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Common Types Of Advanced Analytics That You Need To Know

Companies use different types of analytics to address their business problems. Like diagnostic and descriptive analytics, some are retrospective, while others, like business analytics, are traditional.

Businesses use these analytics for general reporting and knowing why and what happened. They also help enterprises to develop KPIs and other business intelligence metrics. However, analysis can’t be complete without advanced analytics. In this article, you’ll learn what it is and its various types.

What’s Advanced Analytics?

This term refers to data mining, prescriptive analytics, predictive analytics, and other analytic types that use advanced data science to make predictions. Advanced analytics gives companies a more in-depth and more advanced insight into the trends, patterns, and themes hidden within data. These enable organizations to understand their customers better, reduce risks, and predict future outcomes.

There are four types of advanced analytics, namely:

  1. Data Mining

Data mining is considered the foundation of advanced analytics because it enables entrepreneurs to discover knowledge within databases. You can use data mining to thoroughly comb through large data sets to uncover trends, patterns, and other hidden insights that aren’t visible through different analytics types. It’s done through statistics and machine learning.

There are several data mining techniques, but the common ones include clustering analysis, anomaly detection, regression analysis, and association rule mining. Clustering analysis deals with similar large groups of data sets, while anomaly detection analyses rare data points outside an established average or group. Associate rule mining and regression analysis analyze how variables relate to the others.

  1. Predictive Analytics

Predictive analytics digs into what happened and why it happened by analyzing historical data. It predicts future results through statistical techniques such as predictive modeling, data mining, and machine learning. It mines data from systems such as ERP, CRM, and marketing automation stacks. The outcomes are then visualized in ways that business owners can easily interpret.

If you want to gain a competitive advantage over your competitors using this type of analytics, it’s vital to use predictive analytics software. That’s because it’ll enable you to locate new business opportunities, understand various customer interests, address problems before they occur, and minimize risks and costs.

  1. Prescriptive Analytics

Once predictive analytics has given you data that you can use to determine what will happen next, prescriptive analytics will help you to calculate your next steps. Among advanced analytics types, it’s the most actionable. It utilizes structured and unstructured data to form its insight into the complex world of data science. It’ll also allow you to use computer vision, applied statistics, and deep learning to interpret various data sets. Due to its high barrier entry, this type of analytics is commonly used by most businesses today.

  1. Big Data Analytics

Big data analytics has become popular because a lot of data that can outpace a business’s ability to capture and analyze is being generated. Because most data generated is unstructured, you can’t process and analyze it through conventional methods. Instead, you need big data analytics software and other technologies to interpret and make sense of such data, including audiophiles, images, social media posts, videos, and other multimedia.


Advanced analytics is vital because it enables businesses to draw insights from unstructured and structured data through statistical modeling, data mining, and machine learning.

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