Data-Driven Chance Discovery

Abstract

There is more and more data produced by human beings, but also from automated sources: social media platforms, web blogs, search engines and other applications are just the tip of the ice berg. The same applies to data from businesses, there are data streams that offer market prices, feeds provide news articles, governments publish law regulation changes and journals supply the latest research trends. The outlook towards the internet of things, where every device is connected through the means of a network, enforces the assumption that the data amount is not going to stop rising any time soon. This food of data does not only cause IT technicians sleepless nights, more so the managers, who are in charge to overview, interpret and discover potential market demands based on the data. Because of the highly divers sources,in terms of origination, frequency and content it is complicated for decision makers to stay on top of all the latest developments. Furthermore the current top-down approaches of how to identify new potentials in that kind of data, makes it even more difficult. Enterprises and their managers attempt to handle this by delineation and create artificial boundaries to break the complexity of the content down. But what if there are changes outside these boundaries? The very likely answer is, that businesses are not aware of it and that is not a smart answer. Hence, in these times of data overload the current approaches of business to handle it are no longer feasible....