This report updates our 2017 research agenda for digital business architecture and hybrid IT. Our plan for 2018 will be to study how process- and content-oriented application development will evolve to enhance developer productivity and accelerate the rate of business and IT automation. This will include how integration technologies will evolve to become intelligent stewards of data for automated systems. The rapid pace of change has raised concerns within many enterprises as to how to maintain control. Vendors are responding, making gradual improvements to enterprise architecture and IT portfolio management platforms that can better plan and track the rate of change, helping align resources to assure strategic intent.
The 451 Take
Industries are facing upheaval and disruption, and enterprises within them may fail unless they consistently reevaluate how they conduct business. The need to quickly adapt to customer expectations and the actions of rivals demands an ongoing transformative approach that automates the delivery of customer value and crafts new and unique competitive advantages over rivals. We believe the core technologies needed to do so include next-generation application development and deployment models that we call digital automatons platforms (DAPs). Applications crafted from DAPs must have unfettered access to data, regardless of form or source – whether on-premises, or within or across clouds. Hybrid integration platforms (HIPs) have emerged as the intelligent data stewards needed to feed such automated platforms and systems. Concerns over how to manage and control dynamic change will be the role of enhanced enterprise architecture and IT portfolio platforms, exploiting DAPs and HIP technologies to track and control the resources and outcomes expected of enterprise strategy and hybrid IT architecture.
Field of Research
The markets and technologies that will be researched and analyzed through 2018 include:
- Digital automation platforms – next-generation workflow automation and business process management (BPM) software equipped with design tooling, project management capabilities, rapid deployment technology, and low-code or no-code application development techniques. DAPs will play a key role in emerging DevOps strategies.
- Hybrid integration platforms – next-generation data and application integration technology used to link disparate distributed systems. HIPs will be equipped with metadata management, data quality management and powerful discovery capabilities.
- Enterprise architecture and IT portfolio management platforms (EAP) – next-generation strategic planning tools equipped with improved work and resource management tooling, simplified enterprise meta models, and lower-effort data stewardship and integration technology.
We look at these technologies holistically as interconnected systems designed to capture and transform business strategy (via EAPs) into execution plans that include business processes codified in and across distributed applications (via DAPs) that require integration (via HIPs). Collectively, and when properly integrated, these technologies help align business and IT, and provide techniques to improve execution, adaptability and consistency of IT performance and business outcomes.
Digital Automation Platforms
In recent years, BPM and ECM software have converged and transformed into process- and content-oriented application development and runtime platforms. They now enable low-code/no-code approaches that use graphical drag-and-drop tooling and templates to configure or compose, rather than code, applications. Vendors are positioning their new offerings as a means to enable digital business automaton – creating next-generation development environments that we now refer to as digital automation platforms.
A DAP is a set of tools and resources structured within a uniform framework to enable developers to rapidly design, prototype, develop, deploy, manage and monitor business process applications – from simple task-related workflows to dynamic unstructured collaborative activity streams, and even highly structured cross-functional enterprise applications. To do so, DAPs are equipped with a range of new capabilities that go beyond those of their BPM, ECM and application-development predecessors.
DAPs include new resources to assist in user-interface and application design; synthesize the use of new and emerging technologies found in next-generation devices; and simplify the means for collaboration among business and IT professionals to jointly design, prototype and develop applications. They can make applications 'smarter' using machine learning (ML) and artificial intelligence (AI) technologies that can learn from process execution to improve automation of tasks and decisions, and extract insight from data payloads.
Many DAPs will enable low-code or no-code capabilities that abstract away from the developer the need to use one or more programming languages to write software. Low-code/no-code techniques include visual models, prepackaged templates, and graphical design capabilities with drag-and-drop tooling to build software or integrate software and IT infrastructure. DAPs are equipped with libraries containing sample models and templates, connectors, plug-ins, code samples, APIs, and other components/objects to accelerate development and integration. In essence, they enable software to be composed rather than coded. Once designs are complete, DAPs can generate and put into production executable code, essentially combining both development and runtime IT environments into a somewhat simplified DevOps platform.
The overall benefit of a DAP is speed. In general, they can potentially shave 50-90% off development time versus a coding language. We believe low-code/no-code DAPs will craft nearly half of all applications developed in the coming years because they take less time to prototype, test and deploy to production; they are adaptive, and can rapidly enable intelligent process automation when combined with ML and AI capabilities.
DAPs will also integrate with, be supported by and ultimately include robotic process automation (RPA) technology. In its simplest form, RPA crafts software that automates repetitive tasks (e.g., human data entry) that may have been impractical to automate within packaged business applications, or by using traditional workflow or BPM tools. RPA vendors liken the software they create to robots (bots for short). More sophisticated RPA platforms can also call upon various ML and AI technologies to add contextual awareness and guidance of unstructured interactions (within, and in support of, business processes) toward desired outcomes.
DAP vendor landscape
451 Research's coverage will include, but not be limited to, the following DAP vendors:Hybrid integration platforms
As cloud services bear the burden of more workloads, integration challenges with enterprise systems emerge that require easier, faster and smarter solutions. Integration PaaS offerings are now staples in enterprise integration toolkits as a means to assimilate clouds, big data, devices and things into a new hybrid enterprise IT architecture. Moreover, enterprises have been accelerating investment in API lifecycle management technology and services. APIs link to – and exchange data across – all IT infrastructure, software and devices. They are now viewed as assets and products, and are being managed strategically. The iPaaS and API management markets have been converging and transforming to become what we now call hybrid integration platforms.
HIPs represent the next generation of technology used by enterprises to enable data exchange and interoperability across distributed on-premises infrastructure, software, cloud services, mobile devices and things. They are needed because enterprises are gradually transforming their IT strategies – and the infrastructure that supports those strategies – to a hybrid architecture (aka hybrid IT). Hybrid IT makes use of a variety of on-premises infrastructure and cloud services that, when designed and integrated properly, enable enterprises to deploy workloads to their best execution venues as determined by their price/performance characteristics (among other variables).
Moreover, the integration toolkits found in most enterprise IT shops are fragmented and aging. They were not designed to address the dynamic integration challenges of hybrid IT. Consequently, enterprises now seek the broadest range of capabilities from as few vendors as possible. This has driven a convergence trend within the integration markets, and we believe HIPs offer a uniform approach that represents the go-to platform for many enterprises.
The technologies used to create HIPs are relatively modern and capable of adapting to include new capabilities. HIP vendors differentiate themselves by building out their uniform platforms to consolidate a variety of capabilities, including enterprise application integration, enterprise service bus, extract/transform/load, message-oriented middleware, cloud-to-ground (links with on-premises infrastructure), cloud-to-cloud and API management. Several HIP vendors also enable data quality management (DQM), master data management and big-data integration for analytics.
The need to improve data quality is driving many enterprises to link DQM with integration tooling to streamline integration and shorten time to value – why integrate poor-quality data? Doing so requires the ability to describe and discover data. This will drive HIP vendors to craft new ways to create and manage metadata so that data can be easily discovered and consumed.
Metadata will be captured and used within enterprise data integration hubs that today are typically stand-alone repositories/gateways in many enterprises. HIP vendors will begin to include data hub capabilities in their cloud offerings. In doing so, HIP services can enable new means for business-to-business integration. B2B data exchange and integration has typically been a thorny challenge to all parties involved – the complexity makes data mapping and normalizing slow and costly. HIPs equipped with data hubs and enriched metadata can potentially streamline B2B integration to enable self-service capabilities. Indeed, blockchain technology may play a significant role in the future.
The HIP market is now being influenced by business process orchestration, IoT initiatives and streaming real-time integrations. As enterprises accelerate their use of containers and microservices, HIPs will be called upon for their integration and orchestration, as well as to enable means to ingest data to in-memory computing architectures and call out to serverless functions.
As these capabilities are realized, HIPs will play a vital role in many ML and AI initiatives because they control the data needed to feed mathematical models and algorithms. Several HIP vendors have already engineered ML and AI in their offerings to get a jump on the market.
HIP vendor landscape
451 Research's coverage will include, but not be limited to, the following HIP vendors:
Enterprise architecture and IT portfolio management
Historically, enterprise architecture (EA) has been perceived as the practices associated with managing an enterprise's IT stack. This perspective limited EA programs to various IT-standardization projects addressing things like governance, procurement practices, service requests, provisioning, vendor management, and interface definitions between applications and systems. In recent years, EA has been practiced successful by industry-leading firms to transform business vision and strategy into execution using software tools that document, design, structure and synchronize all the assets and resources (e.g., organizational structure, workforce behavior, business processes, performance management and IT systems) required for a chosen business strategy.
Today, however, in the age of digital transformation, hybrid IT architecture, agile development and the move toward DevOps, many enterprises question the value of EA programs. Strategists and architects alike are concerned that the methods and practices of structured EA programs may not be able to keep pace with fast-moving business agendas. We appreciate and understand the concern. However, our findings are to the contrary.
The accelerated pace of business, coupled with the rapid evolution of all things IT, still requires (more than ever) a steady guiding hand. Rather than define and defend architectural principles and managerial policies, EA programs must become agile themselves, providing guardrails that assure the pace of innovation remains in the strategic interests of the enterprise.
Some are concerned that EA programs may not be able to keep pace with rapid agile development and continuous integration and deployment (CI/CD) of software. We believe that keeping pace isn't the issue. The role of EA in agile development shops is to understand the changes being made to reveal an evolutionary pattern of software or system development, and to question when unnecessary or unproductive 'drift' from evolutionary patterns is discovered that may impact the tactical intent of agile projects or the strategic intent of the enterprise.
Doing so will require a new way of looking at the enterprise. Complex enterprise meta models that constantly need new data from various database and administrative systems through data stewardship efforts are too prone to data latency and errors. Next-generation EA and IT portfolio management platforms (EAPs) will be equipped with out-of-the-box connectors smart enough to capture and synchronize the data needed to more easily craft enterprise meta models for strategic planning and tactical project management.
Moreover, EAPs will emphasize the value and importance of resource and work management across the enterprise and within various departmental lines of business. While fundamental frameworks codified in most EA software will remain relevant (e.g., TOGAF, The Zachman Framework), simplified portfolio management techniques will drive the design of smart (AI-equipped), intuitive, and highly visual planning and management dashboards that will become the hallmark of next-generation EAPs.
Today, however, in the age of digital transformation, hybrid IT architecture, agile development and the move toward DevOps, many enterprises question the value of EA programs. Strategists and architects alike are concerned that the methods and practices of structured EA programs may not be able to keep pace with fast-moving business agendas. We appreciate and understand the concern. However, our findings are to the contrary.
The accelerated pace of business, coupled with the rapid evolution of all things IT, still requires (more than ever) a steady guiding hand. Rather than define and defend architectural principles and managerial policies, EA programs must become agile themselves, providing guardrails that assure the pace of innovation remains in the strategic interests of the enterprise.
Some are concerned that EA programs may not be able to keep pace with rapid agile development and continuous integration and deployment (CI/CD) of software. We believe that keeping pace isn't the issue. The role of EA in agile development shops is to understand the changes being made to reveal an evolutionary pattern of software or system development, and to question when unnecessary or unproductive 'drift' from evolutionary patterns is discovered that may impact the tactical intent of agile projects or the strategic intent of the enterprise.
Doing so will require a new way of looking at the enterprise. Complex enterprise meta models that constantly need new data from various database and administrative systems through data stewardship efforts are too prone to data latency and errors. Next-generation EA and IT portfolio management platforms (EAPs) will be equipped with out-of-the-box connectors smart enough to capture and synchronize the data needed to more easily craft enterprise meta models for strategic planning and tactical project management.
Moreover, EAPs will emphasize the value and importance of resource and work management across the enterprise and within various departmental lines of business. While fundamental frameworks codified in most EA software will remain relevant (e.g., TOGAF, The Zachman Framework), simplified portfolio management techniques will drive the design of smart (AI-equipped), intuitive, and highly visual planning and management dashboards that will become the hallmark of next-generation EAPs.
EAP vendor landscape
451 Research's coverage will include, but not be limited to, the following EAP vendors:
Conclusion
We believe the technologies and vendors that compose these markets to be among the core enablers of hybrid IT architecture critical for the digital transformation initiatives of modern enterprises. They represent the backbone technology needed for the strategic design and execution of automated systems, and are critical to enabling new competitive advantages over rivals.
Carl Lehmann
Principal Analyst, Enterprise Architecture, Integration & Process Management
Carl Lehmann is a Principal Analyst in the Development, DevOps & IT Ops Channel. He leads 451 Research's coverage of integration and process management technologies in hybrid cloud architecture, as well as how hybrid IT affects business strategy and operations. The markets covered in his research include enterprise architecture management (EAM) tools, hybrid cloud integration technology (including iPaaS and API management) and business process management (BPM) software.
SHERYL KINGSTONE
Research Director, Customer Experience & Commerce
Sheryl Kingstone leads 451 Research’s coverage for Customer Experience & Commerce, which covers the many aspects of how customer experience is a catalyst for digital transformation. She oversees the company’s coverage of a variety of customer experience software markets spanning ad tech, marketing, sales, commerce
Matt Aslett
Research Director, Data Platforms & Analytics
Matt Aslett is Research Director for the Data Platforms & Analytics Channel at 451 Research. Matt has overall responsibility for the data platforms and analytics research coverage, which includes operational and analytic databases, Hadoop, grid/cache, stream processing, search-based data platforms, data integration, data quality, data management, analytics, machine learning and advanced analytics.