Digital Twins and Advanced Analytics: Who Actually Builds and Maintains Them?
- June 2, 2026
Digital twins have evolved from a futuristic Industry 4.0 concept into a practical business tool for manufacturers, utilities, logistics providers, and infrastructure operators. Powered by advanced analytics, IoT data, AI, and cloud platforms, digital twins help organizations simulate operations, predict failures, optimize assets, and improve decision-making in real time.
KEY TAKEAWAYS
- Operations and engineering leaders are often the starting point for digital twins because they understand the physical assets and processes being modeled.
- Digital twins require enormous amounts of connected data, which places IT teams at the center of implementation.
- Data scientists and analytics teams build predictive maintenance algorithms, machine learning models, simulation logic, optimization models, and forecasting engines.
- Cross-functional governance is key to preventing digital twins from becoming siloed experiments.
- Poor data quality remains one of the largest barriers to successful advanced analytics programs.
The market momentum reflects this shift, with the global digital twin market projected to grow from roughly $35.8 billion in 2025 to more than $328 billion by 2033. But, while the technology receives significant attention, one question is often overlooked: Who actually builds and maintains digital twins and advanced analytics ecosystems?
The answer is more complex than many organizations expect. Successful digital twin initiatives are rarely owned by a single department. Instead, they require collaboration across engineering, operations, IT, data science, and business leadership.
WHAT IS A DIGITAL TWIN?
A common misconception is that a digital twin is simply a visual replica of a machine or facility. In reality, modern digital twins combine:
- Real-time operational data from sensors and industrial equipment,
- Advanced analytics and machine learning models,
- Cloud or edge computing infrastructure,
- Simulation engines,
- Business process logic, and
- Visualization and reporting tools.
The value comes from connecting these components into a living operational model that evolves continuously. For example, for manufacturers, this can mean predicting equipment failures before downtime occurs, whereas for supply chain organizations, it may involve simulating disruptions or testing production scenarios.
WHO BUILDS DIGITAL TWINS?
Operations and Engineering Teams
Operations and engineering leaders are often the starting point for digital twins because they understand the physical assets and processes being modeled. These teams define equipment behavior, process workflows, failure scenarios, operational KPIs, and maintenance requirements. Without operational expertise, even the most advanced analytics model lacks context. Engineers help determine what data matters, which thresholds indicate risk, and how recommendations should translate into real-world actions.
In many organizations, operational technology (OT) engineers also manage the industrial control systems and IoT infrastructure feeding the twin.
IT and Enterprise Architecture Teams
Digital twins require enormous amounts of connected data, which places IT teams at the center of implementation. These teams are typically responsible for cloud infrastructure, cybersecurity, data integration, API management, enterprise architecture, and system interoperability. This becomes especially important when organizations attempt to integrate various platforms into a unified environment.
As digital twin ecosystems mature, scalability becomes a major concern. The infrastructure supporting one production line often needs to scale across multiple locations or business units.
Data Scientists and Analytics Teams
Advanced analytics is what transforms a digital twin from a static model into a predictive decision-making tool. Data scientists and analytics teams build predictive maintenance algorithms, machine learning models, simulation logic, optimization models, and forecasting engines.
These capabilities are becoming increasingly valuable across manufacturing environments. In fact, research shows predictive maintenance remains one of the leading AI use cases in manufacturing, with more than 30% of medium and large manufacturers actively using predictive maintenance technologies. Moreover, among those organizations, over 85% reported a significant decrease in unplanned downtime.
WHO MAINTAINS DIGITAL TWINS LONG-TERM?
Building a digital twin is only the beginning. Maintenance is where many organizations struggle.
Unlike traditional software implementations, digital twins continuously evolve alongside physical operations. Equipment changes, production lines shift, sensors fail, and business priorities change. If models are not updated regularly, performance degrades quickly.
Shared Ownership Is Essential
The most successful organizations establish shared ownership models across teams. Typically, engineering teams maintain operational accuracy, IT teams maintain infrastructure and integrations, analytics teams retrain and optimize models, and business leaders prioritize use cases and ROI. This cross-functional governance prevents digital twins from becoming siloed experiments.
Data Quality Teams Play a Growing Role
Poor data quality remains one of the largest barriers to successful advanced analytics programs. After all, a digital twin is only as reliable as the data feeding it. Sensor drift, inconsistent asset naming conventions, incomplete maintenance records, and disconnected systems can all undermine outcomes.
As a result, many organizations are creating dedicated industrial data governance teams focused on master data management, data standardization, IoT data validation, analytics monitoring, and more. These roles are becoming especially important as organizations scale AI and advanced analytics initiatives enterprise wide.
MOVING FORWARD
Digital twins and advanced analytics initiatives are true operational transformations. The organizations seeing the greatest value are not simply investing in visualization tools or AI platforms. They are building cross-functional ecosystems where operations, IT, analytics, and leadership collaborate continuously.
As advanced analytics adoption accelerates, digital twins will increasingly become part of everyday industrial operations.