What Are the Basics of Data Mining?

2 Mins read

The prevalence of terms like ‘data science,’ ‘big data,’ ‘data analytics,’ ‘data management,’ and ‘data mining’ underscore the fact that data’s essential. Data’s one of the most valuable assets a company can have, but data must be correctly processed for businesses to capitalize on the benefits.

Data mining is a byproduct of enhancements made to data warehouses and databases in the 1990s. Sophisticated data mining software enables companies to process large data sets and use data analytics to develop actionable insights from the data sets. Let’s examine what data mining is, how it works, and how it benefits businesses.

What is data mining?

There are so many terms referencing data, you may be wondering what distinguishes data mining from the others. What is data mining? At a basic level, data mining could be referred to as the process of examining data sets. The data mining process involves gathering large volumes of structured and unstructured data. Data analysts scrutinize the new data, looking for deep insights from the large volumes of data.

Data scientists use various data mining techniques to process data sets. Data mining’s also used to refer to informing business operations via a tool or interface. Data scientists use data mining techniques to promote knowledge discovery and turn raw data into useful information to help companies make business decisions.

How does data mining work?

Data mining is a form of data analysis that involves looking for patterns. Suppose the data demonstrates a cause and effect relationship between events. Data analysts can conclude that if the causal event occurs, the effect’s likely to follow. For example, the data could demonstrate that there’s higher staff turnover when there’s a change in management. A restaurant could determine that when they change their menu, they typically attract new customers.

Data analysis also reveals data affinities, which are events that usually correspond to each other. Data mining’s ultimate objective is to take large volumes of data, process the data, and identify relevant data that can be used to make predictions and determine the outcome of various actions. One of the primary applications of data mining is predictive analytics.

How does data mining benefit businesses?

All types of businesses benefit from data mining. Suppose you’re trying to determine the best way to attract new clients to your restaurant. Data analysis may have demonstrated an association between linked events by determining that changing the menu led to an increase in clientele in the past. With predictive analytics, you can determine the likelihood that you’ll increase your clientele with a menu change. You could also identify other actions that corresponded to increased sales, such as remodeling your restaurant or adding entertainment for patrons.

Computer companies can use predictive modeling to address business problems. Suppose you manufacture computers, tablets, and two-in-one computers. You’ve noted that sales of laptops have declined, and this is a business problem you must address because you have a high number of laptops you need to sell to prevent your department from sustaining a loss this quarter. Data mining turns into actionable business intelligence you can use to identify potential solutions. You can use data mining tools to review a target data set and determine whether offering discounts could increase sales, enabling you to reduce stock.

Software manufacturers can use data mining to inform their decisions when designing new software and creating marketing campaigns for their products. Suppose your company’s thinking about placing ads on social media platforms. Evaluating large amounts of sales data can help you determine whether previous ad campaigns on Twitter or YouTube generated sales. You can also use data mining to identify anomalies that may have affected previous marketing campaigns to determine whether the lack of sales stemmed from the platform or other events.