Behind the Revolution of Industrial Digital Transformation: Uncovering the Mysteries of OT Data

If you want to explore more, check out the OT Data Revolution Series:

• Unlock the secrets of OT data by downloading our white paper.
• The Downfall of Your Industrial Digital Transformation: Beware of these OT Data Traps
• Transforming OT Data: A Makeover towards Greatness with Four Core Abilities
• First Step in Building a Robust OT Cybersecurity Foundation: Asking the Right Questions
• Exploring the Potential of OT Data Beyond Its Protective Barrier
• For more information, visit our microsite.

In the midst of the COVID-19 crisis, leaders in the industrial field are moving toward one of the most impactful changes in recent memory: Industrial Digital Transformation (Industrial DX). However, before joining the trend, it is crucial to comprehend what Industrial DX truly involves, starting with its foundational element: operational data (OT data).

Visualize a factory that transforms harmful waste into natural fertilizer. If there is a slight temperature variation during production, it could disrupt the neutralization process of the active component, leading to reduced production capacity. Previously, on-site staff had to conduct inspections and make adjustments as needed. However, this reactive approach has proven ineffective in handling sudden changes promptly. Therefore, accurate prediction of temperature changes within six hours before the actual fluctuation is critical for making necessary adjustments to maintain optimal neutralization. Various data types, such as equipment operation data, controller data, field temperature data, and weather forecasts, are essential for precise predictions. These forms of operational data (OT data) serve as the fundamental components of industrial DX.

For the creation of effective business insights, OT data not only needs to be gathered but also analyzed to develop a pertinent strategy. Therefore, Industrial DX can be visualized as a process of discovering and interpreting the significance of the existing OT data. A task easier said than done. Drawing from over three decades of experience in linking OT data, Moxa has observed that as the ‘value’ of OT data increases (i.e., higher expectations for data output), the challenge of connecting it also intensifies. Consequently, the original duties of OT data connectivity technologies — securely gathering, processing, and categorizing data before timely transmission — have evolved into more intricate procedures. To address the influx of data, substantial progress has been made in the methods and speed of data transfer. OT data connectivity has now become a hybrid expertise that integrates domain knowledge with cutting-edge technological capabilities. These advances in “OT data connectivity” embody crucial elements of success in Industrial DX. Here are some notable transformations:

Transitioning to Optimizing Future Outcomes from Merely Monitoring Current Status

In the past, the primary objective of collecting OT data was to monitor and control existing operational systems. OT data was utilized to ensure stable machine operations by monitoring current states of devices on the factory floor. It could, for example, regulate oil pipeline flow rates to align with production targets. Essentially, it focused solely on maintaining the present status. However, Industrial DX takes a step further by looking into the future. Acquiring OT data is now about integrating data for future analysis rather than just for monitoring and control of the present. By identifying key factors influencing operational efficiency, optimizing processes, and realizing new business prospects, OT data has enabled early industrial adopters to formulate innovative business models. A notable instance involves a leading power system integrator that used historical methanol usage data in hydrogen energy batteries to project future energy consumption for each customer. This led to the development of personalized charging schemes for clients, transforming the existing usage-based billing into a Machine-as-a-Service monthly billing model, which proved beneficial for both parties.

Shifting from Single Data Points to Tangible Information Value

The distinction between IT and OT diminishes substantially when OT data undergoes in-depth analysis. Previously, ensuring reliable data transmission was the main concern. Nowadays, data quality assurance is equally imperative. This presents a significant challenge for Industrial DX. Industrial equipment typically has long lifecycles, resulting in vast amounts of incomplete or unstructured OT data accumulation over time. It falls on IT professionals to rectify this by cleaning and converting data for usability. Ideally, data cleansing consumes extra time and resources but renders the data usable. However, in the worst-case scenario, when data is incomprehensible, it becomes entirely futile. For instance, viewing an output figure like “5” without any labeling renders it indecipherable. Without contextual information, such as data formatting familiar to the IT system, this number could represent anything. To tackle this common issue, one approach involves preprocessing such data to align it with the requisite format using an embedded program in the OT data connectivity device. This preprocessing assigns context to the data, making it interpretable. Transforming OT data into actionable data, thereby granting it “analytical utility,” is a critical step heralding the onset of the OT data revolution.

Addressing Varied and Complex Needs through Diverse Data Sources and Formats

Traditional control systems rely on diverse OT data to sustain day-to-day operations, ranging from simplistic data indicating water tank levels to intricate production recipes or processes. However, the demands of industrial DX extend beyond these. Consider the renewable energy sector: to swiftly eradicate shadows or stains on solar panels, additional data is essential. Apart from power generation statistics, environmental factors like temperature and humidity play a crucial role. Combining this data with real-time surveillance drone feeds and AI-driven analytics facilitates precise identification of affected solar panels. Armed with this insight, immediate and precise maintenance schedules can be established. Consequently, a wealth of OT data from disparate sources not only trims conventional costs but also amplifies production efficiency.

Shifting from Linear Control to Real-Time Circular Feedback Mechanisms

Traditional automation systems relied on predetermined conditions for operation. The advancements in Industrial DX are revolutionizing this paradigm, transitioning towards dynamic feedback loops that shape operations in real-time based on continuous data analysis. This evolution signifies a shift from rigid control structures to adaptable feedback mechanisms, enabling more responsive and agile industrial processes that can swiftly adapt to changing operational demands.

Operating systems prioritize real-time control to a high degree. Operational Technology (OT) data often serves as an indication for a specific time frame within the linear control process. Its relevance ceases once the particular process concludes. Contrarily, industrial Digital Transformation (DX) places emphasis on a distinct form of real-time interaction, focusing on the “OT data collection/analysis/feedback” cycle. Leveraging advanced big data processing technologies, faster networks, and enhanced industrial computing capacities, Information Technology (IT) can now analyze continuous OT data without interruption and promptly provide feedback to operational equipment post data analysis. This cycle of data reception, analysis, and feedback empowers businesses to make real-time adjustments. Consider KPMG’s services to small and medium-sized manufacturers as an illustration. To minimize wastage of labor hours and material resources due to defective products, additional OT data like vibration, temperature, speed, and current is gathered, uploaded, and scrutinized via an AI platform. Through analysis, it was uncovered that heightened tool current frequency in a specific machine indicates tool wear and tear. Consequently, the tool can be replaced proactively to ensure premium output.

### The Future Holds Increased, not Decreased Incorporation
In the era of Industry 4.0, extensive automated systems (e.g., the distributed control system in an oil refinery) have the capability to process substantial volumes of OT data per second. Notwithstanding, this data is utilized solely while the equipment is operational. Upon completion of operations, the data interpretation also halts. OT data is consequently used solely for present interpretation. Nonetheless, industrial DX takes a step ahead. With a larger dataset, simulations and analyses can be executed swiftly to boost real-time operational efficiency and mitigate operational hazards. For example, in a bid to avert overcrowded carriages during the pandemic, Taiwan’s railway company fitted pressure sensors on its trains to gauge carriage loads. Before a train arrives at the station, the sensors transmit information along with live feeds from each carriage’s CCTV to the control center. This approach gives the control center an accurate depiction of carriage congestion, enabling it to relay this information to passengers waiting at the platform or prompt management to assist in crowd dispersal.

### Data Security Vital for Enterprise and National Security
Though cyber privacy typically isn’t the central focus concerning OT data, it constitutes a high priority for Industrial DX. A significant portion of OT data originates from critical infrastructures (e.g., monitoring equipment in water and power plants) or crucial operational intelligence in key manufacturing facilities (e.g., oil refineries and semiconductor factories). If tampered with maliciously, such data could result in immeasurable losses. In February 2021, hackers managed to breach the Supervisory Control and Data Acquisition (SCADA) system of a public water treatment plant in the U.S. by exploiting outdated Windows operating system versions and subpar network security. The plan was to elevate the sodium hydroxide levels in water to a harmful range. Fortunately, onsite operators promptly detected the anomaly and averted the threat. With the surge in cyber threats, cybersecurity must be a top priority as more industries may become targets of such attacks with grave consequences.

Industrial DX is shedding light on previously enigmatic and overlooked OT data. This transformation is directly accelerating the convergence of IT/OT in knowledge, operations, security, and even workforce mindset.

***Would you like to delve deeper into the mysteries of OT data? Listen to OT Data Next here:***