Data mining refers to the process of analyzing massive amounts of data to find patterns and relationships. It’s an advanced data analysis technique that combines machine learning and artificial intelligence to extract useful information, which can help your business explore more about your customers’ requirements, boost revenue, cut costs, and improve the bottom line.
The Most Common Data Mining Techniques
While data mining encompasses a wide variety of techniques that can be used to identify similarities in data and determine patterns, there are just two main categories: descriptive and predictive.
Descriptive data mining techniques are used to provide correlation, cross-tabulation, frequency, etc. These techniques are usually used for finding the regularities in the data and revealing patterns. Here are two examples:
Association: As the name suggests, this function identifies relationships and associations between items and values.
Clustering: Cluster analysis is used for grouping items into clusters that share similarities.
Predictive data mining techniques are used to predict the future by analyzing the data available. Here are two examples:
Classification: Classification usually utilizes a machine learning model which adds items in a collection to predetermined classes.
Regression: Regression is a statistical technique mostly employed in supervised machine learning that can be used for:
- Identifying the relationship between dependent and independent variables.
- Using that relationship for predicting a variety of numeric values provided for a specific dataset.
Now that you have a foundational understanding of data mining, let’s get to know some data mining tools.
What are Data Mining Tools?
Data mining tools are developed to identify patterns, trends, or groupings among large sets of data and later use that data to get more refined insights.
Why are Data Mining Tools Important?
In 2006, a data scientist coined the catchphrase “data is the new oil.” At that time, a research firm named IDC estimated that the amount of data created, gathered, and replicated was around 1.6 exabytes or, in simple terms, 3 million times the amount of the information available in every book ever written. Now IDC estimates that the global datasphere will reach 175,000 exabytes by the year 2025.
This exponential growth of data is a result of three primary sources:
- Enterprise data
- Machine log and sensor data
- Social data
How are Data Mining Tools Used by Organizations?
Here are a few examples of how data mining tools are being used in today’s world:
- Marketing: With the help of data mining tools, you can explore more about consumer needs and preferences while gathering classifying details like location, gender, and additional profile data. All this data can be used as a basis for your marketing and sales strategy.
- Fraud Detection: Today, financial institutions depend upon data mining tools for detecting fraud and support many risk management systems.
- Decision-making: With the help of data mining, you can get details about processes and trends. These details can help you make better decisions for your business and get better results.
- HR: One of the most important jobs of the HR department is to track employee records and check their backgrounds. Here, data mining tools can be of enormous help and allow HR professionals to save time and money by automating these activities rather than gathering the details manually.