People across the energy sector have a lot to say about the value of data. However, all the data in the world isn’t worth anything if you can’t get actionable insights from it. Data analysis for the energy industry drives everything from increasing safety and lowering emissions to optimizing operational efficiency and minimizing system downtime.

And when you factor in IoT, artificial intelligence (AI), and the growing adoption of smart technology within the energy sector, there is an almost limitless amount of data available that can be used to improve reliability, reduce costs, and streamline processes.

Accurate, actionable information is critical to the energy sector’s success, but with such a massive volume of data being generated across so many different sources, how do you analyze energy data efficiently?

Data visualization in the energy industry Data visualization in the energy industry

Why Data Analytics Is Crucial for the Energy Industry

The energy industry comprises many different resources, processes, and technologies, including power generation, fuel extraction and distribution, refining, sales, and manufacturing. When you factor in all of the ways to source energy—fossil fuels, solar, wind, hydro, and nuclear—energy data analytics gets complex fast.

But harnessing the power of this data is essential to the industry as a driver of several key capabilities:

Analyze Sensor Data

The energy industry relies on networks of sensors for many different functions—from tracking energy consumption to maintaining optimal wind turbine speeds. With the right analytics tools, each of these millions of sensors can provide operators with valuable metrics and critical clues into performance and system health.

Eliminate Siloed Knowledge

Traditionally, data analytics were delivered via complex reporting that only data scientists and other technically minded stakeholders could understand and use. Today’s data analytics are based on the assumption that the person who needs the information may not be a data scientist, so reports are delivered using multidimensional visualizations that convey the data clearly to everyone.

Increase Efficiency

The energy industry is hyper-focused on improving efficiency. Providers that rely on fossil fuels want to reduce consumption of dwindling supplies. Others, such as clean energy vendors, want to streamline processes and cut emissions. Regardless of motivation, data analytics can help companies find and capitalize on efficiencies that they otherwise would miss.

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Predictive Analytics in the Energy Industry

Predictive analytics in energy organizations provide a much-needed safety net. Using predictive artificial intelligence, businesses can implement workflows and policies that proactively prevent system failures and increase security.

Predictive Maintenance

AI-driven predictive models can predict which systems are most likely to fail, so preventative maintenance can be conducted well in advance. 

Fault Detection

Predictive analytics can track component and system performance problems across multiple data sources to identify which parts are at risk of breaking down most frequently.

Reduced Downtime

Mitigating risk of system failure and mechanical breakdowns reduces downtime, which saves money, recovery effort, and your organization’s reputation.

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Data Visualization in the Energy Industry

Data visualization tools are critical in helping teams make sense of their complex analytics. Not every decision maker and stakeholder has a data science background, but they all need to understand what the data is telling them. 

Multidimensional visualizations and 3D modeling are two ways of presenting energy data that provide crucial insight, including:

Identifying Interrelationships Between Data Sets

Traditional, two-dimensional data visualizations don't give a full view of how data sets affect one another. Important data points may be obfuscated, and the relationship between data from disparate sources may not be visible.

Pinpointing the Root Cause of Chronic Problems

Data visualization allows the user to change variables and measure how those changes impact the target data. This can provide insight into which variable is causing a particular performance problem.

Proactively Preventing Performance Issues

Preventing a disruption is always better than recovering from one. Data visualization can help identify issues that might turn into bigger issues, so the maintenance team can step in before an outage occurs.

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Energy Analytics in the Era of Big Data

One of the major roles of Big Data in the energy industry is to help match supply to demand using energy usage analytics. For example, in many regions, smart metering provides energy companies with relevant data that allows them to:

Forecast Energy Consumption

Tracking consumer energy usage over time and during specific weather events allows energy providers to anticipate and plan for load fluctuations.

Optimize Power Generation

Armed with historical and real-time sensor data, energy providers can scale production up or down relative to demand.

Reduce Waste

Understanding patterns in energy usage helps mitigate over-generation of energy, which prevents unnecessary burning of fossil fuels, lowers emissions, and reduces wasted resources.

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Internet of Things in the Energy Sector

By placing sensors in power plants, on wind turbines, in oil refineries, and even in homes, IoT plays a leading role in today’s energy data analytics. IoT data has many practical use cases in the energy sector.


Power generating and monitoring equipment is widely distributed across regions, making manual monitoring and maintenance inefficient. IoT sensors allow operators to track performance and automatically make adjustments as needed, minimizing the need for human intervention.

Cost Reductions

Small efficiencies can add up to large savings when you are optimizing operations at the scale of a power plant, oil refinery, or wind farm. IoT systems collect data about the environment, energy usage, and other significant variables. Then that data can be used to adjust workflows, temperature settings, flow rates, and so on to create the most efficient operating environment possible.

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Data Powers the Energy Industry

The energy sector relies on accurate, reliable data to operate efficiently and safely. Traditional data analytics don’t have the flexibility and depth of insight the industry needs to get full visibility into data relationships and how different variables impact target functions. Oil and gas, utilities, and alternative energy providers are increasingly adopting data analytics solutions that are powered by AI and machine learning.

These solutions provide real-time insight into performance issues, automate critical alerting processes, and provide multidimensional visualizations that increase stakeholder understanding of the data, no matter how technical their background.

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