Many industrial companies have shifted from exploring the benefits of digital transformation to determining how to execute on its promise. For operations personnel, this means implementing things like remote monitoring, connected and optimized plants, digital procedures, predictive asset failure detection or outcome-based services.
Doing so requires converging modern information technology (IT) concepts and software with operational technology (OT) and processes. For operations, successful convergence first begins with understanding key digital transformation IT concepts and how they will impact operations. Rather than focus on the digital transformation IT concepts through a technical lens, let’s look at them in terms of their implication on industrial operations.
Vendors are moving aggressively to develop new solutions or retrofit existing solutions in the cloud. For operations, the cloud provides much more flexible ways to create, consume and support software. It is critical for supporting some of the benefits of modern software, such as rapid scalability and ubiquitous mobility. Additionally, cloud pricing models can shift costs from capital to operating budgets.
Some deployment nuances within the cloud that are worth knowing include:
- Public cloud: Computing services offered by third-party providers over the public Internet, making them available to anyone who wants to use or purchase them.
- Private cloud: Computing services offered either over the Internet or a private internal network and only to select users, instead of the general public.
- Hybrid cloud: A computing environment that combines a public cloud and an on-premise, private cloud by allowing data and applications to be shared between them, managed by a set of common tools.
- Hosted (or virtual) private cloud: A computing environment dedicated to a user’s account. It is logically isolated from other virtual networks in the cloud.
- Serverless computing: A cloud workload model that eliminates the need to manage server hardware and software. Users are charged only for the resources used, rather than for infrastructure (servers) or platform access (subscription).
Microservices-based software is a core concept of modern cloud application platforms. For these platforms, hardware and software are provided as a menu of smaller (micro) services rather than a large application or system. The services can operate in standalone fashion where needed or can be combined logically using programming.
A modern platform as a service (PaaS) is the “store” you visit when you want to purchase or lease various microservices, often called “tools.” Many times, providers position themselves as platforms when the model is a software as a service (SaaS). It’s a key distinction in terms of whether they expose solution microservices (code) and data for you to build upon.
From an operations perspective, it’s important to think about platforms in terms of how you want to leverage it.
Application lifecycle management
This concept relates to software development in PaaS systems. It covers a range of typical IT functions such as code governance, process flows, production, testing, etc.
DevOps unifies software development and operations processes, which is key to agile software development. It injects speed and scale into software development by reducing development cycles and making software releases more dependable while ensuring applications align with business objectives. It drives the design of microservices, collaboration tools, monitoring, etc.
The working environment in a platform, often designated a “cockpit” or “sandbox,” is where data is managed and applications are built, deployed and administered. This is also where security and user access are usually overseen. The “dev environment” is primarily for use by IT-side developers. However, as operations integrate more engineers and subject matter experts (SMEs) into analytics and application deployment, an operational user experience is increasingly key to platform adoption and use.
Technically speaking, the possibilities of artificial intelligence (AI) fall into two camps: broad and narrow. Broad systems are capable of human behavior (think Terminator or HAL 9000). Broad AI doesn’t exist yet, though we are moving toward aspects of it via things like autonomous vehicles.
Narrow AI is important, as it replicates human capability, not behavior. In operations, AI has quickly become marketing speak for things like machine learning, natural language processing and chatbots, all examples of narrow AI.
This term came on the heels of cloud and platforms. It is a way to develop and use applications basically anywhere on physical or virtual hardware without the need for deep expertise. Kubernetes and Dockers are platforms for doing so. ARC analyst Harry Forbes continues to track container advancements.
Industrial network edge
Like its operational industrial edge counterpart, the network industrial edge occurs at the logical extremes of an IT network. It consists of the equipment and devices capable of data communication, management (e.g., security, visualization, preparation, storage) and/or computing. The cloud became the centralized environment from which the edge could then be identified and defined. The network edge was previously viewed as the equipment and systems that fed data to it. However, that definition is too simplistic for today’s industrial purposes.
Don’t get lost in the technical detail
Not meant to be a comprehensive list, these terms are likely to be the ones OT personnel will encounter most frequently. Without an understanding of IT concepts, processes and architectures, it is easy to get lost in the technical detail of IT/OT convergence, much of which can end up being irrelevant to operations. It’s important to concentrate on understanding the operational impact of technology vs. breaking it down functionally. If you do need to dig into the functionality, focus on the why instead of the what.
>>Michael Guilfoyle, firstname.lastname@example.org, is director of research at ARC Advisory Group. His expertise is in analysis, positioning and strategy development for companies facing transformational market drivers. At ARC, he applies his expertise to developments related to IIoT and advanced analytics, including machine learning.
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