The utilities sector is undergoing enormous change due to the advancements in Industry 4.0. The use of new IoT devices, sensors, and cameras is generating massive amounts of data. In March 2023, there were an estimated 41.8 billion connected devices worldwide, and IDC predicted that number would grow to 55.7 billion by 2025. All of these devices are capturing and transmitting valuable data.
But do companies in the utility sector maximize the value of all that data? Many don’t. More data typically leads to siloed, distributed, redundant data that is difficult to integrate with existing systems.
The good news is big data has a silent collaborator: AI. AI utility solutions can improve everything from capturing, storing, cleaning, and analyzing it. That means utility companies have help in their quest to use their big data for big benefits.
Big data and the power of data analytics are reshaping the energy and utilities industry. In fact, the market for data analytics within the utilities sector is expected to increase from USD 2 billion in 2020 to USD 4.3 billion by 2025. While the benefits of big data are infinite, four areas where utilities can start leveraging their data for big gains are enhanced efficiency, improved decision-making, customer satisfaction, and driving sustainability.
Enhanced efficiency is the low-hanging fruit when getting started with big data benefits. Predictive maintenance makes service crews more efficient and can save money by avoiding major repairs. Further, analyzing big data can provide powerful insights into usage trends (seasonal, monthly, weekly, and even daily) to guide intelligent load-balancing decisions.
Real-time information is critical for utility workers to do their jobs efficiently. Sensor data is invaluable for improving equipment maintenance, and data mining and pattern recognition from sensor data can lead to fast responses and reduced service interruptions.
For example, one of the UK’s largest private water utilities wanted to reduce risk and service interruptions. They worked with Sand Technologies to leverage big data, IoT and analytics to build a dashboard that gives them near real-time data to make more informed decisions. The ability to predict and prevent water outages yielded £7M in savings for the water utility and averted service disruption for their customers.
Enhanced efficiency is the low-hanging fruit when getting started with big data benefits. Predictive maintenance makes service crews more efficient and can save money by avoiding major repairs.
Data analytics can improve customer satisfaction in two ways. By analyzing the interaction history of service calls, AI can find key areas to improve customer service. Another use that is growing in popularity is insights into consumption patterns. By analyzing customer energy usage, utility companies can recommend how customers can lower their monthly bills and help conserve energy.
The EIA is projecting that electricity demand will continue to increase, possibly reaching a third to three-quarters higher demand by 2050. The rise in demand will drive the adoption of more renewables. Big data and analysis can help the utility sector comply with sustainability reporting requirements and transition to renewables at the right time.
Sustainability will require information about the health and state of the network and the investments the companies have to make. Some utilities are already making progress in this area. Sand Technologies worked with one UK utility to provide the data analysis mechanism to deliver new value by helping them achieve their sustainability goals.
The energy sector generates massive data sets from connected devices throughout its infrastructure. The data is in different formats, such as video images and measurements.
The challenge for utilities is to collect all data, efficiently manage it, and build a team of data experts who know how to leverage it.
Utility companies have a vast network of devices connected to in-house and in-the-field equipment. This scattered data in multiple source systems can be low-quality, inaccessible, siloed, redundant, distributed, cloudy, and, as previously mentioned, in various formats.
The nature of a utility network leads to data silos, data security concerns, storage and scalability issues, and challenges integrating with existing systems. The fragmentation includes disjointed communication with field workers, data from their handheld devices, and problems integrating new assets with legacy systems.
The rise of AI’s power and performance has brought many solutions to the market. A recent IBM study found that 74% of utility companies have implemented or are exploring using AI which excels in fast and accurate data collection, analysis and management. In addition to this, rather than building custom solutions, utilities are also leveraging existing AI-powered utility solutions to reduce the time-to-value.
Unfortunately, successfully leveraging these new AI tools requires a level of expertise that many utilities lack. Like many other industries, utilities must find upskilling and reskilling programs to combat the global data and AI talent gap. Adding these valuable skills in-house will enable utilities to bridge the gap between information and action.
Utility companies have a vast network of devices connected to in-house and in-the-field equipment, leading to data that is low-quality, inaccessible, siloed, redundant, and in various formats.
Utility companies can start leveraging their data by focusing on four essential aspects that clear the path to big benefits from data analysis.
Integrated data management platforms provide an organization with a single source of truth. These platforms collect and organize data from many sources into centralized data storage, integrate and cleanse the data, and ensure accessibility and consistency from a cloud-based platform. Getting all data to feed into one source is critical; it provides strategic intelligence and valuable insights.
Clear policies and procedures guarantee data quality, access controls, and security protocols. A company’s data governance is the rules of the road that dictate data collection, organization and storage. The governance framework should include data stewardship, quality, security, privacy and management.
Due to workforce changes, finding new AI-qualified candidates for the utility sector will become more challenging. The best strategy is to seek effective upskilling and reskilling programs to bring existing employees up to speed with the skills needed for the industry’s changes. A valuable benefit of this strategy is keeping industry knowledge in-house.
Implementing data collection standards across all systems facilitates easier integration and data analysis. In utilities, data comes from many different sources. Consider the unique aspects of each data source. Data must be consistent to allow interchangeability, compatibility, and commonality.
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