From a public-discussion standpoint, data mining has ceded the advanced analytics spotlight to more au courant components such as data science, predictive modeling and machine learning. But even in the shadows, data mining tasks are still a crucial part of the analytics process, which is taking center stage in more and more organizations that want to use data to light the way for their business units.
To switch metaphors, data science and analytics applications “are the engines of the future,” six Gartner analysts jointly wrote in the consulting company’s “2017 Magic Quadrant for Data Science Platforms” report. They cited several reasons for the stronger focus on data science efforts, including increased computing power and wider availability of machine learning tools.
Author: Craig Stedman