Data mining can be used to discover insights that lead to better marketing strategies, increased sales, decreased costs and reduced churn, and is dependent on proper data collection and warehousing techniques. We can use data mining alongside predictive analytics and machine learning to identify data patterns and investigate opportunities for growth and change.
What Is Data Mining Used For?
Data mining provides a way to analyze large amounts of data to uncover a variety of potential business opportunities.
Data scientists and analysts use data mining techniques to dig through the noise in their data to uncover trends and patterns that can be used in decision-making, particularly when developing new business and operational strategies. The volume of data that exists in the world continues to double nearly every two years, with unstructured data alone making up 90 percent of all existing data. The opportunities that can be uncovered through data mining are virtually limitless.
Data Mining Techniques
Data mining typically uses four techniques to create descriptive and predictive power: regression, association rule discovery, classification and clustering.
1. Regression Analysis
Regression analysis is the most straightforward version of predictive power and is used to predict the value of a feature based on the values of other features in a data set. Regression can be used to predict a product’s revenue based on similar products sold or predict stock market status, amongst many other uses.
2. Association Rule Discovery
Association rule discovery allows analysts to discover relationships between items, for example, products commonly purchased with each other. This is useful for recommendation systems of multiple varieties, whether for content, products, restaurants or others.
3. Classification
Classification is a function of data mining that assigns items in a collection to specific categories or classes. The goal of classification is to accurately predict the class for each case in the data. Classifications do not determine order and are intended to predict relationships between data points. Sorting clothing by color would be a real-world example of classification.
4. Clustering
Finally, clustering determines object groupings so objects in a particular group will be similar to one other while objects in another group are not. A common example is clustering customers together for effectively building marketing strategies.
How Is Data Mining Done?
Data mining is accomplished by implementing several steps that ensure collected data is accurate and usable within a specific context.
There are five steps data analysts use to successfully perform data mining:
- Research: Conduct business research to get an understanding of enterprise objectives, resources that may be utilized and ongoing scenarios to set an effective data mining plan.
- Data Quality Check: Next comes data quality checks, which evaluate and match the data collected from multiple sources to avoid bottlenecks in integration and detect any anomalies before mining.
- Cleaning Data: Data is then cleaned to remove corrupt or inaccurate entries from the data set.
- Data Transformation: Data transformation is the next step in preparing data to be slotted into the final data sets and includes data smoothing, data summary, data generalization, data normalization and data attribute construction sub-processes.
- Data Modeling: Finally, data modeling is used to identify data patterns through the use of mathematical models.
Data Mining Examples
- Mining customer data to determine buying habits and which products with which to target them
- Mining claims data to detect potential insurance fraud
- Determining the average wear and tear of production items in manufacturing based on previous orders and repair data
