Advanced analytics is an umbrella term encompassing predictive analytics, prescriptive analytics, data mining, and other analytics using high-level data science methods.
Advanced analytics provide deeper, more advanced insight into patterns, trends, and themes that may be hidden within data. This allows businesses to understand their customers on a deeper level, predict future outcomes, reduce risk, and more.
Data Mining
The purpose of data mining is to sift through large datasets to uncover patterns, trends, and other hidden insights that may not be clearly visible. This is done using machine learning and statistics. Think of data mining as a deep-dive into data analysis.
Text Mining
Another type of data mining, called text mining, is an advanced method of extracting high-quality information from text data on apps and the Web. It is sometimes referred to as text analysis.
Predictive Analytics
Predictive analytics mines data from systems like CRM, ERP, marketing automation stacks, and other databases. The results are then visualized in a way so key business users can interpret them.
Prescriptive Analytics
Prescriptive analyses are complex in the world of data science, often using both structured and unstructured data to form its insights. Applied statistics, deep learning, computer vision, and other advanced methods are used in prescriptive analytics.
Big Data Analytics
There is so much data generated today, it’s outpacing our ability to capture and analyze it, that’s why big data analytics has become so prominent over recent years.
The finance industry currently works with quantitative models – using big data – to predict enter/exit trade decisions and minimize risk. Large retailers use big data to forecast demand for certain products. Tech-focused education analyzes big data to explore new, more tailored learning options for students.