If this sounds familiar, you’re far from alone. Utilities struggle to efficiently operationalize data from around the organization into repeatable business value. It’s not for lack of trying; digital strategy is in place, and teams are being structured to meet these challenges, yet the conversion point from data into valuable information still seems to be more manual than automatic.
Put simply, data really can’t turn into information without context. In operations, data must be anchored to its appropriate place in the process – usually to an asset hierarchy, process diagram, or network. Skilled subject matter experts understand this context natively, having developed it over time. But for many employees, analysts, and data scientists, this data exists in various formats and silos across the organization, most often lacking the quality, context, and relationships that make these individual points valuable to operations. This translates into longer time for analytics and slower digital deployment cycles.
The development of new digital assets is often initiated by utility innovation programs, but the path of scaling and embedding digital assets in operations is vague. For example, there is already a well-defined operating model for planning, constructing, and maintaining utility assets such as transformers and wires. However, the operating model for digital assets in many utility organizations is yet to be defined. A clear definition of digital assets is critical to realizing their full potential and value.
Enter the digital twin. Since their inception as a concept, digital twins have been an excellent method for presenting data in context and bridging the gap between the physical and digital worlds. Most often used for asset performance management (APM) use cases, digital twins are in control rooms today for simulating what-if scenarios and for enabling remote operations.
On the surface, a digital twin of an asset seems rather straightforward from a user perspective: relevant time series data for an asset is tied to the various subcomponents and areas of interest. That data is then presented with a visual model of the asset along with health indicators based on analytical models and simulators. This can give a more comprehensive view of the asset’s current state and help engineering teams make better maintenance decisions.
Evolving the definition of a "Digital Twin"
For a greener, more reliable, more secure energy future, the utility industry must make digital twins a ubiquitous part of everyday operations. But what does this mean, and how does it look? It starts with redefining the digital twin, away from rigid, constrained, legacy concepts and towards the idea that they must be living, adaptable, scalable, and manageable. Digital twins can represent entire systems of interconnected systems in addition to assets. In this case, think
of entire industrial sites with physical properties and assets, networks of infrastructure and power flows, and even a digital twin of the enterprise. Thinking about a digital twin in this way opens new use cases around autonomous site operations, advanced predictive maintenance, streamlined planning, site-to-site performance benchmarking, and improved connection analysis, among many others.
To solve this expanded view of use cases, it may be more appropriate to think of a digital twin as the complete underlying canon of data and information that can be accessed and understood by any data or engineering user. The traditional scope of data (time series and simulators) must grow quickly to include other non-traditional data sets such as work orders, images, CAD models, etc. This thinking takes advantage of the 90% of data that still goes unused by power and utility organizations and puts it in context with new and increasingly valuable layers of granularity.
While the future opportunity to adopt and leverage digital twins for more business processes is significant, some challenges must be addressed in both the technology layer and the operating model.
Understanding the relationship between Industrial Data Operations and a well-executed digital twin strategy.
To address the challenges of perceived high cost, untrusted data, and business operations integration, a digital twin strategy requires appropriate industrialization of the underlying data management, business process innovation, and change management to drive efficiency and scale.
The underlying costs of data management and contextualization must come down through automation. This market need has delivered innovation in industrial data operations software, which aims to streamline the process by which complex, raw data gets transformed into business value. Put another way, industrial data operations enable more effective provisioning, deployment, and management of digital twins at various levels of granularity.
Operational Digital Twin
The operational digital twin provides the foundation to build tailored solutions across domains. This can include helping utilities scale solutions, reduce time-to-value, and establish trust in solutions by automating the creation of relationships across previously siloed data. AI-powered contextualization services provide the foundation to make data self-service for use in tools and applications your teams already know. This step-change in efficiency is a result of tooling and automation in the following areas:
- Data integration: where data from any source system can be accessed and provided with certain quality assurance for data modeling
- Data contextualization: where relationships between different data sets are formed in code and kept track of over time in an industrial knowledge graph.
- Application development: through tools and microservices that make data useful to a range of data consumers, including data scientists, analysts, and engineers.
- Use case management and scaling: where common data products and templates can be leveraged repeatably to scale across similar assets, sites, and analytical problems.
Where to start?
It may be overwhelming to think of the digital twin development process in its entirety but breaking it down into concrete steps and milestones makes it much more approachable. The goal is, after all, to deliver incremental value from digital transformation that snowballs into significant ROI and a step change in how work is activated and performed. Just like Google Maps puts consumer data into context and continues to get better with more features and more context, an industrial digital twin approach must likewise be additive and act as a platform for continuous improvement of utility operations.
Zero in on real business problems:
What is the right use case with a well-defined problem statement that can be traced back to real tangible value? Where is there an immediate need for new operational visibility? Where do you expect to need more in 3-5 years? Here is where it pays to think about digital twins like a product manager with both short and long-term vision.
Iterate pragmatically in the data layer:
Industrial data will never be perfect, and it doesn’t have to be. Start building your digital twin with data that is used in many use cases and solve the business problem at hand before integrating new sources and augmenting the use case. Digital is as much about continuous improvement as it is about the tech stack, with new tooling that makes it easier to grow your digital twin footprint over time.
Empower your experts:
Digital twins empower utility management, engineers, and data scientists to increase asset performance and drive process excellence. Understanding how a digital twin will impact how you plan, construct, operate and maintain assets - or a system of assets - in the future will be critical to driving a digital twin that enables operational excellence.
Authored by:
Gabe Prado, Cognite, Sr. Director of Product Marketing, Power & Utilities
Georg Baecker, Tetra Tech, Sr. Director, Utility Management Consulting Leader – North America