Data mining vs. machine learning

By Brynne Henn - July 28, 2021

Gathering data is easier than ever, but knowing how to glean insights and knowledge from that data is a bit more complicated. Often, companies end up with far more data than they know what to do with, which can be counterproductive and lead to inaction.

Companies use two primary methods to translate datasets into useful information: data mining and machine learning.

Data mining and machine learning are both computer science methods for gaining insights about data patterns and making informed decisions based on that data. Data mining is the process of gleaning useful information from a large amount of data. It’s a manual process that allows data scientists to discover new patterns in data, and was initially referred to as knowledge discovery in databases (KDD).

While data mining allows data analysts to spot trends and patterns on a case-by-case basis, data mining is typically slower than machine learning and requires highly skilled professionals who can apply various algorithms to gain intelligence.

Machine learning, on the other hand, is an automated process in which computers analyze large datasets. Machine learning is a subset of artificial intelligence (AI) that helps computers learn patterns and make predictions.

Typically, analyzing data with machine learning is a more streamlined process for companies to gain insights with large datasets. After initial programming requirements, it can pretty much run itself, spotting trends based on historical data with unsupervised learning processes.

While both are analytic processes and are helpful for pattern recognition, there are some key differences between data mining and machine learning.

What is the difference between data mining and machine learning?

Data mining and machine learning both learn about data to help improve decision making, but while data mining is reviewing patterns in existing data, machine learning is capable of using those patterns to then make predictions.

Machine learning allows a computer to become more intelligent as it extracts new data and refines its processes, while the data mining process requires human intervention to predict future outcomes.

Let’s look at a few other differences between data mining and machine learning:

How they operate

Data mining relies on big data—or a massive dataset—to operate, allowing data analysts to make predictions for organizations. Machine learning, while it processes swaths of data, is programmed using languages such as Python and works with algorithms rather than raw data. Perhaps one of the largest differences is the human element. Data mining requires humans to operate, while machine learning is self-sufficient after it’s programmed.

Their potential for growth

While it’s a valuable method for data analysis, data mining is a static process. This is a considerable departure from machine learning techniques, which are designed to adapt and evolve instantly as new information becomes available. While data mining as a process is unchanging, machine learning is predictive and built for change and growth, which will ultimately mean better outcomes for pattern recognition and future planning.


While we’d like to believe that skilled data scientists are capable of pulling valuable insights from large datasets by data mining, there’s always the possibility that a data miner will miss multiple connections or relationships between various data. However, machine learning technology can spot relationships quickly and easily and draw highly accurate conclusions to predict outcomes.

How data mining and machine learning are used

There are plenty of valuable data mining uses for businesses today. For example, retailers use data mining to help identify buying habits, while mobile companies use data mining to predict customer churn rates. Beyond the typical corporate applications of data mining, even police departments use it to spot crime trends to help allocate more funding or greater police presence.

Machine learning is helpful for industries that lean on artificial intelligence, whether it’s online streaming services or driverless vehicles. For example, AI and self-learning machines are used for credit card fraud detection, business intelligence, and online customer services. Netflix uses machine learning to recommend your next binge-worthy show, and self-driving cars are built with machine learning.

Which one is better for data science: machine learning or data mining?

For companies with large datasets and a desire to gain insight based on that data, data mining is a useful method. Data mining helps businesses analyze and understand trends, which can ultimately lead to better business decisions.

However, simply analyzing historical data won’t be enough for some companies. Beyond spotting trends based on gathered data, machine learning allows computers to learn and adapt to help manage and continuously analyze large amounts of data.

Machine learning allows data scientists to teach computers how to automatically glean insights through algorithms. This process can help companies distill vital information on an ongoing basis, rather than taking massive sets of data and retroactively spotting trends and patterns.

Sisu helps you drive better decisions

Gathering data is easy. Having the time and resources to gain knowledge from that data is challenging. More often than not, companies struggle with knowing how to take their massive datasets and turn them into actionable insights. This is where machine learning comes in.

Working with Sisu is a reliable way to avoid bottlenecks in your data analytics. To learn more about how we can help you improve your business analytics, schedule a demo with Sisu today.

Read more

Data Analyst 3.0: The next evolution of data workflows

With the rise of cloud-native data warehouses and advancements in AI, Sid Sharma explains how we’re at the cusp of a third phase of BI that will forever change the role of the Data Analyst.

Read more

Humans, not machines, are the main bottleneck in modern analytics

With advances in data storage and compute, the fundamental bottleneck in analytics has shifted from the infrastructure to people. In this post, Peter breaks down this shift and explains how Augmented Analytics will re-balance this human bottleneck.

Read more