市场调查报告书
商品编码
1301277
边缘分析的市场规模,占有率,趋势分析报告:各类型,各零件,配置模式,各用途,各最终用途产业,各地区,及市场区隔趋势:2023年~2030年Edge Analytics Market Size, Share & Trends Analysis Report By Type (Descriptive Analytics, Diagnostics Analytics), By Component, By Deployment Model, By Application, By End Use Industry, By Region, And Segment Forecasts, 2023 - 2030 |
Grand View Research的最新报告显示,全球边缘分析市场规模在2023年到2030年将记录25.3%的年复合成长率,到2030年达到407亿1,000万美元。
通过在边缘设备本身上执行数据分析和处理,机器人可以快速响应其环境,而无需严重依赖集中式系统。这种方法提供了实时洞察、更低的延迟、更高的安全性和优化的带宽。随着物联网的兴起和边缘生成的数据量不断增加,边缘分析受到广泛关注。许多工业组织正在使用物联网 (IoT) 来监控其製造机器、管道和设备。
物联网生成和存储难以实时管理和解释的数据。来自物联网设备的数据被发送到边缘分析进行处理和理解。分析算法帮助人类决定需要什么数据,不需要什么数据。在许多应用和行业中,及时决策对于实现运营效率、确保安全和提供卓越的客户体验至关重要。自动驾驶汽车、工业自动化和智慧城市等某些应用需要实时分析功能。
边缘分析可以在边缘进行即时处理和决策,最大限度地减少延迟并实现快速响应。此外,无人机和机器人等行业严重依赖实时决策能力。这些系统必须处理大量传感器数据并立即响应不断变化的环境和情况。边缘分析可以分析和解释边缘的传感器数据,使这些自主系统能够在不依赖集中处理的情况下做出快速、准确的决策。
The global edge analytics market size is estimated to reach USD 40.71 billion by 2030, registering a CAGR of 25.3% from 2023 to 2030, according to a new report by Grand View Research, Inc. Performing data analysis and processing on the edge devices themselves, robots can quickly respond to their environment without relying heavily on a centralized system. This approach offers real-time insights, reduced latency, improved security, and optimized bandwidth. With the rise of the Internet of Things and the increasing amount of data generated at the edge, edge analytics has gained significant attention. Many industrial organizations use the Internet of Things (IoT) to monitor manufacturing machinery, pipelines, and equipment.
IoT generates and stores data that might be challenging to manage and interpret in real time. The data from IoT devices is delivered into edge analytics to be processed and understood. Analytics algorithms assist humans in determining which data is required and which is unnecessary. In many applications and industries, timely decisions are crucial for achieving operational efficiency, ensuring safety, and delivering superior customer experiences. Certain applications, such as autonomous vehicles, industrial automation, and smart cities, demand real-time analytics capabilities.
Edge analytics enable immediate processing and decision-making at the edge, minimizing latency and enabling rapid responses. Moreover, industries such as drones and robotics heavily rely on real-time decision-making capabilities. These systems must process vast amounts of sensor data and respond instantaneously to changing environments and situations. Edge analytics enable the analysis and interpretation of sensor data at the edge, allowing these autonomous systems to make quick and accurate decisions without relying on centralized processing.
The increasingly vast amount of data from connected devices around the globe is driving market expansion, real-time intelligence acting as a catalyst for the growth of edge analytics on network devices and adopting edge analytics, enhancing scalability and cost optimization. Analytical computing is performed at the device's edge rather than waiting for data to be retrieved back at a centralized storage system and then imply analytical application. Furthermore, the manufacturing industry may make substantial use of edge analytics, for example, in smart production lines, pointing out manufacturing errors, packing, and so on in real-time. The IoT connects numerous devices and sensors that generate massive volumes of data in real-time; by applying the technology, this data can be processed and analyzed at the edge, enabling rapid decision-making and reducing the need to transmit all data to a central location. For example, in smart cities, it can help monitor and manage traffic patterns, energy consumption, and public safety in real-time.
In the manufacturing sector, it enables real-time monitoring and predictive maintenance of machines and equipment; by analyzing sensor data at the edge, manufacturers can identify potential failures, optimize maintenance schedules, and minimize downtime. It also plays a crucial role in healthcare by enabling real-time patient monitoring, remote diagnostics, and personalized treatment. Edge devices can analyze patient data, including vital signs and medical history, to provide timely insights for healthcare professionals. Retailers can leverage it for real-time inventory management, customer analytics, and personalized shopping experiences; by analyzing point-of-sale data, foot traffic patterns, and customer preferences at the edge, retailers can optimize inventory levels, enhance customer satisfaction, and offer targeted promotions.
North America will attain a larger market share in the edge analytics market; predictive analytics have importance in the region and will increase the adoption of edge analytics solutions with a higher concentration of industrial and telecommunication industries. With the rising connection of IoT devices, the regional market has seen a surge in the adoption rate of edge analytics solutions across all verticals. Implementation of edge analytics to keep better track of the health of equipment and output rate and prepare the manufacturing plant to deal with any last-minute problems in production.
Various regional industries have identified the potential benefits and implemented them in specific use cases. For example, it is used in manufacturing for predictive maintenance and quality control. These industry-specific applications have contributed to the growth of the edge analytics market in the region. The region has specific regulations and standards such as data privacy laws and compliance requirements like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). It provides a solution to address data security and privacy concerns by processing sensitive data locally, thereby complying with regulatory requirements.
Edge Analytics provides the same capability as a traditional analytics tool, with the exception of where the analytics are conducted. The key distinction is that edge analytics programmers must run on edge devices that may be limited in storage, computing power, or connection. Digitization has been the driving force behind the most recent revolutions. Companies have long struggled with how to extract relevant insights from the millions of nodes of data created each day by IoT-connected devices. The amount of linked gadgets, from a smartwatch to a smart speaker, is increasing the volume of data to be mined. Many new technologies, like as AI and Big Data, have become indispensable for gathering insights.
North America will gain a larger market share in the edge analytics market due to an increase in the need for predictive analytics, which will increase the adoption of edge analytics solutions with a higher concentration of industrial and telecommunications industries. With the rise of IoT, there has been a surge in interest in edge analytics. For many firms, streaming data from different IoT sources produces a massive data repository that is challenging to manage.