The Ultimate Guide To Digital Twins in Industry

If you're leading data and analytics in a large organization, you've likely heard about digital twins. But beyond the buzzwords, what are they actually doing for companies like yours? More importantly, how do they fit into your existing data infrastructure and strategy?

Peter van Schalkwyk, CEO of XMPro and co-chair of the Natural Resources working group for the Digital Twin Consortium, cuts through the noise with a practical definition: a digital twin is a synchronized instance of a digital model that represents a physical entity throughout its life cycle. But here's what matters for your organization—it's designed to solve specific business problems, from reducing complexity in increasingly complex systems to enabling better decisions through improved situational awareness.

This isn't about creating another data silo. It's about building a dynamic connection between your physical operations and your data strategy that delivers measurable business value.

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Understanding the Model-Instance Architecture

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Before investing in digital twin technology, you need to understand how they actually work. The architecture follows a template-instance pattern that will feel familiar if you've worked with object-oriented systems.

Think of it this way: Tesla has one Model 3 template that describes all the components, specifications, and data points for that vehicle type. But they have millions of unique instances—each representing an individual car with its own VIN, color, telemetry data, and maintenance history.

Key insights for your data architecture:

• Your digital twin strategy requires both a model layer (templates that describe asset types) and an instance layer (unique representations of individual assets)
• Each instance maintains a unique identifier but inherits its structure from the parent model
• This approach scales efficiently—whether you're managing one building or a million pieces of equipment
• The model defines how you store, access, and interact with the data consistently across all instances

This matters because it affects how you design your data lakes, how you structure your metadata management, and how you maintain governance at scale. You're not creating custom schemas for every asset; you're managing templates that can be instantiated across your entire operation.

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Three Types of Digital Twins: Matching Capability to Business Need

Not all digital twins are created equal, and understanding the differences will help you prioritize where to invest. Van Schalkwyk identifies three distinct types based on their capability level:

Status Twins: The FoundationThese provide one-way visibility into what's happening right now. They collect real-time data from physical assets and present current operating conditions. Think of this as your starting point—you're building situational awareness without automation.

Operational Twins: Adding IntelligenceThis is where business value accelerates. Operational twins don't just monitor; they interact with other systems. When conditions change, they can automatically generate work orders, update supply chain systems, or trigger responses across your technology stack. They become active participants in your operations, not passive observers.

Simulation Twins: Predictive Decision-MakingThese twins run scenarios before you take action. They receive real-time data, pass it through simulation models, evaluate multiple outcomes, and help you make informed decisions. This is where you're getting into predictive maintenance, what-if analysis, and optimization.

The practical implication: You don't need to start with the most complex version. Many organizations begin with status twins to prove value and build organizational capability, then progress to operational and simulation twins as use cases mature and trust builds.

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The Technical Infrastructure: From Data Storage to Real-Time Processing

Here's where it gets practical for data architects. Digital twins are not a single massive database file. They're a distributed architecture that needs to integrate with your existing systems while adding new capabilities.

Storage architecture evolution:

• Traditional implementations use relational databases for storing telemetry data, master data, and maintenance records
• Graph databases are emerging as the preferred solution for managing relationships between twins and understanding complex asset hierarchies
• Some implementations use XML or JSON-based structures (following standards like Asset Administration Shell or DTDL)
• The key is that the twin itself acts as an intelligent layer that knows where different data sources exist across your systems

Integration patterns that work:Your digital twin needs to pull data from IoT sensors, SCADA systems, enterprise resource planning platforms, and numerous other sources. Rather than forcing data into a new structure, the twin creates a virtual layer that connects to existing systems of record. This means you're not ripping out and replacing your current data infrastructure—you're adding an intelligent orchestration layer on top.

Real-time processing requirements:Digital twins process events in real time, which means your data architecture needs to support streaming data pipelines. You need pub-sub messaging systems that can handle high-frequency sensor data while maintaining the governance and quality standards you've established for your broader data platform.

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Practical Takeaways for Manufacturing Leaders

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Based on van Schalkwyk's insights and his work with Fortune 10 companies, here's what you should focus on:

Start with use case clarity: Don't build a digital twin because it sounds innovative. Identify specific business problems where real-time data and automated response will drive measurable value—reducing downtime, optimizing operations, or improving safety outcomes.

Build on your existing architecture: Digital twins should integrate with your current data infrastructure, not replace it. Focus on creating that intelligent orchestration layer that connects to your existing systems of record.

Prioritize interoperability: The Digital Twin Consortium exists because vendors have 50 different definitions of what a digital twin is. Work with standards bodies and industry groups to ensure what you build can integrate with partners and suppliers.

Enable your engineers: The most successful implementations use low-code platforms that let domain experts—engineers who understand the equipment and processes—build and maintain their own digital twins. This follows the data democratization principle that should already be part of your strategy.

Think in templates: Invest time in building robust models (templates) for your asset types. Once those are right, scaling to thousands or millions of instances becomes straightforward.

Governance from day one: Digital twins generate and use data that feeds into business decisions. Your data governance framework needs to extend to cover how twins access data, make decisions, and trigger actions across systems.

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Conclusion

Digital twins represent a significant evolution in how enterprises use data to manage physical operations. For data leaders, they're not a separate initiative but rather an advanced application of your existing data strategy—bringing together real-time streaming, machine learning, governance, and operational integration.

The opportunity is clear: better situational awareness, faster decision-making, and automated responses that can reduce costs while increasing reliability. But success requires approaching digital twins with the same rigor you apply to any major data platform investment—clear use cases, solid architecture, proper governance, and a focus on enabling the people who will ultimately create and use these systems.

The question isn't whether digital twins will become part of your data landscape. The question is whether you'll shape that integration strategically or react to it as vendors and business units pursue disconnected initiatives.

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