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. Analytics people might have a lot of innovative ideas about analyzing the data and getting wonderful insights from it. But the ground reality is, unless we have a proper infrastructure and skill to acquire and analyze necessary data, AoT is meaningless.
Now, let us divide the challenges into two broad categories, one is on the organizational side and the other is on the technology and implementation side.
Let’s start with the organizational challenges first.
The most important challenge is to build a solid AoT business case to convince the organization. It will ease the investment and future nurturing of AoTvision.The first investment is required to deploy the IoT devices in proper places with sensors to capture data. Once the devices are ready, organizations need to enable the data movement from sources (IoT devices) to destination (may be a staging DB or data warehouse or some other storage). Finally, a proper strategy has to be built to figure out how the storage and analytics can be managed.
Think, for example, about a “smart” thermostat, now available from a variety of vendors. These thermostats sense not only room temperature, but also whether people are in a room, their patterns of activity during the day, and so forth. In order to make sense of such data and take action on it, smart thermostats have embedded analytics that help them decide when to turn themselves up or down. So they’re smart enough—even without being connected—to save energy with little or no user involvement.
Smart thermostats can also be connected to the Internet through wifi, and there are some potential benefits from doing so. Remote monitoring and control is one. I can turn up my thermostat during my trip home from work, or check remotely to make sure my pipes won’t freeze.
This is useful for controlling remote devices, but connection also yields more data and more potential for analytics. The primary virtue of connected analytics is that you can aggregate data from multiple devices and make comparisons across time and users that can lead to better decisions. Comparative usage of an important resource such as energy, then, is one key analytical approach to connected data.