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Demand for Low-Latency Real-Time Insights Drives Demand for Edge Analytics
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This study focuses on the global edge analytics software market. Edge analytics solutions utilize devices such as routers, sensors, integrated access devices (IADs), and multiplexers to sift through and rank large amounts of data. Edge analytics software analyzes data on the device, reduces latency, eliminates the need for large amounts of data transfers, maintains data privacy, provides real-time actionable insights locally, reduces cost, and increases scale. Several Big Data analytics (BDA) vendors expand their product portfolio to include edge analytics solutions and provide insights in real-time, rather than moving data to a central hub for analysis. Edge ecosystem vendors collaborate to push the technology forward and compete to gain first-mover advantage across industry verticals and locations. There is a consensus that a single vendor cannot deliver on all future use cases; collaboration is becoming the key to market expansion. Key revenue contributors in the market include SAS, C3.ai, SAP, Rockwell Automation, and FogHorn Systems. Other market vendors include Swim.ai and Guavus. Edge vendors such as Siemens, Intel, SiSense, and NVIDIA are still piloting edge software solutions driven by use cases; however, they have not been included in the analysis this year. Additionally, IBM, Microsoft, and Amazon Web Services (AWS) provide open-source solutions or software development kits to support the developer community in creating edge analytics solutions. The need for real-time insights drives the market growth. In manufacturing, factories are dependent on uptime; to predict machine maintenance needs, they process data on the edge to avoid bandwidth and latency issues and draw real-time insights for timely action. The market is also affected by organizational competition, dissatisfaction with BDA solutions, and the new revenue generation through data monetization. However, the market has been unable to reach its full potential due to insufficient data hygiene, difficulty proving return on investment, data security challenges, lack of data standardization, and a shortage of skilled labor.
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