Edge analytics – The pros and cons of immediate, local insight
The Internet of Things (IoT) brings businesses many benefits, primary access to more data and better insights. However, most companies we have spoken with are still largely puzzled by what to do with their data. Whether they should store or discard enterprise data, and if stored, what the best approach is to making that data a strategic asset for their company.
Gartner estimates that there will be 25 billion “things” connected to the Internet by 2020. The sheer size and speed of data collected when every device involved in your business process is online, connected, and communicating can strain the sturdiest network infrastructure. As such, despite the widespread proliferation of sensors, the majority of IoT data, collected is never analysed, which is tragic. Many existing IoT platform solutions are painfully slow, expensive, and a drain on resources, which makes analysing the rest extremely difficult. Furthermore, in situations where timing is critical, delays caused by bandwidth congestion or inefficiently routed data can cause serious problems.
The key takeaway is that data is the most valuable asset for any company. So it would be a shame to completely discard or let it lie dormant in an abandoned data lake somewhere. It’s imperative that all data scientists tap into their swelling pools of IoT data to make sense of the various endpoints of information and help develop conclusions that will ultimately deliver business outcomes. We are totally against of discarding data without processing.
In few years there will be an additional 15 to 40 billion devices generating data from the edge vs. what we have today. That brings new challenges. Just imagine an infrastructure transferring this data to data lakes and processing hubs to process? The load will continue to rise exponentially over coming months and years, creating yet another problem of stretching the limits of your infrastructure. The only benefit of this data will come from analysis either it is traffic of “things” or surveillance cameras. In time-critical situations, if we delay this analysis it might be “too late”. The delay could be due to many reasons like limited network availability or overloaded central systems.
A relatively new approach to solving this issue is called “edge analytics”. It is as simple as to say, perform analysis at the point where data is being generated (or analysing in real-time on site). The architectural design of “things” should consider built-in analysis. For example, sensors built into a train or stop lights that provide intelligent monitoring and management of traffic should be powerful enough to raise the alarm to nearby fire or police departments based on their analysis of the local surroundings.
