Data analytics helps individuals and organizations make sense of data. Data analysts typically analyze raw data for insights and trends. They use various tools and techniques to help organizations make decisions and succeed.

Prescriptive Analytics: Prescriptive analytics, along with descriptive and predictive analytics, is one of the three main types of analytics companies use to analyze data. This type of analytics is sometimes described as being a form of predictive analytics but is a little different in its focus.

The goal of prescriptive analytics is to conceive the best possible recommendations for a situation as it is unfolding, given what the analyst can determine from the available data. Think of prescriptive analytics as working in the present, while predictive looks to the future and descriptive explores the past.

Diagnostic Analytics: This type of data analytics is used to help determine why something happened, diagnostic analytics reviews data to do with a past event or situation. Diagnostic analytics typically uses techniques like data mining, drilling down, and correlation to analyze a situation. It is often used to help identify customer trends.

Descriptive Analytics: Similar to diagnostic analytics, descriptive analytics looks to the past for answers. However, while diagnostic analytics asks why something happened, descriptive analytics asks what happened?

Summary statistics, clustering, and segmentation are techniques used in descriptive analytics. The goal is to dig into the details of what happened, but this can sometimes be time-sensitive as it’s easier to do a descriptive analysis with more recent data.

Predictive Analytics: Predictive analytics attempts to forecast the future using statistics, modeling, data mining, and machine learning to hone in on suggested patterns. It is the most commonly used type of analytics and typically focuses on predicting the outcome of specific scenarios in relation to different potential responses from a company to a situation. There are different types of predictive analytics models, but usually, they all use a scoring system to indicate how likely an outcome is to occur.

Cyber Analytics: With a combination of cybersecurity skills and analytical knowledge, cyber analytics is a new and rising proficiency within the business and data analytics industry. Cybersecurity threats have escalated in volume and sophistication, while the number of internet-connected devices continues to burgeon. Cyber analysts answer the demand for big data sifters with an I.T. background. Cyber analysts use sophisticated tools and software to pinpoint vulnerabilities and close off attack vectors using a data-driven approach.