Data mining refers to the process of identifying within a data set patterns, trends, or anomalies. Organizations use a variety of tools and approaches to mine data and extract information that they can use to improve their business.

data mining illustration

For modern businesses, data is gold. It’s the key to unlocking insights and improving operations. However, just as mining gold is hard work, extracting value from a sprawling data set is no easy task. Looking at surface-level information is unlikely to produce insights. You must employ tools and processes to extract patterns or identify trends.

Data mining doesn’t involve removing data from a data set in the way that you might mine minerals from the earth. Instead, it’s about examining a data set’s structure and content — as well as the relationships between the data within it — to determine what data to extract and analyze for business insights.

The data mining process

Most data mining operations follow a basic process similar to this: Data mining process diagram

Data mining business applications

Data mining can turn data into value by unlocking information hidden within complex data sets. Among its many applications, here are three of the most common.

Predict behavior

Anticipating trends or customers’ behavior is a common goal of data mining operations. For example, retailers can examine connections among customers’ age, gender, and previous purchases to predict their future behavior and implement personalized loyalty or marketing campaigns. Universities can use data mining to predict which prospective students will graduate or drop out.

Deliver personalized services

In a health care system, data mining can help identify risks, predict illnesses in segments of the population, or predict how long a patient will be in the hospital. Doctors can do a better job of identifying patterns and prescribing treatments when they can analyze the entirety of a patient’s medical records, physical examinations, and treatment patterns.

Measure profitability

For large companies with complex product development and sales operations, determining the profitability of a given offering is not always clear-cut. Tasks like this require sorting through complex information, such as how many staff hours were spent developing and marketing a product, how long the product will remain on the market, and how much the company spends to support customers who use the product.

Data mining can help businesses identify relevant trends in all of these areas to determine the profitability of a given product. While staff members might be able to make this type of assessment manually, data mining allows businesses to draw profitability conclusions quickly; for example, they could track profitability in real time, as sales and expense data changes.

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Data mining helps organizations to predict trends, provide personalized services and products, and measure profitability. But your data team should anticipate a few common data mining challenges, and also be prepared to implement some best practices.

Data mining challenges

Several challenges can sometimes make it difficult to achieve the desired results using data mining. Some of the most common challenges are:

Data mining best practices

To get the most valuable insights, and avoid the pitfalls described above, organizations should follow a few data mining best practices:

Get started with data mining

When you want to start mining your data, you can simplify the process by using a cloud-based data warehouse and an ETL solution like Stitch, which can load data to your data warehouse or data lake from more than 100 data sources. Stitch provides a secure, easy-to-use ETL solution and a bridge to data mining, data analytics, and business intelligence. Sign up for a free trial and start mining your data now.

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