[250 Pages Report]Smart Grid Data Analytics Market analysis report by Future Market Insights shows that global sales of Smart Grid Data Analytics Market in 2021 was held at US$ 4.3 Billion. With the projected growth of 12.3% during 2022 to 2032, the market is expected to reach a valuation of US$ 14.9 Billion by 2032. Cloud-based Smart Grid Data Analytics is expected to be the highest revenue generating segment, projected to grow at a CAGR of around 14.9% during 2022 to 2032.
| Attributes | Details |
|---|---|
| Global Smart Grid Data Analytics Market Size (2022) | US$ 4.7 Billion |
| Global Smart Grid Data Analytics Market Size (2032) | US$ 14.9 Billion |
| Global Smart Grid Data Analytics Market CAGR (2022 to 2032) | 12.3% |
| USA Smart Grid Data Analytics Market Size (2032) | US$ 2.5 Billion |
| Key Companies Covered |
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As per the Smart Grid Data Analytics Market research by Future Market Insights - a market research and competitive intelligence provider, historically, from 2015 to 2021, market value of the Smart Grid Data Analytics Market increased at around 14% CAGR.
The global smart grid data analytics market is primarily driven by rising smart grid investments, a rapid increase in the pace of renewable energy integration into existing systems, and technical innovation.
The rise in smart meter installation is projected to expand the revenue for smart grid data analytics market. AMI or Advanced Metering Infrastructure is a structured system that incorporates smart meters, communications networks, and information management framework to provide a two-way digital interface among customers and utilities. AMI offers a variety of operational benefits that help to reduce utility costs and offer convenience to customers.
AMI decreases operational costs considerably by remotely reading meters, connecting/disconnecting services, recognizing outages by issuing more exact bills faster, and enabling utilities to offer consumers with digital access to their use information. Thus, rising smart meter installation and adoption of improved metering infrastructure are likely to drive the worldwide smart grid data analytics industry throughout the forecast period.
Improved grid reliability, outstanding inherent operational performance, and effective outage are expected to drive market growth. The grid feature technologies lead to problem detection and allow the network to self-heal automatically. With the continuous disruption detection, the enhanced technology provides real-time support to energy management services, increasing situational awareness in smart grid distribution control. For instance, Networked Energy Services Corporation, one of the global smart grid solutions and service provider with the industry's major Energy Applications Platform (EAPTM), confirmed in May 2021 the strengthening of its security products with threat identification and response, with plans to deploy over 1 Mn smart meters for its new grid watch solution by mid-2021.
Several government schemes and regulations are likely to promote market expansion. Different administrations are gradually investing in these innovations and technologies in the expectation that they would assist them in meeting their greenhouse emission reduction objectives and enabling long-term economic success. Furthermore, some nations currently have net energy measuring protocols and equipment, while others are still researching the innovation and its operation, which is likely to generate attractive market potential. Countries such as United States and China have seen widespread deployment of smart meters, owing mostly to the ongoing backing of their respective governments. These considerations are projected to boost growth for analytical solutions to manage the massive amounts of data generated by smart meters. Private utility providers in the United States, such as ConEd and Duke, have seen considerable increase in smart meter deployments. This is demonstrated by the estimates of Edison Foundation Institute for Electric Innovation that smart meters installed by utilities in the United States totalled around 98 Mn at the end of 2019 and are expected to reach 107 Mn by the end of 2020.
In another example, the Indian Finance Minister declared in 2020 that the government intends to replace all traditional power meters with smart electricity meters within three years. These government efforts conducted by governments throughout the world are driving market growth.
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During the projected period, North America is expected to hold the largest share in global Smart Grid Data Analytics Market with a projected market size of nearly US$ 3 Bn by 2032. The smart grid data analytics market in North America is expected to witness high adoption of smart grid technologies due to huge expenditures in new grid developments and smart city initiatives. The region has promising government policies in place to promote sustainable energy generation. Furthermore, the increased demand for efficient energy in power generation is likely to drive the regional outlook.
Growing distribution automation investments, as well as the increasing complexity of electricity distribution infrastructure, are expected to boost the market outlook as well. For instance, the GridWise Alliance announced large investments in the transmission and distribution infrastructure system of the United States in July 2021. The project will spend US$ 5 Bn in grid adaptability technology including controls, sensors, and storage, and US$ 8.5 Bn in grid connection technologies like improved metering infrastructure and energy monitoring systems.
Other regions including as Asia Pacific and Europe, continue to account for a sizable market share due to broad acceptance of these solutions and a greater emphasis on sustainable and renewable energy development. For instance, in February 2020, the Government of India under the Smart Meter National Programme, declared that 1 Mn smart meters will be placed across the country (SMNP). In May 2020, the U.K government's smart metering project implementation programme showed around 26.6 Mn electricity meters operated by major energy firms in private households across the country.
The United States is expected to have the highest smart grid data analytics market share of US$ 2.5 Bn by the end of 2032 with an expected CAGR of 10% by the end of 2032. Due to the increasing severity and frequency of natural catastrophes, as well as unscheduled power outages, the United States will experience significant growth. Furthermore, increased government activities aimed at addressing climate change and ensuring mutual energy security, as well as the introduction of renewable energy into power networks, will augment the demand commercial sector.
Rising urbanization, theft prevention facilities, and lower management and operations costs for services are some of the key drivers encouraging smart grid data analytics market growth in the region. The integration of owner and customer power generation systems, including sustainable energy, allows environmental regulations and objectives to be met. Expanding partnerships, joint ventures, and inorganic strategic partnerships will benefit the industry environment.
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Generalized Solutions segment is forecasted to grow at the highest CAGR of around 14% during 2022-2032. To develop a stronger decision support system, smart grid data analytics solutions are installed at the grid owners' end. The generalized solutions are designed to evaluate data provided by various smart grid components such as smart meters, automated distribution systems, smart appliances, and other sensing devices. The gathered data is relayed utilizing the grid's two-way communication network for additional predictive analysis.
Revenue through cloud-based smart grid data analytics is forecasted to grow at the highest CAGR of around 14.9% during 2022-2032. The advent of cloud deployment choices for smart grid data analytics systems has increased demand across multiple industries, like IT and telecom, BFSI, and media & entertainment. The new companies are offering cloud-based solutions to provide cost-effective solutions to SMEs.
For businesses, an on-premises system can be restrictive as the result of being a real-time solution requiring significant implementation. This is avoided with an offsite system, having favorable operational enhancements because of its simplicity and lower expenses from reduced implementation. Another advantage is a broader array of resources that can be connected. Besides, cloud-based technologies are less expensive to implement, as compared to on-premise deployment.
In February 2022, IBM and SAP have increased their collaboration to assist clients in migrating operations from SAP Solutions to Cloud. IBM announced a collaboration with SAP to provide technology and consulting services to help clients embrace a hybrid cloud strategy and migrate mission-critical activities from SAP Solutions to cloud in regulated and non-regulated sectors.
The sector for smart grid data analytics is very much competitive and fragmented. The market has been experiencing fierce competition due to the introduction of new start-ups offering a diversified variety of creative solutions catering to different industrial requirements. Some of the key smart grid data analytics companies include Itron Inc., Siemens AG, and IBM Corporation
Some of the key recent market developments include:
Similarly, recent developments related to companies Smart Grid Data Analytics Market have been tracked by the team at Future Market Insights, which are available in the full report.
The global Smart Grid Data Analytics Market is worth more than US$ 4.3 Bn at present.
The value of Smart Grid Data Analytics Market is projected to increase at a CAGR of around 12.3% during 2022 – 2032.
The value of Smart Grid Data Analytics Market increased at a CAGR of around 14% during 2015 – 2021.
Utility companies’ increasing usage of smart grid data analytics to analyse load behavior, optimise grid operations, reduce power outages, and make smarter choices has been one of the key trends in smart grid data analytics market.
The market for Smart Grid Data Analytics Market in US is projected to expand at a CAGR of around 10% during 2022 – 2032.
1. Executive Summary 1.1. Global Market Outlook 1.2. Demand-side Trends 1.3. Supply-side Trends 1.4. Technology Roadmap Analysis 1.5. Analysis and Recommendations 2. Market Overview 2.1. Market Coverage / Taxonomy 2.2. Market Definition / Scope / Limitations 3. Market Background 3.1. Market Dynamics 3.1.1. Drivers 3.1.2. Restraints 3.1.3. Opportunity 3.1.4. Trends 3.2. Scenario Forecast 3.2.1. Demand in Optimistic Scenario 3.2.2. Demand in Likely Scenario 3.2.3. Demand in Conservative Scenario 3.3. Opportunity Map Analysis 3.4. Investment Feasibility Matrix 3.5. PESTLE and Porter’s Analysis 3.6. Regulatory Landscape 3.6.1. By Key Regions 3.6.2. By Key Countries 4. Global Market Analysis 2017-2021 and Forecast, 2022-2032 4.1. Historical Market Size Value (US$ Mn) Analysis, 2017-2021 4.2. Current and Future Market Size Value (US$ Mn) Projections, 2022-2032 4.2.1. Y-o-Y Growth Trend Analysis 4.2.2. Absolute $ Opportunity Analysis 5. Global Market Analysis 2017-2021 and Forecast 2022-2032, By Component 5.1. Introduction / Key Findings 5.2. Historical Market Size Value (US$ Mn) Analysis By Component, 2017-2021 5.3. Current and Future Market Size Value (US$ Mn) Analysis and Forecast By Component, 2022-2032 5.3.1. Solution 5.3.1.1. AMI Analytics 5.3.1.2. Demand Response Analytics 5.3.1.3. Grid Optimization 5.3.1.4. Asset Management 5.3.1.5. Others 5.4. Y-o-Y Growth Trend Analysis By Component, 2017-2021 5.5. Absolute $ Opportunity Analysis By Component, 2022-2032 6. Global Market Analysis 2017-2021 and Forecast 2022-2032, By Deployment Model 6.1. Introduction / Key Findings 6.2. Historical Market Size Value (US$ Mn) Analysis By Deployment Model, 2017-2021 6.3. Current and Future Market Size Value (US$ Mn) Analysis and Forecast By Deployment Model, 2022-2032 6.3.1. On-premise 6.3.2. Cloud-based 6.3.3. Hybrid 6.4. Y-o-Y Growth Trend Analysis By Deployment Model, 2017-2021 6.5. Absolute $ Opportunity Analysis By Deployment Model, 2022-2032 7. Global Market Analysis 2017-2021 and Forecast 2022-2032, By End-User 7.1. Introduction / Key Findings 7.2. Historical Market Size Value (US$ Mn) Analysis By End-User, 2017-2021 7.3. Current and Future Market Size Value (US$ Mn) Analysis and Forecast By End-User, 2022-2032 7.3.1. Small/ Medium Enterprises 7.3.2. Large Enterprises 7.3.3. Public Sector 7.4. Y-o-Y Growth Trend Analysis By End-User, 2017-2021 7.5. Absolute $ Opportunity Analysis By End-User, 2022-2032 8. Global Market Analysis 2017-2021 and Forecast 2022-2032, By IT Solution 8.1. Introduction / Key Findings 8.2. Historical Market Size Value (US$ Mn) Analysis By IT Solution, 2017-2021 8.3. Current and Future Market Size Value (US$ Mn) Analysis and Forecast By IT Solution, 2022-2032 8.3.1. Specialized Solutions (for Back-end) 8.3.1.1. CRM 8.3.1.2. Billing 8.3.1.3. Customer Care 8.3.1.4. Business Intelligence 8.3.1.5. Others 8.3.2. Generalized Solutions (for Front-end) 8.3.2.1. CRM 8.3.2.2. Billing 8.3.2.3. Customer Care 8.3.2.4. Business Intelligence 8.3.2.5. Others 8.4. Y-o-Y Growth Trend Analysis By IT Solution, 2017-2021 8.5. Absolute $ Opportunity Analysis By IT Solution, 2022-2032 9. Global Market Analysis 2017-2021 and Forecast 2022-2032, By Region 9.1. Introduction 9.2. Historical Market Size Value (US$ Mn) Analysis By Region, 2017-2021 9.3. Current Market Size Value (US$ Mn) Analysis and Forecast By Region, 2022-2032 9.3.1. North America 9.3.2. Latin America 9.3.3. Europe 9.3.4. Asia Pacific 9.3.5. MEA 9.4. Market Attractiveness Analysis By Region 10. North America Market Analysis 2017-2021 and Forecast 2022-2032, By Country 10.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021 10.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 10.2.1. By Country 10.2.1.1. U.S. 10.2.1.2. Canada 10.2.2. By Component 10.2.3. By Deployment Model 10.2.4. By End-User 10.2.5. By IT Solution 10.3. Market Attractiveness Analysis 10.3.1. By Country 10.3.2. By Component 10.3.3. By Deployment Model 10.3.4. By End-User 10.3.5. By IT Solution 10.4. Key Takeaways 11. Latin America Market Analysis 2017-2021 and Forecast 2022-2032, By Country 11.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021 11.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 11.2.1. By Country 11.2.1.1. Brazil 11.2.1.2. Mexico 11.2.1.3. Rest of Latin America 11.2.2. By Component 11.2.3. By Deployment Model 11.2.4. By End-User 11.2.5. By IT Solution 11.3. Market Attractiveness Analysis 11.3.1. By Country 11.3.2. By Component 11.3.3. By Deployment Model 11.3.4. By End-User 11.3.5. By IT Solution 11.4. Key Takeaways 12. Europe Market Analysis 2017-2021 and Forecast 2022-2032, By Country 12.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021 12.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 12.2.1. By Country 12.2.1.1. Germany 12.2.1.2. Italy 12.2.1.3. France 12.2.1.4. U.K. 12.2.1.5. Spain 12.2.1.6. Russia 12.2.1.7. BENELUX 12.2.1.8. Rest of Europe 12.2.2. By Component 12.2.3. By Deployment Model 12.2.4. By End-User 12.2.5. By IT Solution 12.3. Market Attractiveness Analysis 12.3.1. By Country 12.3.2. By Component 12.3.3. By Deployment Model 12.3.4. By End-User 12.3.5. By IT Solution 12.4. Key Takeaways 13. Asia Pacific Market Analysis 2017-2021 and Forecast 2022-2032, By Country 13.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021 13.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 13.2.1. By Country 13.2.1.1. China 13.2.1.2. Japan 13.2.1.3. South Korea 13.2.1.4. India 13.2.1.5. Rest of Asia Pacific 13.2.2. By Component 13.2.3. By Deployment Model 13.2.4. By End-User 13.2.5. By IT Solution 13.3. Market Attractiveness Analysis 13.3.1. By Country 13.3.2. By Component 13.3.3. By Deployment Model 13.3.4. By End-User 13.3.5. By IT Solution 13.4. Key Takeaways 14. MEA Market Analysis 2017-2021 and Forecast 2022-2032, By Country 14.1. Historical Market Size Value (US$ Mn) Trend Analysis By Market Taxonomy, 2017-2021 14.2. Market Size Value (US$ Mn) Forecast By Market Taxonomy, 2022-2032 14.2.1. By Country 14.2.1.1. GCC 14.2.1.2. Rest of MEA 14.2.2. By Component 14.2.3. By Deployment Model 14.2.4. By End-User 14.2.5. By IT Solution 14.3. Market Attractiveness Analysis 14.3.1. By Country 14.3.2. By Component 14.3.3. By Deployment Model 14.3.4. By End-User 14.3.5. By IT Solution 14.4. Key Takeaways 15. Key Countries Market Analysis 15.1. U.S. 15.1.1. Pricing Analysis 15.1.2. Market Share Analysis, 2021 15.1.2.1. By Component 15.1.2.2. By Deployment Model 15.1.2.3. By End-User 15.1.2.4. By IT Solution 15.2. Canada 15.2.1. Pricing Analysis 15.2.2. Market Share Analysis, 2021 15.2.2.1. By Component 15.2.2.2. By Deployment Model 15.2.2.3. By End-User 15.2.2.4. By IT Solution 15.3. Brazil 15.3.1. Pricing Analysis 15.3.2. Market Share Analysis, 2021 15.3.2.1. By Component 15.3.2.2. By Deployment Model 15.3.2.3. By End-User 15.3.2.4. By IT Solution 15.4. Mexico 15.4.1. Pricing Analysis 15.4.2. Market Share Analysis, 2021 15.4.2.1. By Component 15.4.2.2. By Deployment Model 15.4.2.3. By End-User 15.4.2.4. By IT Solution 15.5. Argentina 15.5.1. Pricing Analysis 15.5.2. Market Share Analysis, 2021 15.5.2.1. By Component 15.5.2.2. By Deployment Model 15.5.2.3. By End-User 15.5.2.4. By IT Solution 15.6. Germany 15.6.1. Pricing Analysis 15.6.2. Market Share Analysis, 2021 15.6.2.1. By Component 15.6.2.2. By Deployment Model 15.6.2.3. By End-User 15.6.2.4. By IT Solution 15.7. Italy 15.7.1. Pricing Analysis 15.7.2. Market Share Analysis, 2021 15.7.2.1. By Component 15.7.2.2. By Deployment Model 15.7.2.3. By End-User 15.7.2.4. By IT Solution 15.8. France 15.8.1. Pricing Analysis 15.8.2. Market Share Analysis, 2021 15.8.2.1. By Component 15.8.2.2. By Deployment Model 15.8.2.3. By End-User 15.8.2.4. By IT Solution 15.9. U.K. 15.9.1. Pricing Analysis 15.9.2. Market Share Analysis, 2021 15.9.2.1. By Component 15.9.2.2. By Deployment Model 15.9.2.3. By End-User 15.9.2.4. By IT Solution 15.10. Spain 15.10.1. Pricing Analysis 15.10.2. Market Share Analysis, 2021 15.10.2.1. By Component 15.10.2.2. By Deployment Model 15.10.2.3. By End-User 15.10.2.4. By IT Solution 15.11. Russia 15.11.1. Pricing Analysis 15.11.2. Market Share Analysis, 2021 15.11.2.1. By Component 15.11.2.2. By Deployment Model 15.11.2.3. By End-User 15.11.2.4. By IT Solution 15.12. BENELUX 15.12.1. Pricing Analysis 15.12.2. Market Share Analysis, 2021 15.12.2.1. By Component 15.12.2.2. By Deployment Model 15.12.2.3. By End-User 15.12.2.4. By IT Solution 15.13. China 15.13.1. Pricing Analysis 15.13.2. Market Share Analysis, 2021 15.13.2.1. By Component 15.13.2.2. By Deployment Model 15.13.2.3. By End-User 15.13.2.4. By IT Solution 15.14. Japan 15.14.1. Pricing Analysis 15.14.2. Market Share Analysis, 2021 15.14.2.1. By Component 15.14.2.2. By Deployment Model 15.14.2.3. By End-User 15.14.2.4. By IT Solution 15.15. South Korea 15.15.1. Pricing Analysis 15.15.2. Market Share Analysis, 2021 15.15.2.1. By Component 15.15.2.2. By Deployment Model 15.15.2.3. By End-User 15.15.2.4. By IT Solution 15.16. India 15.16.1. Pricing Analysis 15.16.2. Market Share Analysis, 2021 15.16.2.1. By Component 15.16.2.2. By Deployment Model 15.16.2.3. By End-User 15.16.2.4. By IT Solution 15.17. GCC Countries 15.17.1. Pricing Analysis 15.17.2. Market Share Analysis, 2021 15.17.2.1. By Component 15.17.2.2. By Deployment Model 15.17.2.3. By End-User 15.17.2.4. By IT Solution 16. Market Structure Analysis 16.1. Competition Dashboard 16.2. Competition Benchmarking 16.3. Market Share Analysis of Top Players 16.3.1. By Regional 16.3.2. By Component 16.3.3. By Deployment Model 16.3.4. By End-User 16.3.5. By IT Solution 17. Competition Analysis 17.1. Competition Deep Dive 17.1.1. Accenture, Plc. 17.1.1.1. Overview 17.1.1.2. Product Portfolio 17.1.1.3. Profitability by Market Segment 17.1.1.4. Sales Footprint 17.1.1.5. Strategy Overview 17.1.1.5.1. Marketing Strategy 17.1.2. Capgemini S.A., Inc. 17.1.2.1. Overview 17.1.2.2. Product Portfolio 17.1.2.3. Profitability by Market Segment 17.1.2.4. Sales Footprint 17.1.2.5. Strategy Overview 17.1.2.5.1. Marketing Strategy 17.1.3. Dell EMC 17.1.3.1. Overview 17.1.3.2. Product Portfolio 17.1.3.3. Profitability by Market Segment 17.1.3.4. Sales Footprint 17.1.3.5. Strategy Overview 17.1.3.5.1. Marketing Strategy 17.1.4. IBM Corporation 17.1.4.1. Overview 17.1.4.2. Product Portfolio 17.1.4.3. Profitability by Market Segment 17.1.4.4. Sales Footprint 17.1.4.5. Strategy Overview 17.1.4.5.1. Marketing Strategy 17.1.5. Oracle Corporation 17.1.5.1. Overview 17.1.5.2. Product Portfolio 17.1.5.3. Profitability by Market Segment 17.1.5.4. Sales Footprint 17.1.5.5. Strategy Overview 17.1.5.5.1. Marketing Strategy 17.1.6. Schneider Electric 17.1.6.1. Overview 17.1.6.2. Product Portfolio 17.1.6.3. Profitability by Market Segment 17.1.6.4. Sales Footprint 17.1.6.5. Strategy Overview 17.1.6.5.1. Marketing Strategy 17.1.7. Itron Inc. 17.1.7.1. Overview 17.1.7.2. Product Portfolio 17.1.7.3. Profitability by Market Segment 17.1.7.4. Sales Footprint 17.1.7.5. Strategy Overview 17.1.7.5.1. Marketing Strategy 17.1.8. AES Ohio 17.1.8.1. Overview 17.1.8.2. Product Portfolio 17.1.8.3. Profitability by Market Segment 17.1.8.4. Sales Footprint 17.1.8.5. Strategy Overview 17.1.8.5.1. Marketing Strategy 18. Assumptions & Acronyms Used 19. Research Methodology
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Smart Grid Data Analytics Market
