How Operational Data and Digital Twins Drive Business Reliability and Performance
Over the past 15 years, a confluence of more affordable devices, expanded cloud storage, smartphones, and ubiquitous connectivity has driven an Internet of Things (IoT) boom that is projected to surpass 40 billion devices worldwide by 2030. This explosion of connected devices has set the stage for previously unattainable levels of optimization and reliability for OEMs in the automotive, aerospace, industrial machinery, and energy sectors.
Organizations today collect more operational data than ever. Yet the anticipated gains in productivity and performance have largely failed to materialize. The reason for the shortcoming is clear: as much as 90% of all sensor data collected from IoT devices is never used.
From a return-on-investment perspective, this represents a fundamental disconnect. Companies invest billions of dollars into collecting and storing data, but without the ability to translate it into actionable insight, that data returns little to no operational value.
This idle approach to data utilization represents a critical gap in operational intelligence. Working within this restrictive framework severely limits system productivity and efficiency: teams remain reactive, decisions are made without operational context, and critical insights stay buried in data files, never to be discovered.
Keyfive CEO Jay Douglas shares more about the company’s Reliability Intelligence platform at Capital Factory’s First Look event, during SXSW 2026.
The stark truth for many companies is, for all of the investment they’ve made in sophisticated IoT devices to measure, collect, and store massive volumes of information, operational data is still too often treated as a operational byproduct rather than a driver of performance.
What many organizations fail to recognize is that this very same data, when properly structured, contextualized, and analyzed, becomes something far more valuable: a proprietary asset. One that can deliver precise insight into asset performance, emerging risks, and system optimization opportunities, transforming raw information into actionable intelligence and measurable improvements in reliability.
In practice, this means enabling teams to identify patterns, detect issues as they develop, and anticipate failures before they occur. With this level of visibility, decisions are no longer reactive or based on incomplete information, but grounded in real-world performance data that reduces downtime, avoids costly failures, and continuously optimizes operational efficiency.
The technological capability already exists to accomplish this. The challenge now lies in knowing how to unlock the value of operational data and apply it in a way that drives quantifiable business outcomes.
Your Operational Data Could Be Your Most Valuable Asset
IoT devices generate zettabytes of operational data every year. In theory, this data should serve as the foundation for improving reliability and optimizing performance over time.
In practice, however, most organizations use this data only retrospectively. Operational signals are often fragmented across systems, locked in dashboards, or analyzed after the fact — leaving teams to reactively respond to issues instead of preventing them. Many organizations also lack the expertise or context to turn raw data into actionable insights that guide forward-looking decisions.
Leading organizations take a different approach. They treat operational data as a strategic asset, continuously refined, contextualized, and integrated into decision-making. Over time, this approach compounds: insights improve, decisions become more precise, and reliability and performance outcomes strengthen. This turns data into one of the organization’s most valuable intellectual assets as it is the foundation on which an optimized system is built, not just parts of the system that are optimized in isolation.
Furthermore, an emerging trend among leading organizations is using operational data to inform the design of next‑generation machines, creating a feedback loop that turns field performance into future innovation.
How Most Companies Limit Their Asset Performance Visibility and Reliability
With respect to operational intelligence, recognizing the value of operational data is one thing, but extracting value from it is another. The core challenge lies in building a robust model that can interpret and predict system behavior, not just collect and display data.
This is where most organizations fall short. Despite collecting large volumes of data, it is often viewed in isolation in the form of dashboards, alerts, and uncorrelated metrics and without the context needed to understand how systems actually function. What’s missing is the system-level connective layer that links cause and effect.
As a result, teams are limited to observing what is happening at the asset level, without the knowledge of why it’s happening or what will happen next. Decisions are made with incomplete insight, limiting organizations into reactive positions instead of one with tangible foresight.
To achieve substantial improvements in both asset performance visibility and reliability, organizations must move beyond fragmented data analysis and develop a high-fidelity, real-time model of their assets as part of an integrated system. With this level of oversight, operational data can be continuously interpreted, performance patterns can be connected, and predictive, data-driven decisions can be made confidently.
From Data to Decisions: Building Reliability Intelligence with Digital Twins
Digital twins are how organizations can implement their operational data to achieve a system-level understanding of asset performance.
In lieu of isolated data sets, digital twin platforms create dynamic, system-level simulations of real-world physical assets. These models are continuously iterating via real-time operational data streams. These models establish the context that traditional monitoring systems lack by connecting signals and patterns across system components and processes.
With this foundation in place, the immense potential of operational data can be realized as reliability intelligence. Instead of simply reporting what has happened, organizations gain the ability to understand why it is happening and anticipate what will happen next. This shift enables teams to move beyond observation and will allow them to identify risks earlier, continuously optimize performance, and make decisions with greater precision.
By modeling how the system performs under varying conditions, organizations can evaluate trade-offs, simulate scenarios, and find opportunities for optimization that would otherwise remain undiscovered. Over time, this creates a more complete and actionable understanding of asset performance across the entire operation.
Keyfive makes this possible by enabling organizations to build and integrate these models into their current organizational structure. By merging disparate data sources into a unified, high-fidelity representation of asset behavior, they provide a scalable path from raw data to predictive, system-level insight. This paves the way for organizations to smoothly transition from obsolete reactive maintenance strategies to proactive reliability intelligence across their entire operation.