Most data is never analyzed. Most companies are not data-driven. Both will have to change if companies are to be successful at IoT. But there are analytical challenges with IoT data.
The surge of IoT data comes with a lot of economic value, estimated at around $11 trillion by 2025. But it also comes with some significant challenges in terms of aggregating data from disparate, distributed sources, and applying analytics to extract strategic value.
- The primary challenge of IoT data is its real-time nature. Analytics will have to happen in real-time for companies to benefit from these types of data.
- Then there is the issue of time series data. The system must be capable of collecting, storing, and analyzing vast volumes of time series data. The challenge here is that most conventional databases are not equipped to handle this type of data.
- The distributed nature of IoT data, where most of the information is created outside enterprise data centers. IoT analytics itself, therefore, will have to become distributed with some analytics logic shifting out of the cloud to the edge. IoT analytics will have to be distributed across devices, edge servers, gateways, and central processing environments.
Not highlighted by this article is the challenge to acquire and get the data into the system at all. Most legacy systems, and even those that call themselves “smart” or “IoT” are proprietary solutions that lock in data and events in their environment or service, so-called data silos. It is just tough to re-use this in a broader application, like BI.
And then comes the problem of incompatible standards, hundreds in IoT, especially when you try to connect things from a different domain. How do you connect a BACnet speaking device in an MQTT driven platform, that should control another device talking Z-Wave, for instance?
What you need is a swiss army knife type of multi-standard IoT tech integration service. You need Yggio. Or you won’t have a complete foundation for the analytics at all.