What Is Data Mining?

Data mining involves sifting through large data sets to determine patterns that can help businesses solve more complex problems. With these insights, companies can anticipate industry changes, emerging risks and new growth opportunities.

Teams can combine data mining with predictive analytics and machine learning to identify data patterns and investigate opportunities for growth and change. With proper data collection and warehousing techniques, data mining can give companies across a range of industries the insights they need to thrive long-term.  

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. Data mining can also be used to discover insights that lead to better marketing strategies, increased sales, decreased costs and reduced churn.

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. As a result, the opportunities that can be uncovered through data mining are virtually limitless.

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Data Mining Techniques

Data mining typically uses four data mining 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.

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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:

1. 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.
2. 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.
3. Cleaning data. Data is then cleaned to remove corrupt or inaccurate entries from the data set.
4. 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.
5. Data modeling. Finally, data modeling is used to identify data patterns through the use of mathematical models.

Data Mining Benefits

Data mining provides advantages to businesses in any industry, but here are some of the broader upsides to consider: 

• Enhanced efficiency. Teams can more quickly extract insights from high volumes of data with data mining techniques and algorithms, saving time and labor.  
• Improved problem solving. By identifying patterns from data sets, teams can foresee risks and solve more complicated problems.  
• Predictive capabilities. Companies can combine data mining and predictive analytics to anticipate trends and adapt their strategies accordingly. 
• Refined decision-making. Mining data eliminates guesswork and allows leaders to make more informed, data-based decisions. 
• Greater cost-effectiveness. Data mining is cheaper compared to other data techniques and can help businesses simplify their operations as well.   
• Increased profits: Businesses can increase their efficiency and productivity with data-based insights, raising their profitability over time.  

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.
• Sifting through volumes of stock market data to pinpoint the most promising investments that companies can make. 
• Determining the average wear and tear of production items in manufacturing based on previous orders and repair data. 
• Leveraging operational data to locate bottlenecks and opportunities for making processes more efficient. 
• Analyzing electronic health records and other health data to catch risk factors early on and develop personalized treatments for patients. 
• Compiling student data at schools to gauge student performance and identify students who need additional help. 
• Reviewing network data to determine how to best allocate network resources and resolve any network issues.

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