The global Edge analytics market research by Frost & Sullivan analyses the attractiveness, business benefits, and various technical and economic mega trends that are shifting the view of factories toward adopting edge intelligence for their operations.
This study also provides a significant view to readers by segmenting the market in terms of various components of edge analytics. This enables the reader to understand the market from multiple facets that include technology, services, and end-user segments. This research captures the widespread application of edge devices mainly in the Industrial sector.

Industrial Internet of Things (IIoT) is transforming how businesses work by enabling better real-time insight into operations. IIoT helps release insight from the ocean of data to create hard, actionable insights that drive clear business results. The endless stream of IIoT data can be seen as a valuable asset, but the insight derived has a much greater value than the data stream itself. How organizations utilize this information—including when and where they utilize it—will decide the measure of significant worth that they can bring to the business.

One of the ways organizations are beginning to gain more insight and value from IIoT data is by architecting for analytics at the “edges” of their environments.

Companies such as Cisco and Intel were early adopters of Edge computing. These companies positioned their gateways as edge devices. In the edge computing model, the gateways not only route the data but also perform computation on the data. Edge analytics enables preprocessing and filtering of data closer to where the data were created. However, many vendors such as Dell position their servers as the Edge device by adding computational and analytics capabilities.

What drives the need of edge computing and analytics?

Performance Requirements:  IIoT solutions often require rapid data insights. The transmission of large volumes of raw data increases latency and is sometimes not even feasible due to poor or underpowered network connections. There are certain cases where low latency is a requirement.

Opportunities for data preprocessing: Many a times, all the data generated by the solution are transmitted to the cloud for processing. Instead of sending raw data, transmission of processed data from the edge to the center makes more sense.

Distributed Applications: Applications such as smart grid, pipe line monitoring may have high level of distribution; thereby making analytics at the edge more suitable.

Analyzing data and applying insights can help manufacturers make data driven business decisions, drive innovation and identify new business opportunities.

This research analyses the impact of IIoT on the Edge analytics market, exploring innovative applications and signifying potential growth strategies and competencies that market players should be focusing on during the forecast period. The global edge analytics footprint is expected to rapidly grow during the forecast period due to emerging adoption of IIoT.

Geographically, this research analyses and forecasts the growth of Edge analytics in the major regions classified as North America, Europe, Asia-Pacific, and Rest of the world. This study also discusses in detail the major economic and technological drivers and their regional impact over the forecast period.

The global demand expected to rise during the forecast period.

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