Edge Analytics Market Size & Growth Report to 2030

The global edge analytics market size was valued at around USD 8 billion in 2022 and expected to register a significant CAGR of around 25% during the forecast period from 2023 to 2030. The market is experiencing robust growth due to a convergence of factors that collectively contribute to the demand for edge analytics solutions. At its core, edge analytics refers to the practice of processing and analyzing data closer to its source, right at the “edge” of the network, rather than sending it to centralized data centers or the cloud. This decentralized approach to data analysis offers several compelling advantages, making it a driving force in today’s technology landscape. One of the foremost benefits of edge analytics is its ability to facilitate real-time data processing. In applications where immediacy is crucial, such as industrial automation, autonomous vehicles, and IoT devices, the delay associated with transmitting data to distant data centers is simply unacceptable. Edge analytics bridges this gap by enabling on-the-spot analysis, ensuring that actionable insights are generated and acted upon without delay.

Market Snapshot:

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Market Drivers:

Real-Time Data Processing: Edge analytics enables real-time data processing and analysis at the edge of the network, reducing the need to transmit data to centralized data centers or the cloud. This is crucial for applications that require immediate insights, such as industrial automation, autonomous vehicles, and IoT devices.

Latency Reduction: Reduced data transfer times and lower latency are critical for applications like autonomous vehicles, augmented reality, and remote monitoring of critical infrastructure. Edge analytics helps achieve low-latency processing, enhancing the responsiveness of these systems.

Bandwidth Efficiency: Edge analytics minimizes the volume of data that needs to be transmitted over networks, making it more bandwidth-efficient. This is essential in scenarios where network bandwidth is limited or costly.

Data Privacy and Security: Edge analytics allows data to be processed locally, reducing the risk of data breaches during transit to centralized servers. It enhances data privacy and security, which is particularly important in industries like healthcare and finance.

Scalability: Edge analytics solutions can be easily scaled by adding more edge devices, making them suitable for businesses with evolving data processing needs. This scalability aligns with the growth of IoT and sensor networks.

IoT Proliferation: The increasing adoption of IoT devices and sensors across various industries generates vast amounts of data at the edge. Edge analytics helps organizations make sense of this data and derive actionable insights from it.

Industry 4.0 and Smart Manufacturing: Edge analytics is integral to the implementation of Industry 4.0 initiatives, where manufacturing processes are optimized through real-time monitoring and analytics at the edge. It improves production efficiency and reduces downtime.

AI and Machine Learning Integration: Edge analytics is often integrated with AI and machine learning algorithms, allowing edge devices to make intelligent decisions locally. This is particularly valuable for autonomous systems and smart applications.

Market Insights:

Market Analysis by Component:

The global edge analytics market is bifurcated into type, component, deployment mode, application, end-use, and geography. On the basis of component, the solutions segment dominated the market and estimated to hold the largest revenue share of over 2/3 market share by the end of the analysis period. These solutions play a foundational role in enabling organizations to harness the power of data generated at the edge of the network. They encompass a comprehensive suite of software and hardware components specifically designed to collect, process, and derive actionable insights from data in real-time or near-real-time, directly at the edge. The dominance of edge analytics solutions is attributed to several key factors. These solutions serve as the primary technology enabler for edge computing and analytics, facilitating the transformation of raw data into valuable insights without the latency associated with sending data to centralized data centers.

Furthermore, these solutions have broad applicability, spanning across diverse industries and use cases, from predictive maintenance in manufacturing to real-time patient monitoring in healthcare. They continuously evolve, incorporating advanced analytics techniques such as machine learning and artificial intelligence, and can be customized to meet the specific needs of organizations. Moreover, Edge Analytics Solutions enhance operational efficiency by reducing data transfer costs and providing real-time insights, contributing to cost savings and improved productivity.

Edge Analytics Solutions: Edge Analytics Solutions refer to the software and hardware components designed to perform data analysis and processing directly at the edge of the network where data is generated. These solutions encompass a range of tools, platforms, and applications that enable organizations to collect, process, and gain insights from data in real-time or near-real-time, without the need for data to traverse to centralized data centers or the cloud. Edge Analytics Solutions often include data processing algorithms, machine learning models, and data visualization tools, tailored to address specific use cases such as predictive maintenance, anomaly detection, and real-time monitoring. These solutions are integral to making informed decisions, optimizing operations, and enhancing the efficiency and responsiveness of edge devices and systems.

Edge Analytics Services: Edge Analytics Services encompass a variety of professional and managed services offered by vendors and service providers to support the implementation, operation, and maintenance of edge analytics solutions. These services may include consulting and advisory services for solution design and deployment, data integration services to ensure seamless data flow from edge devices, analytics model development and training, and ongoing support and maintenance services. Managed services can involve the monitoring of edge analytics systems, ensuring their performance, security, and reliability. Edge Analytics Services are essential for organizations seeking expertise and assistance in harnessing the full potential of edge analytics solutions, especially in complex environments or industries where specialized knowledge is required to extract actionable insights from edge-generated data.

Market Analysis by Type:

On the basis of type, the edge analytics market is further segmented into descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. The descriptive analytics segment dominated the market in the benchmark year 2022 and expected to hold the largest revenue share of around 40% of the market share by the end of the analysis period. The emerging markets or industries with limited prior exposure to data analytics may prioritize gaining a foundational understanding of their data through Descriptive Analytics. This initial emphasis on historical data insights could drive significant market growth as organizations embark on their analytics journeys. Additionally, in data-intensive sectors like manufacturing or energy, the sheer volume of data generated at the edge may lead organizations to initially prioritize Descriptive Analytics before progressing to more advanced analytics methods.

Descriptive Analytics: Descriptive analytics in the context of the edge analytics market involves the examination of historical data generated at the edge of the network. This type of analytics focuses on providing a comprehensive overview of past events and trends, enabling organizations to understand what has happened. It plays a fundamental role in summarizing and visualizing data from edge devices, allowing for insights into historical performance, IoT device statuses, and trends within edge processes. Descriptive analytics serves as the foundation for more advanced analytics types by offering a retrospective view of data at the edge, aiding organizations in assessing historical operations.

Diagnostic Analytics: Diagnostic analytics in the edge analytics market goes beyond merely summarizing historical data. It delves into the reasons behind past events and trends, aiming to uncover the “why” behind specific outcomes. In the context of edge analytics, diagnostic analytics is employed to diagnose and understand the root causes of issues, anomalies, or patterns detected at the edge. It plays a critical role in troubleshooting edge-related challenges by identifying contributing factors and providing insights into the underlying causes. Diagnostic analytics is invaluable for maintaining the reliability and performance of edge devices and systems by enabling proactive issue resolution.

Predictive Analytics: Predictive analytics within the edge analytics market utilizes historical data and advanced statistical algorithms to make forecasts and predictions about future events, trends, or conditions at the edge of the network. This type of analytics empowers organizations to anticipate potential future scenarios based on patterns and historical data trends. In edge analytics, predictive analytics is employed to forecast conditions, such as equipment failures, security threats, or data patterns, allowing organizations to take proactive measures and optimize edge operations. It aids in risk management, resource allocation, and decision-making by providing insights into what might happen in the future.

Prescriptive Analytics: Prescriptive analytics is the most advanced type of analytics within the edge analytics market. It not only predicts future outcomes but also provides actionable recommendations and optimal courses of action to achieve desired results. In edge analytics, prescriptive analytics offers organizations guidance on decision-making at the edge by suggesting specific actions to optimize operations, enhance efficiency, and achieve predefined objectives. This type of analytics is instrumental in driving intelligent automation and informed decision support within edge environments, enabling organizations to not only foresee future scenarios but also take the most advantageous actions to shape those scenarios to their advantage.

The edge analytics market research report presents the analysis of each segment from 2020 to 2030 considering 2022 as the base year for the research. The compounded annual growth rate (CAGR) for each respective segment is calculated for the forecast period from 2023 to 2030.

Historical & Forecast Period

• 2020-21 – Historical Year
• 2022 – Base Year
• 2023-2030 – Forecast Period

Market Segmentation:

By Type:

• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics

By Component:

• Solution
• Services

By Deployment:

• On-Premises
• On-Cloud

By Application:

• Marketing & Sales
• Operations
• Finance
• Human Resources
• Others

By End-Use:

• IT & Telecom
• BFSI
• Manufacturing
• Healthcare and Life Science
• Retail
• Transportation and Logistics
• Government
• Energy and Utilities
• Others

By Region:

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East & Africa

Regional Analysis:

Geographically, North America dominated the global edge analytics market in 2022 and expected to hold the largest revenue share of over 1/3 market share by 2030. The United States, in particular, has played a pivotal role in this dominance due to its status as a technological powerhouse. The region has consistently been at the forefront of technological innovation, with Silicon Valley serving as a global hub for cutting-edge technologies and startups. This culture of innovation has fostered the development and adoption of edge analytics solutions.

Moreover, North American organizations have been early adopters of edge analytics, recognizing the advantages of real-time data processing, reduced latency, and improved operational efficiency across various industries. Strong support from the government and regulatory bodies, coupled with significant investments and funding, has also propelled the growth of edge analytics startups and initiatives in the region.

Furthermore, North America’s diverse industry verticals, including manufacturing, healthcare, finance, and transportation, have enthusiastically embraced edge analytics to enhance their operations and gain a competitive edge. The region’s well-established ecosystem of technology vendors, service providers, and industry leaders has facilitated collaboration and innovation, contributing to North America’s dominance in the global Edge Analytics market.

Competitive Analysis:

Some of the prominent market players operating in the global edge analytics market are Cisco Systems, Inc., IBM Corporation, Hewlett Packard Enterprise (HPE), Dell Technologies, Inc., SAS Institute, Inc., and Microsoft Corporation. Companies are exploring markets by expansion, new investment, the introduction of new services, and collaboration as their preferred strategies. Players are exploring new geography through expansion and acquisition to gain a competitive advantage through joint synergy.

Recent Developments:

• Cisco Systems announced in March 2023 that it had launched a new edge analytics platform called Cisco Edge Intelligence. The platform is designed to help businesses collect, analyze, and act on data at the edge of the network.
• Intel announced in April 2023 that it had acquired FogHorn Systems, a provider of edge analytics software. The acquisition will help Intel to expand its edge analytics offerings and accelerate the adoption of edge analytics solutions.
• Microsoft announced in May 2023 that it had launched Azure Edge Zones, a new service that makes it easier for businesses to deploy edge analytics solutions. Azure Edge Zones are located in close proximity to data sources, which can help to improve latency and performance.
• IBM announced in June 2023 that it had launched Watson Edge, a new edge analytics platform that is designed to help businesses collect, analyze, and act on data at the edge of the network.
• Amazon Web Services announced in July 2023 that it had launched AWS Wavelength, a new service that makes it easier for businesses to deploy edge analytics solutions. AWS Wavelength is designed to help businesses reduce latency and improve performance by running applications closer to data sources.

Key Companies:

• Cisco Systems, Inc.
• IBM Corporation
• Hewlett Packard Enterprise (HPE)
• Dell Technologies, Inc.
• SAS Institute, Inc.
• Microsoft Corporation
• SAP SE
• FogHorn Systems
• Vantiq
• Oracle Corporation
• Intel Corporation
• Siemens AG
• General Electric (GE)
• Nutanix, Inc.
• Fog Computing Limited

Key Questions Answered by Edge Analytics Market Report

• Global edge analytics market forecasts from 2023-2030
• Regional market forecasts from 2023-2030 covering Asia-Pacific, North America, Europe, Middle East & Africa, and Latin America
• Country-level forecasts from 2023-2030 covering 15 major countries from the regions as mentioned above
• Edge analytics submarket forecasts from 2023-2030 covering the market by type, component, deployment, application, end-use, and geography
• Various industry models such as SWOT analysis, Value Chain Analysis about the market
• Analysis of the key factors driving and restraining the growth of the global, regional, and country-level edge analytics markets from 2023-2030
• Competitive Landscape and market positioning of top 10 players operating in the market