Data Integrity: Building Trust in Tomorrow's Power Grid
Our power grid is sending us an SOS. From Texas freezing in the dark to California's rolling blackouts, we're watching decades-old infrastructure buckle under 21st-century demands. While we push to modernize with AI-powered controls and smart grid technologies, there's an invisible element that can make or break our success: data integrity.
"Data integrity is about making things work the way people expect them to,” says Keyfive CTO Daniel Barnett, an expert in data architecture and distributed energy resource management. "When you flip the light switch, you expect the lights to come on. When they don’t, it erodes trust in that system.”
But what are the foundations of this sort of guarantee? How do organizations balance data integrity while optimizing system resources and operational efficiency? And what role will data integrity play in securing a resilient energy future? Barnett’s insights on these critical questions are captured below.
What is Data Integrity?
At its core, data integrity ensures every measurement and every decision in an energy system is reliable and from a trustworthy source. It's the assurance that when you collect data from your energy systems, it remains accurate and untampered with throughout its lifecycle, from collection to storage and analysis.
"The best data integrity systems are invisible - they simply work as intended,” Barnett says. “You press a button; your phone boots up. You check your energy management dashboard; the numbers are accurate.”
However, achieving this level of authenticity requires careful attention to three foundational principles in data management, Barnett explains: reliability, accuracy, and security.
1.) Data Reliability
Data integrity is about maintaining continuous, reliable data flow. Imagine your solar farm sending temperature readings every minute. Data integrity ensures that if your network goes down temporarily, that data isn't lost - it's stored locally and transmitted when connection resumes.
“This expectation is table stakes,” Barnett says. “If you're disconnected from network because you have a spotty signal, you expect that the data you recorded and collected will still be there when the connection resumes.”
To achieve this across energy systems, you must consider:
Consistent data sampling at specific intervals
Full fidelity of important information
High-reliability data store
Back-up mechanisms for network interruptions
2.) Data Accuracy
Data integrity requires end-to-end system accuracy, from initial sensor readings to final storage and analysis. Compromised accuracy at any point, such as a faulty sensor reading, can cascade into unreliable operations and bad decision-making across entire energy systems.
To illustrate, Barnett points to the challenge of clock skew: “Most uncorrected quartz clocks drift several seconds per day. Over time, you can be hours off, days off. This could lead to serious operational issues.”
When different devices operate on different time references, you lose the ability to accurately correlate events or ensure coordinated responses across the system. This is especially problematic in advanced control scenarios, where commands aren’t executed immediately but need to be precisely coordinated across multiple devices.
Higher sampling rates and greater data precision approaches provide the most accurate insights, Barnett explains, especially in energy applications, where timing is critical for fault detection and demand response. However, these practices also demand more storage space and processing power. So, system developers are tasked with the delicate balance of sampling frequently enough to detect critical events while saving costs and resources.
“There’s an art to choosing data structures that developers must exercise,” Barnett says. “Knowing the nature of the physical system — knowing your data — is part of complete system design.”
For example, thermal systems change more slowly than electronic systems, so sampling frequency can be tuned to save resources without sacrificing accuracy,” he explains. Trade-offs like these are key to creating an accurate and reliable software system.
3.) Data Security
Security means no one can access or tamper with your data. This requires multiple layers of verification and protection throughout the data pipeline, managed through a comprehensive protocol stack that includes MQTT and TCP/IP.
Advanced features like PKI (Public Key Infrastructure) can also be implemented for higher security needs, Barnett explains. These tools sign and verify data throughout its lifecycle, establishing non-repudiation by making any alterations or tampering mathematically impossible to hide.
“The gotcha on that one is that you must deploy more capable hardware. So, PKI becomes computationally expensive and requires a little bit of storage complexity too.” Barnett reasons, reiterating the need to balance robust data frameworks with operational practicalities.
The Role of AI/ML in Data Integrity
AI tools are proving essential for detecting and addressing the very vulnerabilities they create in regard to our energy future. But let’s be clear about the kind of AI we are talking about, Barnett clarifies. AI as it applies to energy operations relies on sophisticated machine learning algorithms (AI/ML) and automation — not generative large language models like ChatGPT.
Instead, AI/ML employs advanced statistical analysis to detect performance patterns and anomalies, then triggers intelligent system responses, such as scheduling maintenance or firing up a generator. But in order to use this tool effectively, you have to train it with proper, trusted data and decision-making frameworks.
"You can’t just flip a switch to turn on ML and everything just works out great. You must exercise discursive judgement in knowing your data,” Barnett says.
Understanding your data's fundamental characteristics is essential for configuring AI/ML tools effectively in energy systems. For instance, knowing that power quality issues typically manifest within milliseconds while thermal changes occur over minutes allows software developers to set appropriate sampling rates and detection thresholds for AI/ML monitoring systems. This knowledge ensures AI/ML tools can distinguish between actual anomalies requiring immediate attention and normal operational variations, preventing both false alarms and missed critical events.
Looking Ahead: Making Sure Modern Systems Work
Just as any structure requires a solid foundation, energy systems need reliable data architecture at their core. This is why trusting and understanding the integrity of your data is — as Barnett says — "table stakes” for building a successful system.
Organizations racing to innovate must remember that even the most sophisticated AI controls need trusted, accurate data. While sophisticated data approaches offer powerful capabilities, organizations must balance innovation with practical considerations to ensure both integrity and efficiency. Success comes not from maximizing every capability, but from optimizing system resources through reliable, accurate, and secure data collection and processing.
This approach enables everything from basic operations to advanced features, improving monitoring, enabling coordinated responses, and supporting regulatory compliance. Ultimately, every aspect of our modernized grid depends on our ability to trust that everything is working as expected — that when we flip the switch, the light will come on.