Edge analytics vs.
cloud analytics:
Which one is
right for your
business?

With more data available than ever before, businesses of all types increasingly face the challenge of analyzing and processing that information. As a result, organizations are asking: How can we ensure we make the most of the available data, gain relevant insights and make informed decisions?

For the enterprise, edge analytics and cloud analytics are two different models for handling data. While there are similarities between both approaches, they differ in some crucial ways. If you're wondering, "What is edge analytics?" or "What is cloud analytics?" this article will explore each model and its unique benefits to help you decide which is best for your needs.

Before we get to "What is edge analytics?" let's focus on cloud analytics. "Cloud analytics" refers to the process of moving data processing and storage to a public or private cloud network for the purposes of gaining valuable business insights.

For many organizations, cloud analytics provides a better alternative to using on-premises resources to store and process data.

Cloud analytics allow you to move away from your own expensive, bulky data storage hardware. You could save significant amounts of space and money by moving to cloud analytics, paving the way for operational scalability and rapid business growth without incurring massive costs.

Cloud analytics can also help you easily compartmentalize and manage remotely. Large chunks of data can be managed efficiently, plus, services can be turned off and on as needed, so you only pay for what you need and avoid using more IT resources than necessary.

While cloud analytics requires fewer resources than on-premises alternatives, you'll still need access to a powerful cloud data center.

So, what is edge analytics? In short, it allows you to deploy computing resources right at the edge of the network. This way, you can analyze data in near-real time closer to the point where it is collected, using devices like sensors and cameras without needing to transfer the data to a centralized data center.

The result of this near-real-time analysis is a manifest reduction in latency, allowing you to gain rapid insights and respond in near real time locally. Moreover, the network devices used in edge analytics help take the strain off your existing analytics systems, helping you scale more effectively.

While both options may appear similar, there are crucial differences. The primary differentiator is that cloud analytics requires data to be transmitted to the cloud, whereas edge analytics can analyze the data locally. As a result, edge analytics can be much faster. And, as edge analytics requires less bandwidth, it can be deployed in places where bandwidth is limited.

On the other hand, the cloud is better at in-depth processing of large amounts of data when time isn't such a pressing constraint. Cloud typically offers more processing power for more detailed and thorough analysis.