We are headed towards a more connected, more instrumented and more data driven world. This fact is underscored once again in Cisco’s latest Visual Networking Index. The statistics from this report are truly mind boggling.
According to Cicsco, by 2016, 130 Exabytes (130 * 2 ^ 60) will rip through the internet. The number of mobile devices will exceed the human population of 7 billion this year. By 2016 the number of connected devices will touch almost 10 billion.
The devices that are connected to the net range from mobiles, laptops, tablets, sensors and the millions of devices based on the “internet of things”. All these devices will constantly spew data on the internet and business and strategic decisions will be made by determining patterns, trends and outliers among mountains of data.
In this future of swirling data, predictive analytics will be a key discipline and experts in this domain will be much sought after. Predictive analytics uses statistical methods to mine information and patterns in structured, unstructured and streams of data. The data can be anything from click streams, browsing patterns, tweets, sensor data etc. It can be static or it could be dynamic. Predictive analytics will have to identify trends from data streams from mobile call records, retail store purchasing patterns, social network status messages etc.
Analytics and predictive analytics will be applied across many domains from banking, insurance, retail, telecom, energy. In fact predictive analytics will be the new language of the future akin to what C was a couple of decades ago. C language was used in all sorts of applications spanning the whole gamut from finance to telecom.
While analytics can mine data for patterns, trends and outliers, predictive analytics can model the behavior of the system under study and come up with future trends and outcomes.
In this context it is worthwhile to mention The R Language. R language is used for statistical programming and graphics. Wikipedia defines R Language as a language that “provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others”.