Advanced analytics techniques
The advanced analytics process involves mathematical approaches to interpreting data. Classical statistical methods, as well as newer, more machine-driven techniques, such as deep learning, are used to identify patterns, correlations and groupings in data sets. Based on these, users can make a prediction about future behavior, whether it is which group of web users is most likely to engage with an online ad or profit growth over the next quarter.
In many cases, these complex predictive and prescriptive analyses require a highly skilled data scientist. These professionals have extensive training in mathematics; computer coding languages, like Python and the R language; and experience in a particular line of business.
Advanced analytics has become more common during the era of big data. Predictive analytics models — and, in particular, machine learning models — require large amounts of training to identify patterns and correlations before they can make a prediction. The growing amount of data managed by enterprises today opens the door to these advanced analytics techniques.