How to make IOT

Internet of things (IoT) has proven to be a tremendous influencer in driving the fortunes of businesses across the globe. From kitchen appliances and coffee makers to security cameras and air quality sensors, every device or appliance now holds the potential to spew valuable data by connecting to machines or systems. The number of such connected devices too is set to witness a substantial jump in the next few years. Projections range from moderate (IHS predicts 30.7 billion connected devices by 2020) to aggressive (Intel projects 200 billion connected devices by 2020). Whatever the number, the truth remains that IoT is pervading our lives whether we are ready or not.

What can a company do for IoT analytics readiness?

Here is what a company can do:

1. Factor in the intricacies of an IoT environment

The IoT environment spans multiple networking architectures like 3G/4G and peer-to-peer networks. It also covers protocols like MQTT, CoAPP, or BLE. These intricacies of the IoT environment means that your IoT analytics planning needs to have the flexibility to scale up or down depending on the load of inbound data and analytics needed.

2. Gateway level IoT analytics is needed

Inter-connected devices can grow very big very quickly. Hence, rather than overloading servers and networks, some analytics need to be done at the edge, at the gateway level. This allows faster recognition of patterns to act upon.

3. Factor in uncertainty in variables

Variables like nodes failures due to low bandwidth, duplicate messages, and latency in data collection can alter your analytics plan substantially. Be prepared and more importantly, factor in these uncertainties into your IoT framework for better management of real-time data. This means re-configuration of components like algorithms and sequence based queries to ensure the correct proportion of inbound data coming in through IoT nodes.

4. Embrace the power of prediction

IoT implementations need to go beyond basic mathematical functions and bring in predictive analytics. This will yield amazing outcomes to sectors like proactive maintenance, fraud detection, and predictive healthcare. Machine learning too can be used to complement statistical models to see patterns and learn from it.

5. Have a Plan B ready

Because of the huge data explosion from multiple sources and connected devices, we are faced with a crucial question. “Do we know which data needs to be accessed and used, to know the ‘what’, ‘where’ and ‘how’ of connected devices?” IT admins will see a quick jump in the number of devices and things that will be accessed in the IT infrastructure. Hence you need to have a Plan B in place to ensure that such load spikes can be accommodated without compromising budgets or processes.