The global big data analytics market in the energy sector was valued at USD 7.50 billion in 2022. It is estimated to reach USD 19.31 billion by 2031, growing at a CAGR of 11.08% during the forecast period (2023–2031). Growing awareness about the benefits of smart metering and increased investment in alternative energy sources are propelling market growth. The volatility of oil prices, the depletion of fossil fuels, and emerging trends for enhanced efficiency and dependability in power transmission all contribute to the demand for big data analysis in the energy sector.
Big data analytics is the often complex process of analyzing large amounts of data to uncover useful information, such as correlations, hidden patterns, market trends, and consumer preferences, that can assist businesses in making informed business decisions. Big data analytics is a sub-category of advanced analytics that includes applications with complex statistical algorithms, predictive models, and what-if analyses powered by analytics systems.
Systems and software for big data analytics are available to organizations, enabling them to make data-driven decisions that enhance business outcomes. Possible benefits include more effective marketing, new revenue opportunities, consumer personalization, and enhanced operational efficiency. The adoption of greener power generation is facilitated entirely by big data analytics in energy services. It also enables sustainability consulting services and reduces greenhouse gas emissions by purchasing renewable energy. Moreover, big data analytics services aim to resolve complex business issues and make prudent investment choices.
The increasing adoption of digitization systems in the energy industry has increased the rate of data production. The big energy data sources can be divided into electric utility data and supplementary data. Electric utility data consists of enterprise data and information a utility's smart grid reveals. All additional data sources useful for big data applications are called supplemental data.
Data generation has been continuously experiencing an upward trend. For instance, according to OmniSci, there are over 135 million utility smart meters in the United States, generating up to 400 megabytes per year and 54 petabytes of new utility data annually. Power utility companies combine this big data with billions more rows of additional IoT and smart sensor data. Such factors drive market growth.
The volatility in oil prices results in high expenditures on energy-related projects, which creates a major demand for big data analytics. Similarly, the requirement for quality information is increasing due to the factor above, which is likely to boost market growth. In practice, crude oil prediction is always a challenging task. One reason is that numerous information factors usually affect crude oil prices, including fundamental supply-demand relationships, external uncertainty factors, and unexpected impacts such as epidemic disease.
In addition, these factors expand the uncertainty of prediction results and lower the prediction accuracy. Therefore, scholars are always seeking a better and more effective forecasting method. Energy companies realized that effective deployment of big data and digital transformation is the way forward to minimize costs in this era of low oil prices and focus on oil and gas production and exploration in the most optimized way. Consequently, these factors are anticipated to boost market growth.
One of the aspects hindering market growth in the current scenario is the lack of digital skills and digital mindsets, aggravated by the lack of skilled professionals and a workforce to handle the unstructured data effectively for analysis. In 2020, IT businesses listed more job openings for analysis-related talents than traditional ones, such as engineering, customer support, marketing and PR, and administration, for the second time in four years. Analysis skills include machine learning (ML), data science, data engineering, and visualization. In addition, the Accenture survey reported that 78% of the enterprises in India see big data as necessary for developing better customer relationships. About 53% of the enterprises surveyed cited the lack of talent as one of the prominent challenges in deploying big data.
Increased investment in the energy sector's digitization is driving market expansion, as digitalization tools and platforms facilitate the construction of renewable energy plants with automated processes, enabling informed decision-making. In addition, the proposed interconnections are the foundation for a more decentralized generation, thereby preventing the formation of isolated energy islands. These platforms also decrease downtime by providing maintenance alerts and anticipating asset maintenance.
Furthermore, production facilities must be modernized to make them more competitive and efficient, a major factor driving market growth. Moreover, the digitalization of new power plants ensures their services' efficiency and high availability. They are also supported by digital twins that aid in modeling, forecasting, and performance testing, from power generation to customer connection. Consequently, these factors create opportunities for market growth.
| Study Period | 2019-2031 | CAGR | 11.08% |
| Historical Period | 2019-2021 | Forecast Period | 2023-2031 |
| Base Year | 2022 | Base Year Market Size | USD 7.50 Billion |
| Forecast Year | 2031 | Forecast Year Market Size | USD 19.31 Billion |
| Largest Market | North America | Fastest Growing Market | Asia Pacific |
North America Dominates the Global Market
Based on region, the global big data analytics market in the energy sector is bifurcated into North America, Europe, Asia-Pacific, Latin America, and the Middle East and Africa.
North America is the most significant global big data analytics market in the energy sector and is estimated to exhibit a CAGR of 9.77% during the forecast period. North America is among the leading innovators and pioneers in adopting big data analytics. Due to the substantial demand for big data analytics in the energy sector and the region's stronghold on the big data analytics market, there are abundant opportunities for market growth. The region is inclined towards renewable energy sources, which is further expected to drive the growth of data analytics in smart grid systems. In addition, the U.S. plays a prominent role in increasing demand from the region compared to Canada. The nation has increased demand, especially from oil and gas, refining, and power generation segments. Most Americans consider solar and wind power good energy sources for the environment.
Additionally, several new projects have been initiated in renewable energy in the past few years. For instance, in Q4 2021, the construction of a 298 MW solar farm on 1,087 hectares of land in Pueblo County, Colorado, commenced as Sun Mountain Solar Farm, which was budgeted for USD 376 million. These factors are expected to augment the market's growth during the forecast period.
Asia-Pacific is estimated to exhibit a CAGR of 13.24% over the forecast period. Factors like the increasing adoption of IoT and smart technologies and various government initiatives, like smart cities, across Asia-Pacific nations, including China and India, affirm Asia-Pacific to be the fastest-growing region in big data analytics in the energy sector. As organizations increasingly uncover the true potential of business intelligence (BI) for increased productivity and profitability, spending on big data and analytics across the ASEAN region is expected to grow. In the Asia-Pacific region, China is expected to be the leading nation in big data analytics in the energy utility sector owing to its large population and increasing adoption rate of smart metering and smart grid systems nationwide. According to a report published in Smart Energy International, China installed more than 438 million smart electric meters in 2021.
Europe is among the most advanced and has one of the largest crude oil refineries in the world. The need for new projects and refinery expansion has grown significantly due to the recent drop in crude oil prices. One of Europe's key factors in adopting big data analytics is the region's high industrial activity and significant activity in the energy sector. The use of big data analytics in the energy sector is still developing and contingent upon the public's widespread adoption of smart meters. However, the demand from the energy sector over the coming years is expected to be immense. The Smart Metering Implementation Programme, recently launched by the UK government, aims to substitute more than 53 million traditional electricity and Gas Meters in more than 30 million residential buildings by 2020. One of the country's energy providers, Centrica-owned British Gas, claimed to have installed about 5.6 million domestic smart meters and is seeking to deploy 590,000 nationwide. All these factors boost market expansion.
In Latin America, the big data analytics market is gaining prominence as companies become more familiar with the benefits of implementing big data analytics solutions. Mexico and Brazil have taken the lead in adopting the region's smart meters. AES Eletropaulo, Eletrobras, Light, Celpa, and other utilities in Brazil have experimented with smart meters, ranging from small-scale projects to deployments of millions of meters. In addition, the region is witnessing increased government incentives and local investments by foreign energy companies in big data analytics. For instance, EMC invested USD 100 million in a new R&D center in Rio de Janeiro that focuses on big data to meet the needs of the large volume of data generated by the oil industry. The R&D center specializes in solutions and services to store, manage, protect, and analyze the available information.
In the Middle East and Africa, the UAE, the Kingdom of Saudi Arabia, and Qatar are expected to dominate the regional market due to the improving economies and increasing incorporation of technology. Saudi Arabia continues to achieve digital transformation by diversifying its economy through its Vision 2030 agenda. Neom, the Kingdom's mega-city project worth USD 500 billion, is expected to heavily incorporate innovative technology, such as big data analytics, IoT devices, and renewable energy.
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The global big data analytics market in the energy sector is segmented by application.
Based on application, the global big data analytics market in the energy sector is bifurcated into grid operations, smart metering, and asset and workforce management.
The grid operations segment dominates the global market and is projected to exhibit a CAGR of 10.38% over the forecast period. The demand for energy across the world is rising. According to the International Energy Agency, between 2005 and 2030, energy needs are estimated to expand by 55%, with demand rising from 11.4 billion metric tons of oil equivalent to 17.7 billion. With renewable energy sources, such as solar, contributing electricity to the power grid, utilities can use demand response analytics to determine the timing to release these power sources during peak demand. Modern industrial systems depend heavily on data analytics. To quicken the pace of electrification with a surging penetration of renewable energy sources, smart grids and super grids are practical solutions.
Smart grids are also becoming India's next big data and analytics use-case. Smart grids have been one of the biggest future advancements as they can harness big data from many sensors in the electric grid, which is very useful for managing resources. This is useful, especially in the utility sector, where resources like electricity cannot be stored dependably but cannot be underproduced; resources must be managed well.
The big data analytics approach to smart meters includes elements like grid operations, field services, resource planning, customer experience, and legal compliance. Through the data produced, it improves demand and forecasting, which is likely to spur market expansion. Big data analytics in smart meters also aid in predicting energy consumption, which is crucial for managing supply and demand and reducing energy waste. The large number of smart meters deployed can generate tremendous data. For example, if a smart meter records data every half hour, the number of records generated by twenty million smart meters is 3.5×1011 over just one year.
Furthermore, the diversity and nature of smart meter data (e.g., consumption data, fault and outage information, and price information) results in heterogeneous multi-source data. Thus, smart meter data has the characteristics of big data, such as large volume and high heterogeneity. Smart meter data must be quickly processed and analyzed to facilitate real-time analysis.
Other applications comprise asset and workforce management. The smart grid creates new and more complex asset classes, such as hardware, firmware, software, communications systems, and storage capabilities. As the pace of smart technology deployment quickens and swathes of projects result in millions of intelligent endpoint devices (IEDs) across the grid, the volume of assets to be managed is unprecedented. Asset management (AM) is crucial in a company with a high asset concentration. In addition, companies should prioritize using smart asset management models based on regulatory requirements, asset criticality, and other factors to improve operational performance. These models are based on big data analytics and AI.
Similarly, big data analytics (BDA) tools are often integrated into the APM software. A few statistics captured by Schneider Electric APM include a 20% increase in asset availability, a 20% reduction in operations and maintenance costs, a 35% reduction in inventory costs, and a 20% increase in workforce efficiency.
