Everything you need to know about the 4 types of data analysis

The different types of data analysis

Data analysis is used to answer questions, identify trends or gain insights for decision-making. There are 4 basic types of analysis:

• The descriptive analysis helps to describe what the current situation is or what has happened in the past.
• A diagnostic analysis explains why something has happened.
• The predictive analysis can be used to recognize what could happen in the future.
• A prescriptive analysis, on the other hand, shows what measures should be taken next.

1. The descriptive analysis – What happened?

The main aim of descriptive analysis is to find out what the current situation is and thus recognize whether something has worked in the past or not. It is the simplest form of analysis and therefore the most commonly used. It uses historical data to gain a general understanding of what has happened. Dashboards or reports are usually used for this purpose, in which the data is visually prepared and presented in an easily understandable way. This makes it possible to learn from past events and get a rough idea of how they may affect future events.

Examples:

• Marketing analyses: Descriptive analysis can be used, for example, to describe the number of visitors to the company website, the source from which these visitors came, or the engagement on social media. Google Analytics, the tool most commonly used for this purpose, can therefore be regarded as a tool for descriptive analyses.
• Financial reports: Financial reports also describe a current situation by displaying it in key figures, such as turnover, and comparing it to previous periods.
• Production: Descriptive analyses can also be used in production, as they can show the productivity of individual areas, for example.

Descriptive analyses usually form the basis for further analyses, as they can be used to easily analyze the past. Trends and changes that have occurred over time can be easily identified and serve as a starting point for further analysis. In addition, descriptive analyses can help to check whether everything is going according to plan at the current time and, if this is not the case, to find out in which area there are currently difficulties.

2. Diagnostic analysis – Why did it happen?

Diagnostic analysis goes one step further and attempts to identify the causes behind certain events or trends. By examining relationships between different variables, it is possible to find out why something happened and which factors contributed to it. This type of analysis enables companies to identify the causes of problems and find appropriate solutions.
Diagnostic analysis includes various techniques, such as data drilling, data mining, and correlation analysis.

• Data Drilling supports companies in exploring data by providing different data views. This makes it possible to dive deep into the data and at the same time summarize large amounts of raw data in reports and dashboards for analysis.
• Data mining helps to identify anomalies, patterns, or correlations in data sets. Methods such as machine learning or statistics are used for this.
• A correlation analysis examines how different variables are linked to each other and can therefore show how much one variable changes due to the change in another variable. A high correlation indicates a strong relationship between the variables, while a low correlation indicates a weak relationship.

In this way, diagnostic analyses can help to explain unforeseen events, uncover anomalies, or recognize causal relationships.

Examples:

• Company success: If there is a sudden slump in sales, a diagnostic analysis can be used to find out why this is the case and which variables have interacted.
• Healthcare: In medicine, diagnostic analysis is used to identify causes of disease, understand the occurrence of symptoms, and improve treatment options.
• Quality control: In production, diagnostic analyses are used to identify and solve quality problems. This enables the causes of production errors to be identified.
• Customer relationship management: Diagnostic analyses can also be used to identify the reasons for customer complaints or churn. In this way, products or customer service can be improved.

3. Predictive analysis – what could happen in the future?

Predictive analysis uses statistical models and algorithms to predict future events or trends. By using historical data and identifying patterns, predictive models can be developed that help to forecast future developments. Similarly, statistical methods, machine learning, or data mining are utilized here to analyze current or historical data, enabling predictions about future behavior. This type of analysis is particularly useful for identifying opportunities and minimizing risks.

Examples:

• Prevention of fraud: Predictive analysis can be used to predict customer behavior. Therefore, it is often used to uncover patterns in customer behavior that could indicate fraudulent intentions to prevent fraud.
• Insurance: Predictive analytics is also used in insurance, as it can help predict which customers are most likely to make a claim. This can be used to determine the amount of the insurance premium.
• Retail: Predictive analysis can also help retailers predict the demand for products to plan their stock accordingly.

4. Prescriptive analysis – what should we do next?

Prescriptive analysis goes beyond prediction and offers recommendations for action to achieve specific goals. By analyzing different scenarios and testing solutions, companies can make optimal decisions. Predictive analysis usually provides the basis for this. Together, these two analyses not only show what is likely to happen in the future but also provide a clear recommendation on how to deal with it to achieve the desired result. Prescriptive analysis is therefore particularly useful for tackling complex problems and making strategic decisions.

We also see examples of prescriptive analysis in everyday life:

• Navigation: Many navigation systems recommend to drivers which route they should take based on real-time traffic data, for example, to arrive at their destination as quickly as possible.
• Online shopping: In online retail, customers are recommended additional products based on their previous purchases and interests.

However, prescriptive analysis can also be used to optimize processes within companies or to improve customer service, for example.

Conclusion

The different types of data analysis offer companies very different insights into their data, helping them to answer various questions and make well-founded decisions. While descriptive analyses describe the current situation and diagnostic analyses explain the causes of past events, predictive analyses provide a glimpse into the future by making predictions about future developments. Prescriptive analyses go one step further and offer clear recommendations for action to achieve specific goals. By combining these analyses, companies gain a comprehensive insight into their data and can make strategic decisions to achieve their business goals.

With business intelligence software such as myPARM BI^act, these different types of data analysis can be used effectively. For example, myPARM BI^act offers functions for data visualization and analysis, using techniques such as data drilling or data mining. This enables companies to use their data effectively and make informed decisions.

Would you like to get to know myPARM BI^act in a demo presentation? Then make an appointment with us right away!