IoT: Bringing Data Visibility

The pace of adoption of IoT is one of the unprecedented ones in the history of technology. Gartner forecasts that the number of things connected to the Internet will grow to 35 billion by 2020, and that 47% of these devices will have the necessary intelligence to request support. That’s more than 16 billion devices. It is also predicted that by 2021, one million IoT devices will be purchased and installed every single hour.

With the mentioned installation base, millions of dollars would be under spending every minute. But the more baffling figures comes from the amount of data exchange that would be happening. Imagine those billions of devices sensing & processing the data 24 hours, 365 days a year. It becomes more imperative for organisations to identify what to do with this pile of data coming every second. The data must be made visible across echelons of the organisation, and this needs to follow step-by-step process to maximise the utilisation. On the functional aspect, to make the data visible, the approach can be majorly mapped around three areas (though these can be drilled down to many levels further).

Identification of KPIs:

It must be first understood that it is the vision & strategy of an organisation that drives the digital transformation & not vice versa. Thus, it is very important for an organisation to identify what are the various parameters directly aligned to their business objectives and strategies. The amount of data available with the advent of IoT is huge, making it possible to track hundreds of indicators simultaneously. And hence it becomes more important for an organisation to pick & choose carefully from the several options available.

Target Data Source:

Once the parameters to be measured are identified, the next logical step is to identify the source for that data. We need to identify the various points where data is getting captured be it at enterprise level or device level. The data must give a complete overview of the system around the targeted parameter; thus, providing a comprehensive visibility across all levels of the value chain. It is also important to understand that the data source may not be limited to OEMs control vicinity but may well extend upstream & downstream in value chain.

Interpreting the data:

Once the data gets captured, transforming the same to business language is the next aspect one must consider. Data unless presented into acceptable & usable format is of little value. And this collation & representation of data must bring into intelligence gathered out of data received from all the diverse sources. It should act as an enabler to decision making rather than just reporting formats.

To truly gauge the visibility of data across the value chain, it must encompass the above stages mentioned; from identification of parameters to providing business value to all the users. This will increase the optimisation of processes throughout the chain and bring in efficiency into the system, benefiting not only the OEM but the partners as well. Hence win-win situation for all the stakeholders in the value chain.