With an avalanche of data already pouring in from various devices - thanks to the increasingly popular Internet of Things (IoT), the importance of analytics cannot be stressed enough. The volume of data from non-human sources - gadgets, wearables, and other devices - is increasing every day, and it calls for treating that data with care and precision, for the right purposes. This is where invisible analytics comes in.
Big Data is an enabler of this trend. There are vast pools of structured and unstructured data within and outside enterprises that are yet to be analyzed. Analytics aims to find new insights and ideas in the data already present, rather than harvesting more data. This was understood early on when a well-known supermarket chain decided to combine data from its loyalty card systems and point-of-sale systems. What they found was an eye-opener for data analysts. While a lot of correlations in the study results were expected, such as people who buy gin also buy tonic water, there was a correlation that was completely surprising-beer and diapers: analysis showed that young males, once they become fathers, continue their tradition of Friday night drinking, but also carry diapers to care for their young ones. The supermarket chain then proceeded to modify their retail offers based on this data-backed insight.
This is exactly what modern technology must do to serve customers well. All new mobile and other applications must include analytics to back their core function if they aspire to becoming an asset for the consumer. Earlier, Big Data was the trend, with many consumers keeping track of new data derived from tracking exercising hours, sleep quality, calories consumed and burned, and more. But, as we look to the future, more products are leaning towards what can be done with the large amounts of data already available.
For instance, an application that tracks statistics of activities such as running and cycling earlier provided only the distance, the route taken, the speed at which the user was moving and so on. While this was informative for the user, it did nothing more. New apps today are trying to answer the 'What more?' question. They are delivering analyzed information: in addition to tracking statistics related to an activity, apps are also suggesting how far someone must run to achieve a fitness goal, based on heart rate and blood pressure details.
Organizations now need to follow the transition that Big Data made into advanced and invisible analytics. They must strive to filter and analyze received data and deliver the right solution for the right question to improve sales, marketing and customer experience. Customer experience has moved beyond just tracking details, and is now transitioning into a more responsive and interactive environment based on data input. The value is in the analysis, not the data.
What are your thoughts on this transition of Big Data into analytics? Please share your comments in the section below.