Types of Data Analysis

How to turn raw data into valuable insights

What is Data Analysis?

Data analysis is an important process for turning raw data into valuable insights and knowledge. It is the process of investigating and interpreting data to derive insights and make decisions. Data analysis is a method of data processing where data is examined for patterns, trends or correlations to gain insights into business processes and performance. Such data analysis can be performed by using various statistical methods and algorithms, which can be used to examine past events and patterns, identify causes of difficulties, or make predictions and recommendations for the future.

Descriptive Analysis

Descriptive analysis is a type of data analysis that aims to describe past events and trends based on historical data. It provides an overview of the main features of the data and identifies patterns, trends, as well as correlations to gain insights into past events.

Example: Analysis of sales or employee data
A company can use its sales data from the past five years to identify trends and patterns and determine which products sold best in specific months or seasons. This analysis can help identify seasonal patterns in sales data and understand which factors influence revenue, such as promotions, price fluctuations, or customer feedback.

Another example of the application of descriptive analysis in a company is the analysis of employee data. A company can use the HR department to monitor employee turnover and identify the reasons for employee attrition. The analysis can also help identify patterns and trends in employee performance, job satisfaction, or workload to improve the work environment as well as conditions for employees.

Diagnostic Analysis

Diagnostic analysis, or causal analysis, is a type of data analysis that aims to identify the reasons behind past events or problems. It uses historical data to understand the causes of specific outcomes and, in turn, find solutions to current or future problems.
Diagnostic analysis can be conducted in various ways. One option is to use data analysis tools, such as business intelligence software, to consolidate and analyze data from various sources. Another option is to bring together experts and specialists from different areas of the company to jointly interpret and discuss the data.

Example: Analysis of Production Data or Customer Feedback
One example of how diagnostic analysis is used in a company is the analysis of production data. This can help determine, for example, why production stoppages or quality issues have occurred. By examining the data, the company can identify potential causes of the problems, such as machine breakdowns, defects in the production process, or errors in quality control. Based on these findings, the company can then take targeted measures to eliminate the root causes of the problems. At the same time, the insights gained enable the company to respond more quickly should similar problems arise again.

Another example of diagnostic analysis in a company is analysing customer feedback. By analysing customer feedback, a company can determine what problems customers face with products or services or what aspects they particularly appreciate. Based on these findings, measures can be taken to fix the problems and increase customer satisfaction.

Predictive Analysis

Predictive analytics is a type of data analysis that aims to make predictions about future events and to make decisions based on those predictions. It uses historical data and statistical models to calculate probabilities and identify trends that may indicate future events.
Predictive analytics encompasses various methods and techniques, such as statistical modeling, machine learning, and data mining. However, it also requires careful data cleaning, data analysis, and model validation in order to make accurate predictions.

Example: Analysis of Sales Figures or Consumer Behavior
A company that sells its products online can analyze historical data on sales figures and the products that were purchased most frequently to make predictions about which products will be in high demand in the future. Based on these predictions, the company can adjust its inventory levels and tailor its marketing strategies accordingly to increase the likelihood of sales success.

Predictive analysis can also help predict future trends and customer or market behaviour. It can be used, for example, to predict risks and opportunities in investment decisions or to identify opportunities for improving business processes.

Prescriptive Analysis

Prescriptive analytics aims to generate recommendations for action based on predictions and analyses. It uses historical data, statistical models, and algorithms to make decisions designed to achieve a specific goal or outcome. In this way, prescriptive data analysis can help improve efficiency and profitability and achieve long-term competitive advantages.
Unlike predictive analytics, which merely makes predictions about future events, prescriptive analytics goes a step further and provides recommendations for the optimal course of action to achieve a desired outcome.
To do this, prescriptive analytics uses various techniques and methods—including algorithms, machine learning, and optimization models—to generate actionable recommendations. In order to provide accurate recommendations, a careful analysis of the data, as well as precise modeling and validation of the data, is necessary.

Example: Improvement of marketing campaigns or supply chain management
For example, if a company wants to improve its marketing campaigns, prescriptive analytics can be used. By analyzing historical data and predicting future events, the company can deduce which campaign promises the greatest success in the future. Prescriptive analytics can then provide recommendations on how best to implement this campaign to achieve the desired result.

Similarly, prescriptive analysis can be used to optimize supply chain management processes by analysing historical data to determine which actions are best suited to optimize the delivery process and minimize the risk of delivery delays. Prescriptive analysis can then provide a recommendation on how to implement these actions to achieve the desired outcome.

Inferential analysis

Inferential analysis is used to draw conclusions and predictions about a larger set of variables based on samples. It is a statistical approach based on probability distributions. Inferential analysis can help identify significant differences or relationships between variables by conducting hypothesis formation, testing, and estimation.

Example: Customer Surveys or Loan Applications
One example of the use of inferential analysis in a company is the use of customer surveys. If a company wants to measure its customers’ satisfaction with a specific product or service but cannot survey all customers, it can select a random sample of customers and send them a survey. Based on the responses from the sample, the company can then perform an inferential analysis to make predictions about the satisfaction of its entire customer base.

Another example is checking credit applications. To check the creditworthiness of customers, a bank could select a sample of credit applications and conduct an inferential analysis based on the results of checking the creditworthiness of these customers. This can help make predictions about the creditworthiness of all customers.

Mechanistic analysis

Mechanistic data analysis is a method used in the natural and engineering sciences to study complex systems and gain a deep understanding of the underlying physical, biological, or chemical mechanisms that can explain the observed data. The main objectives of mechanistic data analysis are therefore the careful control of the data and the precise measurement of variables in order to make accurate predictions about the behavior of systems.
An important prerequisite for applying mechanistic data analysis is a thorough understanding of the underlying mechanism intended to explain the data. Therefore, mechanistic data analysis is a demanding method that requires specialized knowledge and experience.

Example: Reactions in chemical processes or the development of new materials
One example of the application of mechanistic data analysis is the study of reactions in chemical processes. In this context, data such as the concentrations of various substances at different time points can be collected. By applying mechanistic data analysis, one can identify and describe the underlying chemical reaction mechanisms.
An example of the application of mechanistic data analysis in industry is the development of new materials. For example, if a company wants to develop a new material for use in the automotive industry, it could conduct a mechanistic analysis to understand the material’s behavior under various loads and conditions. The company would first identify a physical mechanism that can explain the material’s behavior—such as its response to stress or temperature. It would then conduct targeted experiments to collect data on the material’s behavior in various situations. By applying mechanistic data analysis, the company could understand the exact variations in the data and recognize how these variations influence other data points. Based on this, it could further optimize the material to improve its performance and durability, as well as ensure that it meets the requirements of the automotive industry.

Conclusion

Data analysis plays an important role in today’s business world. There are various types of data analysis that enable companies to use their data in different ways. This allows them to gain insights into past events, solve problems, make predictions, or derive recommendations for action.
The use of business intelligence tools such as myPARM BI^act enables companies to perform these analyses in a simple and understandable way to make data-driven decisions. The results of the analyses are visually presented in various ways to make them quick and easy to understand. This helps companies make the most of their data and optimize their business processes. Thanks to the integrated control system, insights and decisions can be immediately translated into actions. This allows companies to respond in real time to fluctuations in the data to increase their success.

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