without the AoT, it is difficult to realize the full potential of the IoT. We review the promise and challenges of analytics of Things, including data, security, analytics implementation, standartization, and more.
After the Internet of Things (IoT), the Analytics of Things (AoT) is the next logical step for the enterprises. In fact, without the AoT, it is difficult to realize the full potential of the IoT. It is not enough to just accumulate a lot of data from devices, but enterprises need to make sense out of the data and do something that makes these devices more efficient. Also, the data generated have the potential to improve a lot of things about the business. This is where AoT is so relevant. Businesses need sound analytics that improves the ways it runs the business and overall, the bottom line.
However, as enterprises plan to turn to AoT, they face numerous challenges on the way. IoT itself is still evolving and AoT is in its infancy, so there will be a lot of confusions and misconceptions leading to wrong investments of money and effort. Enterprises need to invest on technology and skilled manpower to get the best out of AoT. A lot of time and patience is required on the way. The question will be, how many can sustain the tempo for that long?
AOT – What does it actually mean?
Analytics of Things is nothing but IoT analytics. In simple terms, AoT means generating analytics from the data generated by the IoT. IoT means that several devices are connected to the Internet and are transmitting data to somewhere. Now, just obtaining the data is the first step. Enterprises need to analyze the data to make the devices smarter and more efficient. The result of IoT analytics is also used to make right decisions in different situations.
Now, if we exclude the ‘Things’ part from the term AoT, then the rest is only ‘Analytics’ ,which is quite similar in nature to any other data analytics. Here the ‘Things’ are nothing but IoT devices.
Similar to other data analytics, AoT can be of different types like descriptive, diagnostic, predictive or prescriptive.For example, diagnostic and prescriptive analytics can be done with the help of medical IoT devices and predictions can be made based on the data generated by the industrial IoT devices etc. But, we must remember that all these forms of IoT analytics/AOT are still evolving and requires significant amount of time and effort to get real business value.
What are the challenges?
When we talk about the 'Analytics of Things', there are mainly two parts in it, one is the analytics part and the other is the data collection part, generated by the things/connected devices. The analytics part is reasonably matured but the biggest hurdle is the data collection part, which the analytics world is facing for years. So, we are actually iterating the same old problem while pursuing AoT.
