Big data is renowned for its ability to transform, refocus, and even create new business where none existed before. Its advantages and potential are widely recognized across multiple industries. The drawbacks and disadvantages of big data in modern business are typically well understood too. Time, costs, and the bandwidth requirements of cloud computing can keep many companies from using data analysis to its greatest potential.
The genius of edge analytics is in flipping the whole model of big data and cloud computing completely on its head. While conventional computation uploads vast amounts of data to the cloud, edge computing works to perform some or all of its analysis at the point of collection.
Processing Local Data Locally
Devices at the periphery or ‘edge’ of the network can do some, or all, of the data processing before uploading to the cloud. Analysis can range from simply filtering out irrelevant data to performing a ‘first pass’ analysis to gather additional business intelligence.
By using ‘smart’ devices over basic sensors, the network can make decisions about what data to send to the cloud, what to log for later analysis, and what to discard altogether. While not as powerful or complete as a cloud computing solution, early data filtering and analysis ensures only relevant and useful data is sent to the cloud. More and more, edge analytics are used to guarantee efficient use of the most valuable resources on the network.
Edge computing can provide a power and efficiency boost to systems by utilizing:
- Real-time data processing
- Basic analytical capabilities
- Data caching and buffering
- Data filtering
- Machine-to-machine direct communications
Practical Benefits of Edge Computing
One of the prime advantages of edge analytics is its ability to create inherently scalable systems. Companies often find themselves overwhelmed by data as more and more of it is captured and generated. As data needs increase, companies are forced to expand their storage, processing, and bandwidth capabilities at a rate many struggle to keep up with.
Edge analytics can provide a competitive advantage which solves these problems as data needs grow. Edge devices delegate some data processing to machines located close to the point of data input. This solution adds devices to the network at a steady rate. By handling initial analysis at the nodes rather than a central location, the capabilities of the system grow proportionally to its size.
Computation on edge devices can happen on any combination of varying data inputs. Whether data is local, downloaded, or generated in real-time, edge devices can combine feeds to produce new insights close to where they are needed.
For many use cases, the latency supplied with accessing big data resources is simply unacceptable. Data shared between devices with common interests can reduce latency by sharing local data directly between devices. Simply eliminating the time to upload and download critical data alone can produce staggering reductions in overhead which are vital to a number of systems.
Some prime use cases for implementing edge computing include:
- Autonomous systems which require instant feedback
- Commercial systems requiring a highly responsive user interface
- Regulated systems with strict guidelines on data ownership and privacy
- Remote systems with restrictive bandwidth constraints
Real-Time Efficiency Improvements
A warehouse with multiple autonomous production lines and automated vehicles is a perfect use-case example for the benefits of edge-analytics. It would be unwise and unsafe for every system to wait for a shutdown command issued by a central processing server in the event of a failure or emergency.
Even in everyday use, waiting idly for a central data link to control every production line is prohibitively costly and massively time-consuming. Smart, interconnected devices controlling machines upstream or downstream of its line can play a huge rule in improving efficiency, safety, and regulatory compliance.
Machines with the power to analyze and report on their own efficiency, throughput, and failures have the power to provide transformative productivity insights. While conventional computing talks about linear one-to-one relationships, the power of a web-like structure with computational nodes at every point can unlock a whole host of new potential.
Edge analytics is essentially about harnessing the power of interconnected devices. It’s one way in which big data can maintain scalability and performance while the amount of data we collect continues to grow at an exponential rate.