Data has the potential to uncover new efficiencies, improve reliability, and maximize the availability of your operations. But with so much data at your fingertips, the real challenge lies in knowing how to leverage it effectively. When channeled properly, the value of data is undeniable — you can obtain insights, identify risks, track outcomes for continuous improvement and enhancement, the list goes on and on.

Gone are the days when organizations could be reactive. The goal now must be to move from reactive maintenance to predictive strategies. Data and artificial intelligence (AI) can be used to address issues in real time (or near real time) and make timely, confident decisions that drive value across the board.

This marks the final installment of our three-part reliable energy production series, dubbed Lightbulb Moments. In our first article, we explored how cyber security and operational technology can fortify your critical infrastructure against threats. Our second guided you through preparing your platforms for the future of energy production. By embracing these emerging technologies, energy and utilities companies can not only ensure reliable energy production, but they also set themselves up to weather the ups and downs of this evolving sector.

In our third installment, we delve into the transformative power of data.

It’s important to approach these topics holistically, rather than in silos. Data platforms and AI should not be deployed without examining your cyber security. As a leader, you should consider all three areas together to maximize their benefits.

And now, let’s take a closer look at how data can help enhance your energy reliability.

Energy and utilities companies are awash in data. Whether its sensor data from field equipment, operational metrics from control rooms, or external datasets like weather patterns, the sheer volume can be overwhelming. And while we often hear that “data is the new oil,” extracting and refining its value is easier said than done.

However, despite the investment in technology and infrastructure, many energy organizations find themselves struggling to harness the full potential of their data. They know there is value to be found — but how? The ones who know the answer to that are already jumping ahead of the pack.

Reactive maintenance has long been the norm in the energy industry. We wait for something to break, then we fix it. But this approach leads to unplanned downtime, higher costs, and inefficiencies that can ripple throughout your organization.

Predictive maintenance uses data to identify patterns and forecast when equipment is likely to fail. The result? Reduced downtime, extended asset life, and improved efficiency.

By using predictive analytics, for instance, your organization can anticipate potential issues in your pipelines, power grids, or wind turbines and proactively address them.

Data is only as good as its quality. Clean, accurate, and well-organized data is the foundation upon which all data-driven strategies are built. Without it, even the most sophisticated analytics and predictive models will produce flawed results.

Consider this: if your data is incomplete, outdated, siloed, or inconsistent, the insights you generate will be unreliable — leading to poor decisions and missed opportunities. This is why ensuring data quality is priority number one.

Here are some tips to help ensure clean data:

• Establish stringent data governance: Implement a governance framework that outlines how data is collected, stored, managed, and accessed.
• Invest in data cleansing tools and processes: Regularly cleanse your data to improve accuracy. Cleansing goes beyond removing duplicated and correcting inaccuracies — it involves validating and enriching the data to make sure it’s reliable and useful.
• Integrate data across systems: Make sure data from different sources is properly integrated to create a unified dataset. This can lead to better analysis, insights, and decision-making.
• Enhance accuracy through validation and enrichment: Along with cleansing, use data validation methods to verify your data, and enrich it by adding additional context or missing information.

Regardless of the quality, having the data available to those who need it, when they need it, is important. Data health scores and quality metrics can be used to better understand whether the data should be used to drive decisions or needs more scrutiny. There are multitudes of hidden, side-of-the-desk, and gated data points in organizations. These siloed and disparate data sources can be combined and may present new use cases for AI and machine learning.

Uncovering these and using them to enrich existing sources can drastically improve the quality of results and lead to better decisions.

The shift to predictive strategies isn’t just theoretical — it’s already delivering meaningful results. Here are some real-life examples of how a leading Canadian energy company implemented projects, with our support, and successfully leveraged their data.

By integrating sensor and physical dig data, this solution improved threat detection for issues like corrosion and cracks within pipelines. Machine-learning models matched sensor data with dig data, providing analysts with confidence scores, insights into asset health, and reduced costly physical digs.

The data was visualized using a front-end tool that mapped the alignment of sensor and dig data, allowing for detailed analysis. These visual dashboards track savings in hours, vendor costs, and digs, uncovering insights and highlighting the value of the solution to the client.

Meter and sensor failures are a costly problem that can shut down operations. Using advanced machine-learning models and health scores, our client was able to predict failures and anomalies and detect communication issues in liquid pipeline metering systems. Operating with and combining large volumes of streaming and batch data sources was done at cloud scale.

This project used innovative Microsoft Azure cloud solutions — including Azure Data Explorer (ADX/Kusto), Azure SQL, Event Hub, Logic Apps, Databricks and Azure Machine Learning — to generate health scores and identify anomalies in near real time. Power BI dashboards and real-time alerts enabled teams to do proactive maintenance, lowered costs, and prevented costly interruptions in operations.

By calculating and generating predictive maintenance scores, this solution integrates with other systems to detect failures in sensors. With help from our innovative solutions — like Azure Databricks, Azure SQL, Power BI, and Azure Data Factory — the client gained enhanced visibility into asset health, enabling timely intervention and reducing unexpected downtime.

On-demand results from machine learning (ML) optimization models and custom-built tools helped pipeline operators optimize operational configurations and reduced operating costs — all without sacrificing throughput or safety.

This approach ensures consistent commodity flow and lowers operational expenses, while simplifying operations across a complex network.

In the renewables energy sector, this anomaly detection platform used object detection and machine learning to analyze drone imagery for wind turbine inspections. The solution — which included Azure Kubernetes Services, GPU compute and Deep Learning for processing the imagery, and Azure SQL and Power BI Dashboard for presenting results — enabled our client to detect defects early and predicted the remaining useful life of assets. This helped prevent failures and optimized maintenance schedules.

By applying deep learning for aerial patrols, and training object detection models across large volumes of aerial and satellite imagery, our client improved security and visibility across the footprint of their assets. Using advanced imaging technologies, such as LiDAR, and custom cameras integrated with purpose-built hardware and software, the client could identify known and unknown objects, detect anomalies and threats, manage terrain differentials and shifting terrain, actively visualize their assets, and improve overall safety and security.

Through digital twins — created for pipeline terminals using a Windows desktop application, AI, machine learning, and the suite of Azure services — our client improved their asset management and operational efficiency. This included using deep learning object detection models to label assets and integrate data points with digital diagrams, resulting in streamlined operations and enhanced terminal utilization.

Using an AI-driven anomaly detection and monitoring tool, the client’s pipeline operators can detect anomalies in operations which cause high power use during demand window periods. Operators are called with an automated voice message when an anomaly and power peak are detected, which gives them insights into efficient operations to conserve energy costs.

This solution avoids operational costs by optimizing energy demand during peak pricing times and provides additional visibility into energy use and anomalies in operations. This solution leverages sensor data, Azure Databricks Delta Live Tables (DLT), MLFlow, and other Azure batch and streaming services. The tool’s AI pipelines provide early warnings, enabling more informed decision-making and cost management.

These real-world projects show how sophisticated technologies like machine learning, AI, and cloud platforms are changing operations across the energy sector. Each example highlights how organizations can move beyond longstanding approaches, driving new value and efficiencies. By integrating these advanced technologies, your company is not only improving its operations — it helps make sure operations remains safe and uninterrupted.