Differentiation between Edge and Cloud-Based Analytics

Big data analytics continues to be a key factor advancing innovations within security and surveillance platforms for financial institutions. A prominent discussion in the market today centers on video analytics and implementations for the “edge” or the “cloud.”

Cloud-based analytics is the use of remote public or private computing resources (aka the “cloud”) to analyze data on demand, and edge-based analytics is the application of video data analytics that is processed within a camera (IoT on the edge).

A great starting place for this topic is to consider the differences in the technical approach…

Analytics Must Drive Security Improvement Outcomes

Video based data analytics is the science of analyzing raw surveillance data to find trends and outcomes that can be transformed into useful security operations-business intelligence. It allows security and fraud investigation managers to make sense of vast amounts of security video data and identify risks or abnormal activity—and thereby enable the execution of actions to remediate security breaches and risks.

The adoption of analytic applications is especially beneficial in banking, where fraud and malicious theft attempts are rife. The rise of analytics is more critical than ever to help ensure compliance and productivity.

The question is, with so much video data being created and so much to analyze, how do financial institutions choose the most efficient and effective technology approach: edge or cloud?

Here, we will analyze the differences between cloud-based analytics and edge-based analytics, considering their strengths, weaknesses and potential synergies.

Cloud analytics

Cloud computing has become one of the most widely acclaimed technologies of our generation, and for a good reason.

Its centralized nature provides easy, seamless remote access while also maximizing security and control. As a result, it is transforming business models globally, allowing authorized users to access the information and applications they need anytime, anywhere, with ease.

The cloud’s core strengths come in the forms of power and capacity.

While the edge may suffer from limitations on these fronts, the cloud is able to seamlessly perform large-scale analytics projects while incorporating machine learning and AI. Furthermore, it allows for ease of flexibility and scalability—supporting the growth and ambitions of companies in an agile way.

There are some drawbacks, however. Because the data within the cloud is centralized, it can be difficult to transfer large video data files from the edge of the network to the cloud at fast speeds. Despite this, the use of cloud technology is non-negotiable to stay relevant.

While all financial institutions utilize the cloud in general, for security, storing video data in the cloud provides the ability to fuse data with other cloud sources to solve problems that edge-based analytics alone cannot solve.

Edge analytics

Conversely, speed is the outstanding benefit of edge analytics, supporting applications where computational processes need to happen in mere fractions of seconds thanks to a lower latency of responses, where the cloud might otherwise be too slow.

Thanks to advances in technology, today’s IP cameras increasingly have the processing power to run AI or deep learning-based analytics and algorithms such as facial recognition. The benefits of processing data on the camera include lower bandwidth consumption—a  significant boon for financial institutions struggling with upstream bandwidth.

Additionally, processing video data on the camera itself can sometimes lead to faster processing and alarm response for a suspicious incident (or event).

Edge-based analytics versus cloud-based analytics

Organizations worldwide are increasingly reaping the benefits of today’s cloud-based video analytics, with the latest camera platform capabilities providing a bird’s eye view of their entire organization. Providing fast and easy access to data from any location, this innovative technology is improving security-business intelligence.

Integrated with building management and other systems, managers are leveraging cloud video surveillance platforms to boost operational efficiencies while enabling safer and more productive environments for occupants.

It’s worth noting there are certain instances where edge analytics will be preferred, and other scenarios where centralized, large-scale cloud-based data analysis that is not time-sensitive makes more sense.

For those organizations that need real-time data analysis for real-time applications, it may be worth considering using edge as an extension of the cloud rather than a replacement. This hybrid approach may be an ideal solution for financial institutions looking to upgrade their legacy surveillance systems.

While edge-based analytics and cloud-based analytics have different, distinct purposes—they don’t have to be pitted against each other. The benefits of both systems can be leveraged as needed, and the edge can be used to make the cloud stronger.