Types of Data Analysis
How to turn raw data into valuable insights
Data is everywhere – from fitness trackers that count our steps to the databases of companies that contain huge amounts of information. But these data are just numbers and letters on a screen. It takes business intelligence tools to bring them to life. From descriptive analyses that examine past events to prescriptive analyses that provide recommendations for future decisions, many types of data analysis can help companies make better decisions.
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 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: Sales or Employee Data Analysis
A company can use its sales data from the past five years to identify trends and patterns to determine which products sold best in certain months or seasons. So, this analysis can help identify seasonal patterns in sales data and understand what factors affect sales, such as promotional activities, 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 or causal analysis is a type of data analysis that aims to identify the reasons for past events or problems. It uses data from the past to understand the causes of certain outcomes and thus find solutions for current or future problems.
Diagnostic analysis can be performed in various ways. One way is to use data analysis tools such as business intelligence software to gather and analyse data from various sources. Another way is to bring together experts and professionals from different areas of the company to interpret and discuss the data together.
Example: Analysis of production data or customer feedback
An example of applying diagnostic analysis in a company is analysing production data. It can be used to determine why there were production failures or quality issues, for example. By examining the data, the company can identify possible causes for the problems, such as machine breakdowns, deficiencies in the production process, or quality control errors. Based on these findings, the company can then take targeted measures to eliminate the causes of the problems. Furthermore, the company can react more quickly if similar problems should occur 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 is a type of data analysis that aims to make predictions about future events as well as to make decisions based on these predictions. It uses historical data and statistical models to calculate probabilities and to identify trends that may indicate future events.
Predictive analysis includes various methods and techniques such as statistical modelling, machine learning, or data mining. However, it also requires careful data cleaning, data analysis, and model validation to make accurate predictions.
Example: Analysis of sales figures or behaviours
An online company selling products can analyse historical data on sales figures and the products that were most frequently purchased to make predictions about which products will be in high demand in the future. Based on these predictions, the company can adjust its inventory and align 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.
The prescriptive analysis aims to provide recommendations based on predictions and analyses. It uses historical data, statistical models, and algorithms to make decisions that are intended to achieve a specific goal or outcome. Therefore, prescriptive data analysis can help improve efficiency and profitability as well as achieve long-term competitive advantages.
Unlike predictive analysis, which only makes predictions about future events, prescriptive analysis goes one step further and provides recommendations for the optimal course of action to achieve a desired outcome.
To generate accurate recommendations, prescriptive analysis uses various techniques and methods, including algorithms, machine learning, or optimization models. Careful data analysis, modelling, and validation are necessary to make accurate recommendations.
Example: Improving marketing campaigns or supply chain management
If a company wants to improve its marketing campaigns, prescriptive analysis can be used, for example. By analysing historical data and predicting future events, a company can determine which campaign is most likely to be successful in the future. Prescriptive analysis can then provide a recommendation on how to implement this campaign to achieve the desired outcome.
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 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 credit applications
An example of using inferential analysis in business is the use of customer surveys. If a company wants to measure its customers’ satisfaction with a particular product or service but cannot survey all customers, it can select a random sample of customers and send them a survey. Based on the sample responses, the company can then perform an inferential analysis to make predictions about the satisfaction of the entire customer database.
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 data analysis is a method applied in natural and engineering science to investigate complex systems and gain a deep understanding of the underlying physical, biological, or chemical mechanisms that can explain the observed data. The main goals of mechanistic data analysis are precise data control and measurement of variables to make exact predictions about system behaviour.
A fundamental prerequisite for the application of mechanistic data analysis is a profound understanding of the underlying mechanism that is to be explained by the data. Therefore, mechanistic data analysis is a demanding method that requires specialized knowledge and experience.
Example: Chemical reactions or development of new materials
An example of using mechanistic data analysis is the study of chemical reactions in chemical processes. Here, data such as concentrations of different substances at different time points can be collected. By applying mechanistic data analysis, one can identify and describe the underlying chemical reaction mechanisms.
Another example of applying mechanistic data analysis in industry is the development of new materials. If a company wants to develop a new material for use in the automotive industry, for example, it could perform a mechanistic analysis to understand the behaviour of the material under various loads and conditions. The company would first identify a physical mechanism that can explain the behaviour of the material, such as the material’s response to stress or temperature. Then, it would conduct targeted experiments to collect data on the material’s behaviour in various situations. By conducting a mechanistic data analysis, the company could understand the exact variations in the data and recognize how these variations affect other data. Based on this, it could further optimize the material to improve its performance as well as durability and thus ensure that the material meets the requirements of the automotive industry.
Data analysis plays an important role in today’s business world. There are various types of data analysis that offer companies the opportunity to use their data in different ways. Insights into past events can be gained, problems solved, predictions made, or recommendations derived.
The use of business intelligence tools such as myPARM BIact allows 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 different ways to make them quick and easy to understand. This helps companies to achieve the best possible use of their data and optimize their business processes. Through integrated control, insights and decisions can be immediately translated into actions. This enables companies to react in real time to fluctuations in the data as well as to increase their success.
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