When we talk about networking and IT in process industries, we usually focus on technology. Today’s computing infrastructure is very good at doing tasks which simply aren’t practical for human beings. But at the same time, those kinds of systems do not exhibit much in the way of creativity. Yes, they can handle all the big data housekeeping, but they need the human touch to create value from this data.
The idea of turning raw data into information is what these systems are all about, and people have to guide the effort. Finding the right people to drive those efforts in optimal directions isn’t easy, as Bob Karschnia points out in his article in the December issue of CIOReview, Finding the Right Personnel Resources to Analyze Big Data.
IT and OT executives in the process manufacturing industries know there is a tremendous value just waiting to be unearthed from their big data, but they're not always sure about what type of talent is required to turn this raw data into actionable information. Executives in other industries and commercial sectors with complex operations have similar challenges.
Bob gets into that special term: OT. They’re the people who keep the plant running and deal with all the networks and equipment associated with the DCS and other plant automation systems. Banks and insurance companies don’t have those kinds of people or data, which is why process manufacturers need their own set of specialties. What is the practical path forward?
The three main choices are:
So how do these kinds of individuals work, and more importantly, what can they do in a process manufacturing context? Bob says data scientists, number 1 above, may be good at math, but often aren’t well versed in operations. A similar problem emerges when trying to use the second strategy.
The second option is to take existing IT personnel and train them in two areas: data analytics and operations. The advantage of this approach is existing personnel can be used, and their IT expertise usually allows them to pick up on data analytics concepts quite quickly. But as with data scientists, our experience shows few if any IT personnel have the requisite knowledge of process operations. Therefore, extensive training would be required to bring an IT expert up to speed on process operations, and certain complex processes could remain outside their realm of understanding.
So that leaves number 3. Its approach is to provide software tools for people to utilize their skills, but without creating additional workload for other people to bring them up to speed.
In the process industries, companies like ours have extensive OT expertise, primarily in the form of engineers and scientists with many years of process operations experience. The solution we’ve found most effective is to simplify the data analytics step—the transformation of raw data into actionable information—through the use of software apps designed to analyze data with solutions that are simple to use. For example, a pump app looks at data from sensors mounted on or near a pump and guides users through the required data analytics.
Do such tools exist? Yes, and a primary example is the Plantweb Insight family. These apps cover a variety of specialized functional applications, such as evaluating the performance of a heat exchanger or centrifugal pump. This gives them very specific capabilities for examining a small but critical segment of the asset population. Fortunately, there are enough apps to cover the bulk of critical plant assets, and they are easy to use and inexpensive to deploy. Plantweb Insight fills this critical personnel gap, reducing the need for pricy data scientists and IT experts.
You can find more information like this and meet with other people looking at the same kinds of situations in the Emerson Exchange365 community. It’s a place where you can communicate and exchange information with experts and peers in all sorts of industries around the world. Look for the IIoT Groups and other specialty areas for suggestions and answers.