Prevent These OT Data Pitfalls from Ruin of Your Industrial Digital Transformation

3 Kinds of Obstacles Hindering OT Data Acquisition

• Obscure environmental obstacles: Imagine your OT data originates from a well in a desert where temperatures fluctuate between 40 to 50°C, an extensive oil pipeline system in freezing regions, a fast-moving train transportation system subjected to intense vibrations, a chemical fuel tank, or a switchgear system within an unmanned high-voltage substation. Various environmental interferences, such as extreme temperatures, vibrations, airborne chemicals, and electromagnetic signals, can easily disrupt the functionality of OT data-acquisition devices, leading to sporadic data transmission instability, or even worse, inaccuracies in data, which can result in faulty analyses later on. For instance, large automated warehouse systems in smart factories emit strong electromagnetic interference during startup, causing disruptions in nearby network equipment. A momentary network failure could jeopardize the accuracy of inventory calculations as well as the production process for an entire batch of products.
• Unforeseen design hurdles: All OT devices, ranging from sensors and controllers to control systems, share a common purpose: to support highly specialized industrial applications. By nature, industrial equipment is purpose-built. Controllers and sensors used in a well differ from those used in power monitoring devices, for instance. However, when attempting to correlate control levels of a well with power consumption, a diverse range of specialized equipment must provide OT data. Currently, it is widely recognized that each device operates on a distinct communication protocol that only it can interpret. Hence, to extract OT data from multiple sources, it is vital to have the capability to communicate with different devices; otherwise, analyzing diverse OT data becomes more challenging and incurs additional costs.
• Data recognition challenges: Data generated by OT devices or systems is primarily raw data, devoid of contextual information. For example, PLCs collect temperature data from sensors positioned in various locations for monitoring purposes. When the temperature surpasses 45°C, fans activate to reduce the temperature. However, for OT data analysts, the raw OT data (e.g., 45°C ) obtained directly from the PLCs lacks context as they cannot identify the source devices, data acquisition time, and data ownership, among other details. This raw data holds little value in their eyes. Therefore, preprocessing raw data and providing context is critical in OT data acquisition. To achieve this, IT capabilities should be integrated into the development focus of OT equipment vendors since IT processes play a significant role in preprocessing raw data. Moreover, an excessive amount of data can overwhelm data analysts in normalizing the collected data into a standardized format for a database, a task that can now be simplified by data transformation technology.

Elevate OT Data Acquisition to Hasten IT/OT Integration

OT data wields significant influence on the success or failure of an industrial digital transformation initiative. Before embarking on a project, it is prudent to understand the various methods of obtaining OT data, the types of OT data accessible, and plan for converting the data into formats and contexts required by the IT database. By steering clear of these three types of obstacles and aligning with your company’s requirements, you can fortify the OT data acquisition capability in advance. By doing so, the integration of IT/OT operations can be expedited to kick off industrial digital transformation securely and steadfastly.

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