For years, energy and utility enterprises have seen the writing on the wall, anticipating the need for major business transformations. The growing outcry for global carbon neutrality, the convergence of information and operational technologies, and increasing customer demand for enhanced, connected experiences have pushed leaders to develop and drive transformational strategies.
While the first leg of the race may be done—identifying use cases and launching proofs of concept, most energy and utility companies still have a marathon ahead of them before they can truly achieve scaled intelligent operations.
More often than not, intelligent operation initiatives don’t scale because organizations cannot properly enable enterprise-wide data interoperability. Data—the driving factor behind digitally driven operations like predictive asset management, production optimization and automation—is “trapped” across various sources. IT and OT teams alike cannot access, manage, or merge their data.
To overcome this, energy and utilities companies, their partners and suppliers, and even technology providers must open and standardize their data frameworks to drive interoperability. Here are four enablers to help organizations get started:
Data Acquisition and Storage
To progress intelligent solutions, like smart grids, or services, like vegetation management, energy and utility enterprises must be able to combine their IT and OT systems, blending the digital and physical worlds. Data acquisition is central to this as it collects real-world samples from physical hardware or conditions and translates those findings into digitized data. This is primarily done through IoT sensorization in which sensors are embedded into devices, like gyroscopes and accelerometers, or used to measure conditions, like temperature and voltage. Companies then must utilize sensor fusion, merging data from multiple sensors to reduce the amount of uncertainty that may be involved in a robot navigation motion or task. Sensor fusion helps in building a more accurate world model for the robot to navigate and behave more successfully.
However, having a strong grasp on these capabilities doesn’t mean anything if organizations cannot turn raw data into actionable insights. Like most industrial enterprises, energy and utility companies need to evolve their historian data strategy to benefit from machine learning (ML) and artificial intelligence (AI) algorithms. This can be done by leveraging an industrial AI infrastructure that helps accelerate business value from industrial data. However, IT and OT leaders alike must recognize that data historians can’t just be used to collect and process data. Data must be treated as the core of a greater industrial management strategy—one that shifts gears from mass data accumulation to more thoughtful application, integration, and mobility. Purposeful application of AI and ML are key to facilitating that evolution in the data historian’s function in an industrial organization, to tap previously undiscovered or unoptimized industrial data sets for new business value.
Standardized IT & OT Asset Hierarchy
An asset hierarchy is a logical index of all maintenance equipment, machines, and components, and how they work together. Building and understanding a facility's asset hierarchy is critical to efficiently tracking, scheduling, and identifying the root causes of failure in your equipment. This is especially significant for energy and utility plants because it provides a consistent way to identify and classify assets across the organization. Without standardization, IT and OT teams may use different names or classifications for the same assets, leading to confusion and errors when integrating data between different systems
To properly standardized IT and OT asset hierarchy, leaders must:
1. Define a common taxonomy
2. Adopt standard data models
3. Implement data governance framework
4. Use open standards
5. Invest in data integration platforms
Data Models
The crux of intelligent operations is IT/OT convergence and the crux of IT/OT convergence in many respects is data model connectivity. A data model is composed of three components: data structures, operations on data structures, and integrity constraints for operations and structures. Data model connectivity links disparate data sets and applications, including data from different identity spaces. This enables collaboration among different parties with data controls, ensuring safe and effective activation across the broader ecosystem.
Data model connectivity can be improved by adopting standard data models, using data integration platforms, implementing API-based integration, establishing data governance policies and investing in data analytics tools. This in turn will drive improved operational efficiency, better decision making, enhanced customer service, increased agility, and overall improved data quality.
Organizational Change Management
Digital capabilities aside, data interoperability among IT and OT networks will largely be driven by leaders. Organizational change management tactics will help organizations unlock the business value made possible by digital transformation. Energy and utility enterprises must prioritize the adoption and implementation of new software, platforms, and tools to increase productivity of people, processes, and management. Leaders should therefore introduce product and functionality trainings as new digital capabilities are introduced. Problem solving, creative thinking, digital skills, and collaboration are in greater need as the energy and utility sector goes digital. Updating your business process will also be critical during the market-wide rush to digitize operations. Improving business processes not only helps energy and utility companies to remain compliant with regulations by continually reassessing their processes, but it can also lead to more efficient ways of working.
All eyes are on the energy and utility sectors. Organizations are not only expected to become sustainable and smarter, but they’re expected to transform much faster than they’ve been able to in order to achieve their ESG commitments. Data interoperability will enable enterprises to scale their existing investments in intelligent operations—but that can only be done when enterprises have the proper data, digital capabilities, and business processes in place. As it stands, many energy companies and utilities have to dedicate more resources in order to pull those levers and finally achieve fully scaled data interoperability across IT and OT environments.
Michael Duffy is the intelligent operations lead for energy, utilities and chemicals at Capgemini Americas. His experience in asset management and digital transformations in production, process manufacturing, generation and transmission helps the Capgemini team realize value at scale for their customers. Michael is an avid runner and has completed a marathon on every continent; he lives in the Houston area with his wife and daughter.
