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What is data mining and how does it benefit organisations

Volume of data collected is doubled every year and a tool is needed to bring order to the clutter. The answer? Data mining.

The idea of digging through data for useful information goes back to the 1990s. However, advances in processing power has made the mining of data more relevant and useful than ever before. In fact, one would argue that it is essential. The volume of data collected and produced doubles each year and a tool is needed to make sense of all the clutter. Therefore, while the concept might be a little dated, it pays to reexamine data mining and its value to organisations.

What is data mining?

Also known as knowledge discovery or data discovery, it’s about analysing data for hidden patterns, according to different criteria and categorising it appropriately. The data is then stored in a data warehouse for future analysis and study. In essence, it is about trying to make sense of big data by finding subtle patterns and connections. Mining for data is different from data analytics. The former is about churning through data using statistics, while the later is about testing hypotheses to answer specific questions.

How does data mining benefit businesses?

Since data discovery is about finding hidden connections and patterns within big data. It brings several benefits to organisations. When these hidden connections are found, they pave the way for benefits in several areas. Some of the benefits include better decision-making, improvements in planning and forecasting, cost reduction and better customer acquisition. These benefits, in turn, lead to higher profits, a competitive advantage, lower costs and a business that is much more in tune with their customer base.

What are the practical uses of data mining?

The benefits and practical uses of data discovery help companies across different industries. Here are a few.

Fraud Detection

Traditional methods for detecting fraud are no longer enough. However, it’s the perfect tool for fraud prevention. Making it easier to study millions of transactions and classify them as fraudulent or genuine. This makes it easier to identify fraudulent transactions in the future.

Customer Segmentation

Data mining allows marketers to dive deeper into segmentation and increase marketing effectiveness. Mining makes it easier to align customers into distinct segments and discover new segments not found before. Marketers can even find customer segments in danger of leaving the brand, allowing them to take action before losing buyers.


Data mining facilitates research by cleaning, organising and integrating datasets. Researchers will have an easier time finding patterns, correlations and co-occurring sequences.

Data mining techniques

There are several techniques in data mining.

Regression: The purpose of regression is to find the exact connection between two or multiple variables. It’s accomplished by calculating the likelihood of a certain variable in the presence of other variables.

Clustering: This is the process of grouping data based on their similarities – this method of mining is often used to sort out customer demographics.

Classification: Classification forces an analyst to collect data and place them into certain categories. An excellent example is placing banking customers into ‘low’, ‘medium’ and ‘high’ risk segments. Classification is different from clustering because the former groups data into pre-defined labels, while the latter groups data based on their characteristics or attributes.

Outlier detection: This is not just about finding overarching patterns, its also about identifying the anomalies or outliers. Outlier detection helps organisations to identify new and unexpected opportunities.

Pattern tracking: One of the most basic techniques of data mining, pattern tracking is finding the connections in datasets. Patterns in data happen in several ways. For example, seeing the increase or decrease of a certain variable over time or certain incidents occurring at regular intervals.

Association: Similar to pattern tracking, association is about finding the link between dependent variables and is quite similar to market basket analysis. An example of association is seen when shopping for goods on Amazon. If you scroll down a product page, there will be a ‘Frequently bought together’ section where they show other products you can buy with your first product. This option is powered by the association rule.

Key takeaways

Data mining is crucial for organisations. It is about making sense of big data by finding connections and categorising them according to different perspectives. It’s important to note that mining is different from data analytics because the former is about determining connections without a hypothesis, while analytics is about testing a hypothesis. There are several benefits to data mining like more efficient data analysis. Hence, it becomes a vital part of data breakdown and analysis. Many leading analytics providers like SAS provide data mining software packages.

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