Every technologist’s dream is to make a machine, which is even more intelligent than human beings themselves. However, we have not reached that milestone yet, but intelligent machines are throwing up several opportunities. The emergence of global tech giants including Amazon, Google, Uber, Apple, has completely changed traditional business models in area of selling books, advertising, music industry or taxi ride. All these companies are sitting on massive amounts of consumer data. This has given rise to creation of disruptive business models, moving away from providing an omni channel customer experience to always connected customer experience. 

A recent study by NASSCOM revealed that the IoT market in India is expected to reach USD 15 billion by 2020. The devices connected to internet are not a new concept. In fact, every industry is witnessing a plethora of possibilities with IoT. However, what has changed is that these platforms are getting standardized. Experts claim that by 2020, there will be 70 billion devices connect to internet. These devices are expected to generate zeta bytes of data. Now this data is a gold mine to understand customer behavior.  Typical problems organizations face today is that they lack correct mechanism to collect customer feedback. With their customers connected to internet at every touchpoint, companies will automatically have access to all customer data, which can then be analyzed to understand patterns and derive insights.

IoT is the final frontier in connecting customers to companies in order to understand their behavior.  IoT data marketplaces can provide aggregated anonymized information, as third party data. Consider this - a person taking a vacation at Disney land is likely to buy new luxury car model. If he/she loves adventure rides in theme park, they can be presented with adventure or off beat vacation options as well.

Millions of customers on millions of connected devices generate massive amount of data. This data coming from IoT devices meets all three V’s of big data technologies - Variety of data, Velocity of data and Volume of data, which in turn further necessitates building an effective big data platform to process this data and make it available for analytics.

Humans are good at visual intelligence. When presented information visually, we can decipher patterns in the underlying data, but processing large amount of data on its own becomes difficult. This is where machine learning helps in predicting customer behavior using predictive analytics. These technologies can help operationalize predictive models.

Machine learning methods like supervised and unsupervised learning can be applied on huge data sets. The process involves selecting correct algorithm or selecting right mix of machine learning algorithms using ensemble methods to improve model accuracy. This information can be fed back to improve Machine-to-Machine intelligence or Machine to Human Intelligence.

We have seen how new-age technologies like IoTare re-defining the way businesses, across the globe, generate and consume data. Successful businesses need to build the ability to convert data into insights and use that for improving customer experience or to drive new operational efficiencies to gain competitive edge. As devices become smarter, organizations will have to continue innovating and experimenting with the ongoing flux to transform their businesses and the industry at large.