市場調查報告書
商品編碼
1301277
邊緣分析的市場規模,佔有率,趨勢分析報告:各類型,各零件,配置模式,各用途,各最終用途產業,各地區,及市場區隔趨勢: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的最新報告顯示,全球邊緣分析市場規模在2023年到2030年將記錄25.3%的年複合成長率,到2030年達到407億1,000萬美元。
通過在邊緣設備本身上執行數據分析和處理,機器人可以快速響應其環境,而無需嚴重依賴集中式系統。這種方法提供了實時洞察、更低的延遲、更高的安全性和優化的帶寬。隨著物聯網的興起和邊緣生成的數據量不斷增加,邊緣分析受到廣泛關注。許多工業組織正在使用物聯網 (IoT) 來監控其製造機器、管道和設備。
物聯網生成和存儲難以實時管理和解釋的數據。來自物聯網設備的數據被發送到邊緣分析進行處理和理解。分析算法幫助人類決定需要什麼數據,不需要什麼數據。在許多應用和行業中,及時決策對於實現運營效率、確保安全和提供卓越的客戶體驗至關重要。自動駕駛汽車、工業自動化和智慧城市等某些應用需要實時分析功能。
邊緣分析可以在邊緣進行即時處理和決策,最大限度地減少延遲並實現快速響應。此外,無人機和機器人等行業嚴重依賴實時決策能力。這些系統必須處理大量傳感器數據並立即響應不斷變化的環境和情況。邊緣分析可以分析和解釋邊緣的傳感器數據,使這些自主系統能夠在不依賴集中處理的情況下做出快速、準確的決策。
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.