Smart grid data analytics are software solutions that are designed to analyze the data generated from a network of smart grid. These can be referred to as a subset of Big Data analytics solutions designed for the energy & utilities sector. These solutions are developed to help utility providers address issues related to the grid operations and financial management more rapidly and effectively. The primary function of smart grid data analytics solutions is to analyze the huge volumes of data generated from various components including smart meters, automated distribution systems, smart appliances and other sensing equipment in the network.
The Smart Grid Data Analytics market estimate is anticipated to develop at a Compound Annual Growth Rate (CAGR) of +25%.
Smart Grid Data Analytics Market provides an overview of the various factors enabling growth in the market. It also presents insights into various restraints that pose threat and highlights opportunities that will help the market pick pace in the forthcoming years. Global Smart Grid Data Analytics Market report compiles exhaustive information obtained via proven research methodologies and from trusted sources from within the industries. It also includes expert opinions to provide readers a clearer perspective regarding the global market.
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Companies Profiled in this report includes, SAS, Itron, AutoGrid, Sensus, Silver Spring Networks, IBM, Infosys, Microsoft, SAP, Verizon, Accenture, EMC Corporation.
Top Players are analyzed based on attributes such as their product profile, product introductions, SWOT analysis, and contact information. Each of the key players in the market is revealed in terms of their basic company data, key competitors, and presence of manufacturing bases. These players are also described according to their offerings in product types and technologies, the production costs, revenue generated, and overall price of these solutions.
The Smart grid analytics market is broadly segmented by type of its solution: AMI analytics, demand response analytics/ advanced analytics for Demand Response Management System (DRMS), asset analytics, analytics for grid optimization, energy data forecasting/ load forecasting, and visualization tools; by type of service: professional service, and support and maintenance service; by type of deployment model: on-premise, and on-demand (cloud-based).
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Further, the study offers an analysis of the current performance of the key regional markets, namely North America, Europe, Asia-Pacific (APAC), Middle East & Africa and Latin America. The analysis is based on the manufacturing volume, production capacity, pricing strategy, the dynamics of demand, supply, and sales, return on investments (RoI), and the growth rate of the global Smart Grid Data Analytics market in each of the regions.
In conclusion, the global Smart Grid Data Analytics market standing in the past years and in the coming years is determined by its performance up until now. Results are supported by various sources and market research techniques, all of which are verified. Then, the report explores the major industry players in detail. In order to identify the existing hierarchy in this market, analysts have reviewed the profiles of the key market participants, using several analytical tools, such as SWOT analysis.
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Key questions answered in the report include
• What are the key components driving the global Smart Grid Data Analytics market?
• What will be the market estimate and the development rate in 2022?
• What are the challenges to market growth?
• Who are the significant players in the global Smart Grid Data Analytics market?
• What are the market opportunities and threats faced by the vendors in the global Smart Grid Data Analytics market?
• Trending factors influencing the market shares of the Americas, APAC, and EMEA.
Table of Contents
Global Smart Grid Data Analytics Market Research Report
Chapter 1 Smart Grid Data Analytics Market Overview
Chapter 2 Global Economic Impact on Industry
Chapter 3 Global Market Competition by Manufacturers
Chapter 4 Global Production, Revenue (Value) by Region
Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions
Chapter 6 Global Production, Revenue (Value), Price Trend by Type
Chapter 7 Global Market Analysis by Application
Chapter 8 Manufacturing Cost Analysis
Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 10 Marketing Strategy Analysis, Distributors/Traders
Chapter 11 Market Effect Factors Analysis
Chapter 12 Global Smart Grid Data Analytics Market Forecast
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