There has been a lot of controversy lately about the growing
competition and overlapping roles of Operational Technologists (OT) and
Information Technologists (IT) in industrial automation. While traditionally IT
technologies have been delineated, the increasing proliferation of IT
technologies at the operational level - especially in the areas of IIoT and
cybersecurity - makes it clear that IT / OT convergence must remain. At a
recent LNS Research webinar, analyst Matthew Littlefield explored the idea of
how IT can play an important role in driving IIoT and analytics adoption.
No one can deny that IT professionals have critical skills
in data management and the use of cloud, networking and cybersecurity
technologies. These are all integral parts of the IT role. Of course, most of
these areas, with the exception of clouds, overlap with OT. What sets IT apart
from others is the scale of the operations they have to deal with in these
areas. While historians are an important part of the implementation of
automation, the amount of data they typically collect regarding IT networks is
often very small. When we talk about IIoT and analytics, the amount of data
collected often far exceeds the scale of today's automation systems. This is
the result of an increase in the speed of data collection, additional data
sources (for example, sensors), or a combination of both.
The IT department's expertise in processing large amounts of
data and moving that data into secure long-term repositories such as the cloud
can make them a key partner or consultant in implementing digitalization and
industrial automation analytics. And let's not forget that they bring
additional cybersecurity expertise, which is especially important if the data
is stored offsite or in the public cloud. IT professionals can also share their
networking expertise, providing segmentation and adding another dimension of
security through their business responsibilities in this area.
Specified all that IT has to offer, it is important to
understand where they may have limitations and where OT understands the
requirements better. This intersection is often where OT and IT come into
conflict and where conflict can arise. The question arises: why is this
happening in the convergence of IT / OT?
It all derives down to how they approach technology needs
depending on what they're used to.
For example, OT is used to work with a variety of equipment,
from mechanical valves, pumps and motors to sensors, PLC / PAC, computing
platforms and various automation applications, SCADA, HMI, Historian, etc. And
specific analytical applications involving complex algorithms and machine
learning is undoubtedly the next wave. For OT, the leading platforms that run
these applications are just a tool, and like everything else in the OT world,
eliminating unplanned downtime is an important KPI, especially in the edge
where support resources may be scarce or unavailable. Automation systems are
often highly deterministic and run in real time; certain events must happen at
a specific time, in a specific order, and within tight tolerances.
The IT world is a little different. While it is important
for the IT department to avoid downtime, any IT professional will tell you that
security is their number one priority. Typical IT clustering or high
availability virtualization techniques enable IT to standardize and scale its
operations. But the inherent complexity of these deployments does not translate
well into high-performance remote deployments such as control rooms or control
cabinet environments. In addition, the IT world does not deal with
deterministic events in real time, and variability of a few seconds or even
minutes does not really matter. Think the email or web page is loaded.
However, from an analytics perspective, the automation
industry generally agrees that there is a need for cloud (IT) and edge (OT)
analytics capabilities. IT / OT convergence is a must. Cloud analytics are
ideal for meeting broad requirements where enterprise data sharing can be
beneficial, such as revenue optimization, asset performance benchmarking, or
digital twinning. Edge analysis is critical to improving operations in areas
such as advanced process control, real-time quality checks, and device failure
detection. This is the OT area. These locations require simple, easy-to-manage
platforms with built-in redundancy and self-monitoring capabilities that enable
OT to plan for service or replace, rather than react to it.
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