The OT data revolution
Operational data is the foundation of industrial digital transformation.
In the past, the companies with the fastest or most advanced automation equipment would have stood head and shoulders above the crowd in the industrial automation game. However, with the surfacing of issues such as increasing labour cost, an ageing population, the reduction of available land and the emerging awareness of environmental protection, the original strategy of simply increasing production line capacity is simply not viable enough anymore. Fortunately for industry, another door has opened thanks to the rapid development of technologies such as cloud computing, big data analysis, AI and machine learning. As a result, ‘digitalisation’ and ‘intelligence’ have emerged as the next big things in industrial automation development and are set to lead new advancements.
The three stages of digital transformation
Industrial digital transformation is a gradual process that is divided into three stages:
- Digitisation: The converting of physical state information from an analog format to a digital format.
- Digitalisation: The establishment of an automated process or system on top of digitisation. Examples can be found in a digital dashboard, a secure digital network for remote maintenance or a system that monitors the connection between a traditional power grid and renewable energy sources.
Digital transformation: This refers to the process that involves comprehensively collecting, integrating and analysing different types of relevant data in a system. Subsequently, it creates a new digital-based operation and business model.
The pillar of transformation: OT data
The key to successful digital transformation lies in OT data, since the first stage — digitisation — is the collection of a large amount of OT data. With each successive stage, more value is added to the existing OT data.
Industrial digital transformation is a process in which value is continuously added to OT data, fundamentally changing OT data across multiple dimensions, specifically in terms of its quality and quantity.
Qualitative changes to OT data
Industrial digital transformation has brought qualitative changes to OT data. We can consider these changes from two different dimensions: purpose and impact.
The purpose of obtaining data: from monitoring to optimisation
Originally, OT data was obtained to monitor and control a system. Its purpose was to ensure that the system can meet the expected requirements or goals, such as monitoring whether the equipment or system is operating correctly, or controlling the equipment’s operating efficiency to meet expected goals.
However, in the digital transformation era, the purpose of obtaining OT data has since changed from monitoring and controlling to optimisation. Such optimisation creates tailored, long-term strategies that are evidence based. Through in-depth analysis of OT data’s composition and modification, the key factors affecting operational performance could be indicated and managed for a better system. In more developed digital transformation projects, common optimisation goals include:
- Improving reliability: Preventive maintenance can be implemented to improve the reliability of equipment, as well as anticipate and prevent anomalies from occurring.
- Optimising overall equipment effectiveness (OEE): Identifying idle equipment in the factory to improve OEE.
Reducing cost: Let’s take the example of a water pump. Instead of starting the pump whenever necessary, it is adjusted to pump water during those periods when high demand forecasts overlap with the seasonal electricity discounts to reduce costs.
Thus, the chance of bringing forth an innovative and competitive business model transformation depends largely on users having the ability to reduce costs and increase efficiency through analysis and optimisation.
The impact of data: from the edge to the core
Traditionally, OT data refers to data that exists in sensors, meters, controllers or monitoring platforms, such as SCADA. Most of the data is discarded if the operating system is running smoothly.
OT data, however, is about to be taken to a new level. The biggest difference between the OT data of the past and the OT data of the digital transformation era is the latter has been shaped into an integral part of an enterprise. Compared to its old self, OT data is now a digital asset meant to be used by other digital systems and can generate higher values through various interactive analyses and integration. Thus OT data’s influence has increased exponentially by playing an integral part in business decision-making. With the growth in its importance, simply maintaining the stability of OT data is no longer enough. For enterprises to step into digital transformation smoothly, high-quality data is now required. But what is considered ‘high-quality’ for OT data?
Quantitative changes to OT data
In addition to qualitative changes, OT data has also undergone quantitative changes. We can consider these changes from four different dimensions: variety, velocity, volume and veracity.
Variety: requiring deeper and wider data
The traditional control system already relies on a large amount of OT data to operate. Operational data, which is used to indicate the operating status of a system at a certain time, can be simple, such as the position of a water gate and daily oil production, or it can be complex, such as production recipes, processes, etc. However, digital transformation requires more diverse data on two levels:
- Deeper data: Take the predictive maintenance of CNC machines as an example. To accurately predict when certain machines would require maintenance or provide pre-emptive upkeep, more in-depth information is required. In addition to the basic machine operating status, the vibration frequency and current value of the motor must also be collected for analysis. Therefore, in this case, additional vibration sensors or meter measuring instruments are needed to obtain the ‘deeper’ data.
Wider data: ‘Wider’ data is basically cross-spectrum data that needs to be analysed with other systems or even third-party data. For example, to ensure overall power grid stability, traditional power companies need the power output estimations from renewable energy companies. To make these estimations, renewable energy companies have begun to incorporate weather forecasts as an important reference. Hence, to garner information for the power grid that is both useful and comprehensible, data needs to be pulled from a wide spectrum.
The foundation of digital transformation is built on data that is not just found at the core of a control system but also ‘deeper’ in critical equipment of the same system and ‘wider’ from other systems.
Velocity: requiring circular feedback of data
The main reason traditional automation systems focus on real-time data is to gain better control of the devices. In digital transformation, ‘real time’ refers to the promptness in displaying, analysing and feeding back OT data.
By combining the capabilities of big data processing and edge computing technology with faster network transmission, a large amount of OT data can be converted into a format that allows for real-time or near-real-time streaming. This creates a continuous and accelerating OT data cycle, which starts from the equipment and flows continuously to the IT system for analysis. The analytic results are then immediately fed back to optimise the operating efficiency of the OT equipment. Thus, an effective ‘circle of life’ for OT data is born.
Volume: requiring reliable networks to stream more diverse data
A large-scale automation system (for example, the DCS of an oil refinery) could already process hundreds of thousands of data inputs per second in Industry 3.0. However, once the machines stopped working, the data lost its value.
Digital transformation makes sure no data is left behind. By obtaining large amounts of data, digital transformation seeks the meaning beyond the surface value, and the potential influences between a wide variety of data, so that it will keep working and create value, even when machines are not. A network that can easily transmit a large amount of OT data is needed in order for each system to obtain and share each other’s OT data.
However, providing an OT transmission network that offers stable and uninterrupted transmission of massive volumes of OT data is a challenge still waiting to be resolved.
Veracity: requiring the security of data and networks
Digital transformation is a data-driven movement, thus elevating the importance of the accuracy and security of data. OT data may include information regarding special production processes or even operational details of the monitoring equipment of key infrastructure or manufacturing facilities. If this important information is incorrectly or maliciously tampered with, it could cause immeasurable loss.
The protection of the basic data transmission network between IT and OT is critical. Connecting OT equipment with IT systems for data transmission normally results in a much more complicated network that is vulnerable to cyber attack at many entry points. Ensuring the security of OT data is a critical lesson every company must prepare for prior to entering the digital transformation arena.
Dealing with the qualitative and quantitative changes of OT data
The qualitative changes of OT data brought forth by the digital transformation are prompting organisations to set new goals. Subsequently, enterprises must now consider how to make corresponding changes. Most companies that have successfully pushed through transformation have adopted the following strategies:
- Set clear long-term goals for the organisation: Clear, long-term goals ensure that everyone is on board before each department sets its own priorities, so that everyone can gradually move cohesively towards the finish line.
- Set KPIs to help with cross-departmental integration: Joint cross-departmental projects break down the barriers between departments and help employees at different organisational levels understand the benefits of digitalisation and cross-departmental integration.
Start small: Test run with a small project. Small projects often reflect what works and what doesn’t within an organisation relatively quickly.
When facing the four quantitative changes in OT data (variety, velocity, volume and veracity), enterprises must adapt the attitude of continuously learning and embracing digital technology. This will help you transition smoothly into digital transformation.
Facing challenges from the so-called ‘variety’ changes, consider adding two OT data-related skills:
- Analog/digital conversion: To obtain more in-depth operating data from key equipment, especially ones without digital format conversion capabilities, consider installing new industrial-grade sensors, supplemented by remote I/O devices with analog/digital conversion functions.
- OT equipment communication protocol conversion: Since different equipment or OT systems use different data communication protocols, cross-schematic OT data acquisition can be a challenge. In these cases, consider using industrial protocol gateways and industrial IIoT gateways to convert the data hidden in different industrial equipment, controllers and HMIs into a single data format for transmission and analysis.
Facing the challenges from the so-called ‘velocity’ changes, focus on establishing a fully automatic OT-IT data circulation channel to reduce manual interventions. OT data can be continuously added through the three major stages of the process (display, analysis and feedback). The two most common issues to consider when establishing this cycle of OT data are:
- Seamless conversion of IT/OT data streams: For OT data to be successfully analysed by the IT system, a slew of background information, such as data source, data unit, data format and collection time needs to be provided. If this data isn’t properly converted, manual intervention will be required, causing the system to lag.
- Smart edge-cloud integration: Use AI at the edge to resolve on-site issues in real time without relying on a cloud analytic system to come up with a solution.
Facing challenges from the so-called ‘volume’ changes, it is necessary to construct a high-speed and stable OT data transmission network. Two points should be noted when building this network:
Use data transmission technology with high bandwidth and a backup mechanism: Technologies such as 10 Gigabit ultra-high-bandwidth industrial Ethernet, time-sensitive networking (TSN), Wi-Fi 6 or industrial 5G, and other new-generation wired/wireless communication devices are recommended.
Manage the volume and flow of data: When dealing with the flow of large volumes of data, a unified, cross-unit OT data management platform or visualisation tool should be established to systematically store and manage the data. This will enable different departments to meet each other’s data application requirements.
Facing challenges from the so-called ‘veracity’ changes, it’s important to first understand that there is no perfect solution for cybersecurity. Nothing is 100% secure. The only thing to do is to effectively reduce risks through good management. The following three aspects need your serious consideration:
Security comes from design: the strength of a system’s security is predetermined at conception. Therefore, it is necessary to pre-emptively think about the situations where cyber attacks may occur, and when designing or updating a system, it is crucial to also incorporate the corresponding cybersecurity protocols.
Besides managing the security of the company’s own system, many cybersecurity incidents are caused by the products or services provided by third-party suppliers. These suppliers could create vulnerabilities within the company’s system by accident (for example, by providing computers containing ransomware).
Improving staff cybersecurity awareness and crisis management ability is also important. It is necessary to provide appropriate cybersecurity training for IT and OT maintenance personnel to improve awareness and avoid being the source of cybersecurity vulnerabilities.
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