Data mining includes all systematic processes to identify correlations or dependencies in data sets, which in turn can be used for business applications.
Data mining combines findings and methods from a wide range of disciplines, such as mathematics, computer science, and statistics. In science, it is an indispensable component of large-scale surveys or experiments in order to be able to prove the results obtained in terms of data technology or to be able to recognize patterns in the experimental data. Since large amounts of data are now also generated in many companies, such methods are also being used more and more in the business environment.
Why is Data Mining important?
Many companies already use business analytics and visualize their data with the help of business intelligence tools such as Power BI or Tableau. These tools are a good way to keep track of changes in key performance indicators such as sales, profits, or inventories. However, pure business intelligence does not allow conclusive statements to be made about how these changes can be explained.
Data mining helps to explain the obvious changes in the data and to understand the backgrounds that may not seem clear even to the people involved at first glance. For example, algorithms could be used to find out whether an observed increase in sales is ultimately due to a marketing campaign, lowered prices, or the modernization of the web store front end. Possible positive correlations between the three measures can only be found using dedicated data mining methods.
Types of Data Mining
Depending on the type of data to be examined, there are different approaches and algorithms that can be used. This is also largely dependent on which context exists in the data or is at least suspected:
- Classification: If individual data points are to be assigned to different categories, algorithms such as decision trees or random forests can help. They are able to learn the classification rules and features independently from the data.
- Clustering: This method is relatively similar to classification but not the same. In clustering, individual data sets are assigned to clusters if they share common characteristics. In k-means clustering, for example, only the number of clusters to be searched for must be specified and the algorithm then makes the assignment independently.
- Regression: This method attempts to use the data set to calculate new data points based on given variables. This can be used, for example, to find out how large the influence of a variable is on the element to be explained. Examples of this are linear or logistic regression.
- Neural Networks: These algorithms try to take advantage of the way the human brain works to learn complex relationships from data sets and apply them to new data. Depending on how these networks are structured in detail, a distinction is made between different types, such as a Convolutional Neural Network or a Recurrent Neural Network.
Advantages of Data Mining
Within the business environment, there are several benefits that can be achieved using data mining:
- Effective marketing and sales strategies: With the help of data mining, customer behavior can be better understood or certain customer segments can be formed. As a result, marketing or sales measures can be better tailored to customers and thus also lead to higher success rates.
- Faster customer service: Targeted analysis of incoming service requests can automate customer service processes and thus relieve human colleagues. This means that customers’ questions can be answered directly and long waiting times can be avoided.
- Prevention of production downtimes: The evaluation of production data can lead to algorithms that detect potential problems and impending failures in the production process at an early stage. If these are known before they occur, targeted repairs or interventions can prevent the machine from breaking down.
- Saving costs: By evaluating business processes, inefficiencies and cost-intensive process steps can be identified and optimized. As a result, waiting times or errors can possibly be avoided, leading to cost savings.
This is what you should take with you
- Data mining includes all systematic processes to identify relationships or dependencies in data sets.
- It goes beyond pure business intelligence by trying to find explanations for the data changes.
- The different types of data mining include classification, various types of regression or neural networks.
Explanation of the Apache Hadoop Distributed File System with examples and benefits.
Other Articles on the Topic of Data Mining
- On the pages of the SAS Institute, you will find an even more detailed description of data mining.