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Edge Analytics Market size was valued at USD 11.1 billion in 2023 and is projected to register a CAGR of over 25% between 2023 and 2032. The market is driven by the significant growth in the adoption of IoT devices worldwide. Further, the rapid expansion of the IoT landscape, majorly driven by the increasing internet penetration, has led to a proliferation of connected devices generating massive volumes of data at the network edge.
According to the International Telecommunication Union, as of 2023, around 5.4 billion people have internet access across the globe. The number of internet users has grown by around 4.7% since 2022.
Report Attribute | Details |
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Base Year: | 2023 |
Edge Analytics Market Size in 2023: | USD 11.1 Billion |
Forecast Period: | 2024 to 2032 |
Forecast Period 2024 to 2032 CAGR: | 25% |
2032 Value Projection: | USD 85 Billion |
Historical Data for: | 2021 – 2023 |
No. of Pages: | 300 |
Tables, Charts & Figures: | 383 |
Segments covered: | Component, Business Application, Type, Deployment Model, Industry Vertical |
Growth Drivers: |
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Pitfalls & Challenges: |
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Edge analytics empowers real-time data processing and insights directly at the data source, eliminating the need to transmit all data to central cloud servers. This approach provides swift data processing and enables organizations to extract real-time insights. This is considered crucial for time-sensitive applications such as industrial automation, autonomous cars, and remote monitoring, driving the growth of the edge analytics market.
Further, the growing demand for autonomous systems across several end-use verticals is playing a significant role in fueling the edge analytics business globally. The technology is crucial for powering autonomous systems, such as autonomous vehicles and drones, by enabling quick decision-making and reducing dependencies on cloud connectivity for critical operations.
The major factor hindering the growth of the market is the limited computational resources of edge devices as compared to cloud servers which reduces the ability to scale analytics workloads. Also, the integration of a significant number of edge devices and ensuring they work smoothly together is complex and costly. Furthermore, edge devices are vulnerable to cyberattacks due to their weaker security measures as compared to centralized data centers.