Consider the example of a driver of a smart car about to have a stroke. You can’t wait for the smart car data to upload to the cloud for analysis and then wait for a signal to return to the edge device to direct the proper action. The cloud is too far away to process the data and respond in a timely manner. Every data has a time frame before you can apply analytics, beyond which its value depreciates.

A relatively new approach namely Edge Analytics, addresses these issues. Edge analytics is an approach to data collection and analysis wherein an automated analytical computation is performed on data at a sensor, network switch or other device instead of waiting for the data to be sent back to a centralized data store.

Edge analytics is data analytics in real-time and in-situ or on site, where data collection is happening. It could be descriptive or diagnostic or predictive analytics.

With the growing popularity of connected devices through the evolution of Internet Of things (IOT), many industries such as retail, manufacturing, transportation, and energy are generating vast amounts of data at the edge of the network. For large-scale IoT deployments, this functionality is critical because of the sheer volumes of Data being generated.

It’s a model which is increasingly being rolled out. A recent IDC report for IoT found that by 2018, 40% of IoT data will be stored, processed, analyzed, and acted upon at the edge of the network where it is created.

So Why Edge Analytics?

The answer depends on the situation. It is all about bringing enterprise-class thinking from the cloud to the edge and to everywhere in between. It includes all the components and it requires incorporating enterprise thinking for developing the software for the devices or ‘things’.

Organizations are deploying millions of sensors or other smart connected devices at the edge of their networks at a rapid pace and the operational data that they collect on this massive scale could present a huge problem to manage. Edge analytics offers few key benefits:

  • Reduce latency of data analytics. In many environments such as remote manufacturing environments like oil rigs, aircraft, CCTV cameras, there may not be sufficient time to send data to central data analytics environment and wait for the results to meaningfully impact decisions to be taken on site in a timely manner. It may be more efficient to analyze data at the premises and get results.
  • Scalability of analytics. As the number of sensors and network devices grow, the amount of data that they collect also grows exponentially and it increases the strain on the central data analytics resources that process this huge amount of data. Edge analytics enable organizations to scale their processing and analytics capabilities by decentralizing to the sites where the data is collected.
  • Resolve the issue of low bandwidth environments. The amount of bandwidth needed to transmit the entire data collected by thousands of these edge devices will also grow exponentially with the increasing number of these devices. Many of these remote sites may not even have the bandwidth to transmit the data and analysis back and forth. Edge analytics alleviates this problem by delivering analytics capabilities in these remote locations.
  • It will probably reduce overall expenses by minimizing bandwidth, scaling of the operations and reducing the latency of critical decisions.

Will it replace centralized data analytics?

It is worth noting that edge-based analytics will not replace the centralized data center model. Rather it is an approach which can be used to supplement or augment analytics capabilities in certain situations, such as when insight needs to be acted upon very quickly.

Both can and will supplement each other in delivering data insights and both models have their place in organizations. The only concern is that edge analytics will process and analyze only a subset of data at the edge and only the results may be transmitted over the network back to central offices.

This will result in ‘loss’ of raw data that might never be stored or processed. So, basically edge analytics is acceptable if companies are OK with this data loss. On the other hand, if the latency of decisions (& analytics) is not acceptable as in-flight operations or critical remote manufacturing/energy, edge analytics should be preferred.

Edge analytics is an exciting area within the organizations dealing with Industrial Internet of Things (IIOT) area. Leading vendors are aggressively investing into this fast-growing area especially in specific segments such as retail, manufacturing, energy, and logistics. Edge analytics delivers quantifiable business benefits by reducing latency of decisions, scaling out analytics resources, solving bandwidth problem and potentially reducing expenses.