As we all know, data analytics is the process of collecting, engineering, analyzing of data's from an organization to gain insightful information which can help in the growth and development of the organization. Since the introduction of Data Analytics, various new terms and trends were introduced to us such as machine learning, artificial intelligence, Iot etc. which works hand in hand with data science and this field is flourishing more greater than before. These days, more and more companies depend on the ever growing mountains of data and hence we now have Edge Analytics to help companies in solving their problems much more faster and efficiently.
So What IS Edge Analytics?
The typical definition of Edge Analytics states that
“ Edge analytics is an approach to data collection and analysis in which 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.”
A simpler way of understanding this is, In any manufacturing type of industry, there will be a huge production line fitted with various devices and sensors to come together to complete the whole process. Normally what would happen is, The data's produced by each of the sensors or switches are collected at a central data storage and it is then worked upon by the analysts. But when it comes to large industries where LARGE AMOUNTS of data's produced each hour, the typical way of analysis proves to be tedious.
In Edge Analytics, the analytical computation is done then and there at the position of the sensor rather than sending the data to the central location to be worked on later. This is a BIG BOON for majority of the cooperation's.
With the advancement in technologies such as IoT, a large number of sensors and actuators come into play and these devices large amounts of data every hour. Managing ALL the data's produced by these devices is almost impossible in fact, in such cases, majority of the data's go untouched or unused.
But when it comes to EDGE ANALYTICS, every data is processed, managed and analyzed right at the spot or in other words, the data's are managed in real time which is definitely a HUGE BREAKTHROUGH!
Advantages of Edge Analytics
- Better Security: Since the data collection and analysis is done on site by an automatic computer program and does not involve the transfer of data from one point to another, we can expect our data to be more safe and better protected.
- Improved Scalability: Since the data management is executed on point rather than accumulating it at the central data storage, the workload is easily distributed and hence highly scalable.
- Real time data processing: As discussed earlier, Since all the work is done on site, on the board itself, The data is received, worked on, and analyzed at almost real time. The problem of latency is nearly eliminated.
- Cost effective: Since most of the data works are carried on spot, the cost of cloud management, storing and all other back end works can be cut down or eliminated. Even the maintenance expenses of edge devices are way less compared to cloud.
- Predict Failures: The computational power of edge analytics is in such a way that, each device or sensor is handled by an edge device and hence it is possible to accurately predict failures or issues in advance.
There is A LOT more to discuss about edge analytics here but taking everything into consideration so far, this is one tech that's going to find its place in many industries such as automation, healthcare, IOT, Security Solutions and what not. There are a lot of things that still needs to be worked on, For example, the fact that this whole concept is still developing means many of the devices wont be compatible with this process and developers might need to develop their own algorithms based on their requirements. The advantages outweigh the disadvantages and hence i guess its safe to say that Edge Analytics is here to stay!