7 Essential Data Mining Techniques Every Company Should Know

MX Bites / August 8, 2024

Many companies live and dine with huge amounts of data every day without doing anything significant with it. They sit on a gold mine of information that can drive substantial business growth because they lack basic knowledge about data mining or have not learnt to place value on their business data. This shouldn’t be so. If you’re going to scale your business in this data-driven world, you must learn to leverage every bit of data that flows in your direction through data mining. 

Insights gleaned from this vast amount of data can help you gain a competitive edge at this time when competition is fierce in the market. But if you don’t know about the data mining techniques, you are likely to remain in a spot and be unable to compete at a global level. Hence, you should read further to find out the basic techniques that will keep your business scalable and drive profitable growth.

Defining data mining techniques

Data mining is simply the process of discovering patterns, correlations, and anomalies within a large set of data to predict outcomes.  It uses statistical analysis, machine learning, and database management to transform raw data into valuable information. The following are basic data mining techniques that every business should know and leverage when making strategic decisions in the organization:

1. Classification: This is a specialized technique used in data mining to sift data into categories. It identifies the relationship that exists between a vast amount of data and identifies patterns in it. It is used in customer segmentation, spam detection, and credit scoring. Classifying data saves you a lot of stress and helps you find information more easily. It also helps you keep track of growth happening in different aspects of your organization. For example, a customer management team can use the classification technique to categorise different customers according to their behaviour or buyer’s journey and monitor their interactions with the products over time.
2. Clustering: Unlike the classification technique, this technique is unsupervised and is used to group only similar data points together. For example, it could be used to group different markets with similar demographics during a market research process. It prioritizes data according to certain parameters. You can use this technique to segment the market based on customer behaviour and demographics to boost their marketing campaign.
3. Association Rule Mining: This technique is used to identify the relationship between different variables in a large dataset. It is often called frequent-itemset mining. The rule states that if event A happens, then event B is likely to happen. For instance, when a customer buys a phone, he is likely to get a phone stand or screen protector. E-commerce platforms usually use association rule mining to identify product associations and recommend complementary products to customers to increase cross-selling opportunities.
4. Text mining: This technique involves extracting useful information from text data through techniques like natural language processing (NLP). It can be used for sentiment analysis, topic modeling, and information retrieval. Text mining is used in market research processes to analyze customer feedback and get a qualitative view of customers’ behavior, preferences, and wants.
5. Neural Networks: These are sets of algorithms modeled after the human brain and are designed to recognize patterns in a large data set. It uses external input from humans to adjust internal features in order to recognize patterns and trends in large datasets. Deep learning, which is a subset of neural networks, helps in image and speech recognition. Convolutional neural networks (CNN) and recurrent neural networks (RNN) are notable types of neural networks. 
6. Decision tree: This is a popular data mining technique used for classification and regression tasks. It is a tree-like model that splits data into branches to arrive at a decision or prediction. The decision tree represents a large data set with nodes, which represent each of the categories with questions. It is made up of several branches, which are split into other branches until only one branch is left, which forms the basis for the decision-making process.
7. Anomaly detection: This basically helps to identify anomalies in large datasets. It focuses on identifying outliers or unusual data points that deviate from the norm. This technique could be used in companies with high-risk data breaches because it is crucial for fraud detection, network security, and quality control. Since every company should be cybersecurity-conscious, incorporating anomaly detection into business operations should not be debated. 

Conclusion

The world is largely data-driven, and it’s no longer an excuse for even SMEs to ignore some of the basic data mining techniques that are listed above. Therefore, understanding and implementing these basic data mining techniques will guide your decision-making processes and help you stand a chance in the competitive business world. Uncovering patterns in data and categorising them according to their classes can give you a better understanding of your customer behaviour, predict their future expectations, and serve them to satisfaction.