6 Most Prevalent Types of Data Analytics

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Today, organisations from different industries, whether healthcare, hospitality, or information technology, use data analytics to improve decision-making and performance. There are various popular types of data analytics used by organisations. Some are similar while others are complimentary.

This article will focus on the six most prevalent types of data analytics, organisations use in various projects to help gain success.

1. Descriptive Data Analytics

Descriptive analytics is the most basic data analytics method that uses historical data to describe ‘what had happened’. According to the situation, Descriptive analytics is a tool that certain data analysts may utilise as a summary to aid inquiries and analysis from other types of data analytics.

It can be considered the ‘best practice’ since it explains the conclusions drawn from other analytics on historical data. Businesses use descriptive analytics to identify problems, compare historical data, and identify strengths and weaknesses.

2. Diagnostic Data Analytics

Diagnostics data analytics examine previous data to determine the cause of an anomaly. Businesses use this type of analytics to answer the question “why did this happen?” from descriptive data analytics.

Diagnostic data analytics is commonly carried out using techniques such as:

  • Data discovery: Data analysts use the data discovery technique to identify sources that can help them infer causes from results.
  • Drill-down: Drilling-down focuses on a specific aspect of the data or a widget.
  • Data mining: Data mining entails using automated processes to extract information from a large raw data set.
  • Correlations: Identifying connections or patterns between various data sets yields diagnostic analytics results.

This data analytics helps businesses develop accurate solutions rather than relying on guesswork.

3. Predictive Data Analytics

Predictive data analytics evaluates current and historical data to forecast future actions. It aims to answer the question ‘what is likely going to happen near the term’. Businesses perform this type of data analytics by combining historical data with machine learning, data mining techniques, and statistical modelling. These techniques help businesses quickly identify patterns and predict future risks and opportunities.

Businesses that do not use predictive analytics are more likely to make future mistakes from which they will never fully recover.

4. Prescriptive Data Analytics

Prescriptive data analytics entails selecting the best solution for a problem from a set of options. This type of data analytics evaluates the results of other analytics and offers guidance on how to reach a specific conclusion.

It is used in dynamic pricing strategies, machine maintenance schedules, recommendation engines, loan approval engines, and other similar tools to analyse all possible decision options and personalise the procedure. These tools demonstrate the consequences of every option, and provide better options.

Prescriptive analysis can help businesses automate decision-making and speed up complex approvals.

5. Real-time data analytics

Unlike other types of data analytics, real-time analytics evaluates recent data rather than historical data from customers or external sources. This type of data analytics would be ideal for applications with high availability and low response time.

Businesses use it to outperform their competitors in predicting trends and benchmarks. They can also instantly track and analyse their competitors’ operations.

6. Augmented Data Analytics

Augmented analytics employs machine learning and natural language processing to analyse data. This reduces the possibility of errors and gives analysts more time to perform other actionable tasks. Most analysts use this type of data analytics to take advantage of machine language and other outstanding features.

Businesses use augmented analytics in their analysis process to interact with data organically and identify trends.

Take Away

We hope this blog helps you better understand the goals of various types of data analytics and how businesses use them to gain valuable insights. Understanding the data analytics process can help you examine data more efficiently and use different algorithms to derive solutions and offer expertise.

Enrol in advanced data analytics courses if you want to better manage your data and begin using analytical solutions. These online certificate courses will enable you to gain insights more quickly, provide valuable expertise, and drive business growth.

Heather Breese
Heather Breese is a qualified writer who fell in love with creativity and became a specialist creator and writer, focused on readers and market need.

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