Types of data analytics

Explore the fundamental concepts of data analytics, including what it is, its pivotal role in business, and the four main types: descriptive, diagnostic, predictive, and prescriptive analytics.

The importance of data in business cannot be overlooked, and the powerful role of data analytics can be transformative for businesses when carried out effectively.

This post discusses what data analytics is, the four main types of data analytics and examples of how it translates into impacting industries. 

What is data analytics? 

Data analytics is an important factor in just about every business. It is the process of analysing raw data and drawing conclusions from the information you obtain. Once we’ve got the numbers, Data Analysts use their skills look at patterns and trends found within data and use that insight to make recommendations. It’s pretty simple, but incredibly useful in a business context. 

The key to data analysis is an unbiased eye – you’re looking at the numbers and only the numbers. From there, you’ll use that insight you’ve gathered to inform business decisions.

There are four main categories of data analytics, which you can read about below. 

Four main types of data analytics  

Descriptive, diagnostic, predictive and prescriptive are four key strands of data analytics, each serving a distinct purpose in extracting insights and making informed decisions. Here's an overview of each: 

Descriptive analytics 

Descriptive analytics focuses on summarising historical data to provide an understanding of what has happened in the past, to better understand the changes that occur in a business. It deals with the "what" of data analysis.

This arm of data analytics involves the use of various statistical measures and data analytics tools to present data in a meaningful way, such as tables, graphs and charts for visual representations. 

Descriptive Analytics Example: An ecommerce business, for example, could utilise descriptive analytics data to analyse sales data from previous seasons to understand which products are the best-sellers in different regions, and help inform stock levels for coming seasons. 

Diagnostic analytics 

Diagnostic analytics explains the “why” of the data. This arm of data analysis delves deeper into data to understand why certain events occurred, focusing on identifying patterns, trends and relationships within the data. The ultimate aim of diagnostic analysis is to uncover the factors influencing specific outcomes which occurred. 
 
Diagnostic analytics Example: If a company experiences a sudden, unexpected drop in website traffic, diagnostic analytics may be used to identify potential causes, such as changes in marketing strategy, technical issues with the site, or a Google algorithm update causing organic visibility to drop. 

Predictive analytics 

Predictive analytics uses historical data and statistical algorithms to make predictions about future events or trends. This arm of data analytics involves forecasting to predict what is likely to happen, based on what’s gone before. 
 
Predictive analytics Example: A bank or money lender might use predictive analytics to assess the likelihood of a customer defaulting on payments based on their spending behaviour in the past and credit history. 

Prescriptive analytics 

Prescriptive analytics uses analyses (often a combination of all of the above) to recommend actions to improve.  
 
Prescriptive analytics Example: In supply chain management, prescriptive analytics might suggest the most cost-effective route for shipping products based on findings from the data. 
 
As you can see, there is overlap between the main types of data analytics, but all are useful methods in different circumstances. Businesses often use a combination of these analytics to gain a comprehensive understanding of their data and drive informed decision-making. See examples of how data analytics can be used in businesses. 

How an MSc Data Analytics can help you understand and apply the fundamentals of data analytics

Whether you’re already working in the field, transferring from a related subject area, or are simply interested in building a career as a data professional, data analytics courses can help speed up your journey. 
 
Our 100% online, MSc in Data Analytics postgraduate course will help you learn valuable data analysis skills without needing to take time away from your work or other commitments.