This post is part 4 in a 4-part series: “How Sentient Infrastructure Changes the Game for Field Services, and Why This Is Important”.
Technology Architecture Choice Has Real Business Implications
As any product developer knows, business requirements logically precede technical requirements. In the case of field services for IoT-enabled infrastructure, the technology tail can also wag the business dog, so to speak. Cliché aside, a company’s choice of technology architecture for its deployed smart infrastructure can have profound implications for the company’s business model and prospects for value creation.
Enhancing an ecosystem with artificial intelligence capabilities completes the picture of sentient infrastructure by creating a self-learning system that improves with time and experience. By taking this architectural approach, forward-thinking companies can transform their deployed infrastructure ecosystems from merely “smart” to truly “sentient.”
Industry-leading companies with deployed infrastructure realize that efficient field service operations not only drive a lean cost structure, but also support business continuity and revenue growth. Their field service teams rely on processes and technologies that deliver visibility across their entire set of deployed systems, generating data that enables precise and effective action. The value of this data is clear, yet it is equally important to optimize the manner in which this data is collected, synthesized, and transformed into actionable insights.
Transforming an array of deployed infrastructure into an integrated, IoT-enabled ecosystem that operates with partial or full autonomy is a complex undertaking, especially when a company is retrofitting infrastructure that has already been deployed. If the company approaches this task by seeking a multitude of “best-of-breed” technologies, the complexity can increase exponentially, since integrating multiple components becomes a necessity, and since achieving interoperability often becomes a challenge. Simplification through standardizing technologies, consolidating systems, and minimizing the number of technology vendors involved can dramatically improve results.
The Cloud Plays An Essential Role
Cloud technologies address many of the architectural and performance challenges inherent to complex distributed systems, including large-scale deployments of IoT-enabled infrastructure. The application layer provides the solutions that field service practitioners use on a daily basis to perform essential tasks; the platform layer enables the development, deployment, and operation of the applications; and the underlying infrastructure layer provides base-level computing, network, and storage capabilities upon which the platform and application layers rely. Simple, right?
At this point in the evolution of cloud computing, the “what” of the cloud for IoT shouldn’t be in question, and we won’t spend time here making the case for cloud vs. on-premise IoT solution architectures. The “how” of using the cloud for IoT remains a question for many companies, and many companies may be choosing a technology path that drives significant inefficiency in the business. This inefficiency exists not only in the form of technical performance, but also in the form of IT operational processes, the field service processes that depend on IT, and the company’s business model that relies on the availability, performance, and security of the deployed IoT infrastructure.
Simplification and efficiency start at the cloud infrastructure layer. A company utilizing the cloud for its IoT applications ultimately relies on the compute, network, and storage capabilities of the IaaS layer. Configuring these components to deliver orchestrated, IoT-specific functionality represents a daunting engineering challenge, especially when the cloud provider’s components are constantly evolving, and when the cloud provider adds new components to its portfolio. Amazon Web Services, the market leader in cloud infrastructure and services, currently offers over 90 discrete products, of which at least 50 are relevant for IoT solutions. This is a large portfolio for any IoT engineering team to master, not to mention manage over an extended period of time. Deployed physical infrastructure tends to stay in place.
Companies that choose to tackle this engineering challenge can succeed if they have in-house IT and product development talent with the right skills, adequate schedule and budget capacity to take on an extensive custom engineering project, and the ability to continuously manage and evolve the solution that they architect as the cloud provider’s underlying architecture changes over time. Even when companies have the skills, resources, and organizational stamina for this, taking this path may represent a significant opportunity cost vs. other activities that the company could have pursued instead.
Cloud-based platforms (PaaS) can substantially diminish or even eliminate this challenge. They allow the business to focus on market-facing activities rather than engineering and maintaining the underlying cloud infrastructure. The company can devote the majority of its attention to understanding the data picture that the device ecosystem generates and using that data to drive revenue-generating activities. Creating the organizational capacity to maintain this focus on the business becomes even more important with the evolution towards sentient infrastructure. As that infrastructure continues to become more autonomous, the business must still maintain an understanding of what the infrastructure is doing to regulate itself and drive actions and processes in the physical world. Autonomous infrastructure doesn’t absolve the company of responsibility to understand and guide what the infrastructure is doing.
As a business with deployed physical infrastructure contemplates its options for an ideal future-state IoT technology stack, it should consider the “winning combination” to be cloud-based technologies that offer:
- Market-facing IoT applications with specialized and/or differentiated device and ecosystem-wide functionality
- Orchestration of the underlying infrastructure layer (IaaS) across compute, network, and storage functions
- End-to-end management of the market-facing IoT application across the entire lifecycle
- Full-spectrum field service capabilities (e.g., scheduling, workflow management, entitlement management)
- Integration with business operations systems (e.g., CRM, ERP, accounting, billing)
- Enterprise-grade big data analytics capabilities, including machine learning functionality
- The ability to drive automated actions as a result of advanced analytics outputs
A Bright, Digital Future for Field Services
The business opportunity for servicing deployed infrastructure is already immense, and technical advances that impart intelligence to that infrastructure increase both the sophistication of the service challenge and the size of the associated service revenue opportunity. Forward-thinking companies with deployed infrastructure, as well as field service providers that seek to lead service management in this new marketplace, must become adept at managing the increasingly multifaceted data picture that sentient infrastructure creates. As more of the world’s deployed equipment and systems become capable of autonomous activity, companies, municipalities, and individuals will increasingly rely upon it. As our dependency on sentient infrastructure increases, so does our dependency on field services that ensure the availability, performance and security of that infrastructure, as well as the integrity of the data that courses across it. Field services businesses that embrace this opportunity and develop the capabilities to master it will emerge as key players in this latest realm of our digital future.