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Data Mining for Business Analytics

Today’s organizations generate massive amounts of data related to customer purchasing behavior, operational performance metrics, financial transaction records, and more. The sheer volume of data makes working with it unwieldy and challenges business teams who need to act quickly to gain actionable insights. Data mining for business analytics allows companies to comb through large data sets to identify patterns and relationships that point to solutions, including improving efficiencies, effectively targeting marketing spend, and increasing sales. 

In this article, we explore what data mining is and how it works. We also present examples of how different industries are using data mining to solve business problems and identify new opportunities. We wrap up with some thoughts on where data mining is headed next.

What Is Data Mining in Business Analytics?

Data mining is a process that involves searching for and finding patterns, anomalies, associations, and correlations in very large data sets. The goal of data mining is to predict an outcome based on available data. Due to the large volume of data inherent in data mining, the process often uses the power of machine learning and artificial intelligence to speed up the path to insights. 

Data mining can help companies uncover hidden risks and seize new opportunities. Although they’re sometimes used synonymously, the terms data analytics and data mining don’t refer to the same thing. Where data analytics develops models and tests various hypotheses using analytical methods, data mining is focused on unearthing overlooked patterns or relationships in extensive data sets.

How Data Mining Works

While there’s no universally accepted framework, most data mining projects follow a workflow similar to the following.

1. Determine objectives

When you’re faced with a time-sensitive issue to solve, it can be tempting to just dive into the data and get started. But clearly defining the business problem is crucial to success. Focus on a primary issue and create a focused list of questions that will result in the insights you need to solve the problem.  

2. Gather and prepare data

The success of any data mining project depends on identifying the relevant sources of data. During this step, the business team typically collaborates with the BI team to determine which data sources will be useful. Before being mined, the data needs to be cleaned and transformed to remove errors, outliers, and missing data that can skew the results. Once the data quality is ensured, the next step can be taken. 

3. Investigate relationships and apply algorithms

The third step is where the real action begins. At this point, teams will select modeling techniques and apply algorithms. You may choose to use various approaches such as classification, cluster analysis, pattern detection, prediction, or regression analysis techniques to support your objectives. Depending on the data sets you’re mining, AI and machine learning algorithms can also be deployed to comb through massive amounts of data quickly. 

4. Evaluate results

As you investigate the results of the data mining process, insights will emerge, providing a deeper understanding of potential solutions to the business problem being targeted. You should also be able to answer the questions you outlined in the first step. Now your team is ready to take these insights and create an actionable plan to deploy a solution. 

Examples of Data Mining for Business Analytics

Data mining can benefit companies in nearly any industry. Here are a few examples of how organizations are using data mining for business analytics, solving business problems, and identifying new opportunities.


Market basket analysis: Using market basket analysis, online retailers can determine which products consumers are most likely to purchase with ones already in their shopping cart, enabling them to serve up relevant recommendations for complementary items. 

Customer journey: Data mining can also examine an individual customer’s past purchases, items saved on a wishlist, and site browsing history to display targeted advertisements for products they’re most likely to purchase.


Personalized treatment: Data mining can help healthcare providers offer improved levels of care by allowing them to efficiently identify treatment options tailored to a patient’s medical profile. Insights gained from data mining can result in higher quality, more affordable care. 

Insurance fraud detection: Data mining also makes it easier for insurers to detect insurance fraud and abuse quickly.


The financial industry operates in an environment where significant risks abound. A comprehensive data mining strategy can help financial institutions more effectively anticipate market risks, detect fraud quickly, and generate higher market returns.


Targeted advertising: Ad revenue is the lifeblood of today’s media companies. Data mining provides deep insights into each individual user’s interests, preferences, and biases. Media companies can leverage this enhanced understanding of their audience to display more highly targeted ads, resulting in higher conversion rates for advertisers. 

The Future of Data Mining for Business Analytics

The amount of data that companies collect and store is growing exponentially. At the same time, the algorithms for analyzing this data have become increasingly sophisticated, aided by advancements in machine learning and AI. This has resulted in an expansion of data mining capabilities into new and more powerful applications.  Here’s what we expect to see next for the future of data mining.

Multimedia data mining

Data comes in many forms, including video, audio, images, and text. Mining data from multimedia sources is now possible, and newly developed tools make gleaning actionable information from unstructured data, such as video and audio sources, more approachable. We expect multimedia to be a promising new frontier for data mining.

Geographic and spatial data mining

Geographic, spatial, and astronomical data is analyzed to provide information regarding topology and distance-related business problems. This type of data mining is most commonly used in travel, navigation, and government applications. But there are many other use cases, including marketing, asset management, and logistics.

The Snowflake Data Cloud for Data Mining and Business Analytics

Many organizations are struggling with their data mining initiatives because they’re limited by the traditional data warehousing structure, which wasn’t built to accommodate the massive volumes of data generated by disparate systems in various formats. The Snowflake Data Cloud provides quick and easy access to a single trusted source for internal and third-party data. Additionally, Snowflake’s advanced capabilities foster an environment where systems driven by artificial intelligence and machine learning can improve rapidly. There’s no limit to the amount of data that can be stored and shared. In turn, there’s no limit to feeding models with real-time data.

See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial