Types of Data Analysis and its examples

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Data Analysis is crucial for every business to achieve more success. In this article, we will explain all widely used types of Data Analysis with examples to show how businesses actually use them. Then, You can assist your organization in developing precise business decisions by turning complex data volumes into actionable forms.

Types of Data Analysis

Four Types of Data Analysis

The insights type that Data Analysts drive from the data relies on the kind of Analysis they execute. There are four main ways to derive information from the raw data at different levels in all industries. As you move from the first to the last type, the difficulty level, experience required, and resource usage increase. 

Descriptive Analysis

Descriptive Analysis is the initial stage in data analysis, where analysts summarize and explain the past data via descriptive statistics. This type of Analysis assists almost every business in determining to get the answer “What has happened.” The descriptive analysis insights don’t explain the cause of any event. 

This Analysis sets the base for performing the other in-depth data Analysis. Once you know what has happened with the collected data, you can start to find the related causes. It involves two techniques.

Data Aggregation 

It’s a process of assembling data to demonstrate it in a summarized structure. Aggregated data provides an outline of large data sets. For example – E-commerce collects data on the customers who land on their websites.

Data Mining 

It involves the analysis element, where you will explore the historical data to discover patterns/trends. 


  • Which Campaign videos have received more views?
  • Which healthcare product sales have increased in the last month?
  • Marketing campaigns use Descriptive analysis data to set KPIs via dashboards.
  • Google Analytics reports represent website data like no. of people who visited a particular page and time spent.

Diagnostic Analysis

Diagnostic comes into the picture where analysts must dig deep to find the answer to “why something has happened.” The reason for conducting a Diagnostic analysis is to find the causes behind the discovered patterns or anomalies in the data. Standard techniques involved in Diagnostic Analysis are – Data discovery, Correlations, and Data mining. 

Data Analysts initiate the discovery phase and study additional resources to find the probable causes. Further, you may need to use advanced concepts like probability theory, filtering, regression analysis, time-series analytics, etc., to conclude decisions.

For example- An e-commerce business discovered that their product sales dropped by 35% in August. Data analysts found that visitor numbers and “Add to cart” metrics show good numbers, but customers are still not completing purchase actions. Upon digging deeper, it was revealed that the Payment gateway page faced some errors. 

Predictive Analysis

This analysis type is the next step after descriptive and diagnostic analyses, where you can predict the event most likely to occur after examining the relations and anomalies between historical data. Predictive Analysis assists businesses in making data-driven logical decisions to increase profit in the future. 

Data Analysts can save time in making illogical guesses.

Many companies need more technological tools and resources to conduct Predictive Analysis. This analytics type uses different statistical techniques like machine learning, game theory, modeling, data mining, linear regressions, etc., to increase the likelihood of making accurate decisions.  

Example: Marketing companies use Predictive Analysis to make sales forecasting. You can study the relationship between seasons and sales volumes to determine which season experience the sale drops. You can keep more discounts during those seasons to increase sales or adjust the campaign budgets for better-performing seasons.

Prescriptive Analysis

After conducting descriptive, diagnostic, and predictive Analysis, data analysts perform prescriptive analytics to determine which actions a company should take after predicting future events to solve particular problems. This is a crucial type of Analysis that is more complex due to the usage of advanced tools, statistical methods, programming languages, machine learning algorithms, AI, etc., to stop any future risk which can happen due to the occurrence of a predicted event.

Many organizations would rather refrain from investing in employing resources for prescriptive Analysis. Mainly all big data-driven companies like Microsoft, Deloitte, apple, etc., use Prescriptive Analysis after predictive and descriptive Analysis to improve the quality of decision-making.

For example – To suggest the best way to reach from pick-up point to the destination, Google Maps consider different transport modes, the present traffic condition, and roadworks.

Check Some more

Along with the above four types, more Data Analysis types are practiced depending on industry and data type. 

Exploratory Analysis (EDA)

Exploratory Data Analysis is used to drive relations between two variables that currently have no connection to form a new hypothesis for research. You can express discovered relations between two variables through different graphics.

For example – To study climate change patterns, Data analysts often use Exploratory Analysis to find relations between temperature change between chosen years and anthropogenic activities by humans.

Inferential Analysis

Data Analysts use Inferential Analysis to discover various conclusions from a large volume of data by picking different samples every time. The motive behind using this type of Analysis is to use small samples to generalize data to a larger population. 

For example – The calculation of per capita income for population size. You cannot connect with every person to get the details. Therefore, Inferential Analysis would come to the rescue and save time.

Causal Analysis

Data Analysts use Causal Analysis to determine and manage the reasons and effects of a problem. It solely focuses on the root causes between the two variables. Ensure observed correlations you will use for conclusions are correct. For example – Reasons for social media platforms addiction among adults of a specific age group.

Mechanistic Analysis

The mechanistic Analysis is highly effective in estimating the exact differences in different variables that can impact more related variables. Mechanistic Analysis works like predictive Analysis and has applications in engineering sciences.

For example – Engineers use Mechanistic Analysis to estimate shifts in engine design parameters like fuel injection rate, piston size exhaust pressure, or fuel efficiency for better-designed engines.


Therefore, having detailed knowledge of different Data analysis types makes it more uncomplicated for Analysts to suggest practical solutions once the problem is outlined. Every type is related to another. These Data Analysis types are crucial for all industries, no matter big or small, because raw data is of use. With time and experience, you can increase your expertise in Data Analysis.

Mark Roberts is a freelance writer and tech enthusiast based in San Diego, specializing in internet security and Ai tools.

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