to blend all integration styles will increase: batch, virtual, message, replication, synchronization, streaming. Tools that serve multiple personas from the citizen to infrastructure expert will be in demand. Additionally, there is increasing demand for smart metadata driven development and introspection.
By 2020, 75% of integration platforms will leverage machine learning to automate integration between application APIs, thus reducing the need for skilled integration specialists.
By 2021, 30% of new integration technology deployed will include an AI component for powering a highly adaptive integration infrastructure and accelerating time to delivery.
Integration work significantly exceeds other types efforts required for AI / ML projects. However, AI for integration is becoming increasingly important, as it is very challenging to design for integrations for emerging and unknown future ecosystems, platforms and things. It is challenging to manage integration when everyone needs to do it (Integration Specialist, LOB Managers, Citizen Integrators and Application Developers).
AI and machine-learning techniques applied to integration are emerging as digital integrator technologies. There is a growing critical need for businesses to leverage AI to support present and future needs of increasing automation, deriving insight and enhancing engagement. Emerging technology innovations on AI directed at integration and enabling citizen and ad hoc integrators. However, organizations lack of awareness of these emerging offerings of AI in integration platforms and the benefits of applying such new technologies.
AI techniques applied to integration improve integration user experience, provide metadata analysis for improved data mapping, and provide process analysis to suggest better integrations. Such technologies target the reduction of the need for integration specialists.
As integration has now evolved into a pervasive task, digital integrator technologies can dramatically simplify integration and empower many (more) people to perform integration work themselves. AI in integration potentially shortens the learning curve of both specialist and less / non-technical integrators to manage data and business flows, enabling extensive range of roles to perform integration tasks to pervasively address needs.
Organizations need to investigate the benefits of AI in integration platforms by experimenting and proceeding with caution as this innovation is in a nascent stage and scarcely proven.
In this blog, we explored some of the emerging trends in integration approaches and solutions that need to be understood now for developing strategies for addressing the associated governance, risks and security requirements, comprehensively.
In subsequent blogs we will explore the impact of shifts in operating environments, and improving trust and security in the use of business applications.
- Eric Thoo, Artificial Intelligence in integration: The rise of digital integrator technologies, Gartner Application Strategies & Solutions Summit, 2017.
- Keith Guttridge and Betty J. Zakheim, Integration personas and the impact on your integration platform strategy, Gartner Application Strategies & Solutions Summit, 2017.