No professionals are more important to the Industrial Internet of Things (IIoT) than data scientists. Data scientists are charged with taking vast amounts of raw industrial data, creating structure within that data, and ultimately finding valuable meaning. In other words, let’s dump a bunch of dirty data on these data scientists, tell them whatever we know about it, and trust they will magically build the model that provides meaningful insights and improves our processes. Powerful idea, but highly debatable on actual effectiveness.
The ideal IIoT deployment, whether it’s in the cloud or at the edge, is a seamless marriage between the know-how of the factory floor domain expert who makes sense of the physical industrial world and the power of a data scientist to derive complex analytics and machine learning algorithms. This combination of skills creates the industrial models that accurately prevent a bad outcome before it happens, such as a gigantic compressor failing in the middle of nowhere or an upstream process affecting the yield downstream. Notice that before it happens is emphasized.
This grand vision of data science that prevents expensive mishaps and inefficiency from wreaking havoc on operations faces significant hurdles. A lack of input from domain experts, the “dirtiness” of unstructured, raw data and the ultimate challenge of applying a model quickly enough to prevent a bad outcome, all combine to undermine this vision. Data context from the people who understand the industrial world, proper cleansing and alignment of this data, and the ability to apply insights in real-time transform the goals of data science from an unachievable ideal into reality.
Operations technology (OT) is the domain that manages industrial environments like factories, refineries, mines, etc., and is where all this valuable industrial data originates. OT staff are experts in these machines and processes, as well as the systems that control them. An OT person has deep domain knowledge, which can range from knowing what a failing machine sounds like to how different machine functions correlate to one another.
This expertise is the missing link in providing context and clarity to the vast amount of data points produced by machines and sensors. Without knowing how to interpret the data and some starting points of the relationship, data scientists are often left in the dark. To bridge this gap, OT experts must participate in creating the algorithms alongside data scientists without the need to program it themselves. This collaboration allows OT experts to easily convert raw data into meaningful results, eliminate bad data points and even begin to define correlations and patterns. Analytic tools that enable OT teams and data scientists to speak the same language are an absolute requirement.
Read the source article at InformationWeek.com.