Industrial digital transformation aims to break down the silos between an enterprise's information technology (IT) and operational technology (OT), transform the physical behavior of OT devices into data, and extract effective information with the help of IT analytics. Through OT-IT collaboration, this instructive information helps optimize the entire physical operating system. For example, data from a factory's Manufacturing Execution System (MES) can be integrated with data from a Customer Relationship Management (CRM) system to shorten lead times, increase capacity, and reduce costs. However, according to the latest Industry 4.0 Maturity Index, 96 percent of companies surveyed are just starting out on their digital transformation journey, and only 4 percent have reached the “visualization” or “analytics” maturity stage. Clearly, this journey has not been kind to most businesses. Experience shows that getting started is the hardest, and getting OT data the hardest.
3 pitfalls that hinder OT data acquisition
Invisible environmental traps: Imagine that your OT data comes from drilling wells located in vast deserts at 40 to 50°C, petroleum pipeline systems stretching hundreds of kilometers in severe cold regions, transportation systems of fast-moving, high-frequency vibrating trains, chemical fuel tanks , or the switching system in an unmanned high-voltage substation. Various environmental disturbances, such as extreme temperature, vibration, chemical gas and electromagnetic radiation, can easily cause OT data acquisition electronic devices to malfunction, and data transmission is unstable from time to time, what's more, data inaccuracy leads to late analysis results mistake. For example, the large-scale automated warehouse system of a smart factory will generate strong electromagnetic interference at the moment of startup, causing abnormalities in nearby network devices. Even a single second of network outage can disrupt the accuracy of inventory calculations, as well as the production process of entire batches of products.
Unexpected Design Pitfalls: All OT devices, from sensors to controllers to control systems, have one thing in common: they are designed for highly specialized industrial applications. Industrial equipment is designed for a specific purpose. For example, the controllers and sensors used in drilling are not the same as those that support power monitoring equipment. However, if you want to understand the correlation between drilling control levels and power consumption, you must collect OT data from a variety of specialized equipment. What most people don't realize until now is that each device uses a specific communication protocol that only that device recognizes and understands. Therefore, to widely collect OT data, the ability to "talk" to different devices must first be obtained; otherwise, analyzing different OT data will be difficult and directly lead to increased costs.
Data identification pitfalls: Most of the data generated from OT devices or systems are raw data without context information. For example, PLC collects temperature data from sensors deployed in different locations for monitoring. When the temperature exceeds 45°C, the fan turns on to help cool down. However, the raw OT data collected directly from the PLC (ie, 45°C) lacks context for OT data analysts, as they have no way of knowing which devices the data was collected from, when it was collected, who owns the data, etc. These raw data are nothing more than meaningless values in their eyes. Therefore, a key part of OT data acquisition is to preprocess the raw data and give it context. To achieve this goal, OT equipment vendors must focus on convergence capabilities with IT, because preprocessing of raw data involves many technologies familiar to IT users. In addition, if there is too much data, data analysts will be overwhelmed by a huge workload in the process of converting the collected data into a unified format for the database. Fortunately, data conversion technology can now assist in this work.
Increase the intensity of OT data collection and accelerate the pace of IT/OT integration
OT data can make or break industrial digital transformation. Before starting a project, it is necessary to evaluate the different ways of obtaining OT data and the types of OT data available, and plan the format and structure required to convert OT data into IT databases; also need to avoid the three pitfalls mentioned above, combined with The company needs to strengthen OT data collection capabilities in advance. After these efforts, the pace of IT/OT integration will surely accelerate, helping you take the first step of industrial digital transformation firmly and steadily.
