Skip to main content

Data mining vs. machine learning – what’s the difference

(Image credit: Image source: Shutterstock/alexskopje)

Remember that sci-fi movie, I, Robot? The one set in 2035 starring Will Smith and a bunch of robots. Well, if you haven’t, you should watch it. If you have, you’d remember the depiction of artificial intelligence (AI), one of the most talked about buzzwords in the technology industry.

However, over the years we’ve watched AI transition as a figment of our imagination, from movies and the realm of science fiction, into the real world, where it’s being used by businesses on a day-to-day basis. Not only is it helping to enhance business performance, but it’s now assisting ordinary people. Tasks that could be considered ‘mundane’ are now being handled by AI, so that humans can focus on other things, such as being more creative, or watching more sci-fi movies…  

Despite the fact that AI is starting to reach us ordinary people, that doesn’t mean it’s full-matured. In fact, it’s quite the opposite. Businesses are continuously striving to make the latest AI breakthrough, and to do this, they’re relying on two primary technologies: data mining and machine learning.

Machine learning and data mining help companies build tools and solutions that can make decisions and even take actions based on our behaviour. They gain insight into our common habits. From there, they anticipate what we might be interested in and drive us towards the products or services most useful to us.

Both technologies also help professionals find answers to myriad problems; they provide a way to gain a deeper understanding that isn’t possible by simply looking at all of the disparate information and siloed datasets out there.

To provide a clear understanding of the differences between the two, let’s take a look at what drives each process. Then, we’ll delve into the services now being driven by each of these technologies.

‘Digging’ for data

Think of data mining as a search for information. It could be about people, concepts, behaviour, or the devices people use for personal or business use. Data mining searches through vast amounts of data from different sources.

Data warehouses are commonly used to hold information in one place. It makes it easier for businesses to conduct searches to find what they’re looking for.  They’re hoping to find valuable insights that can help them decide what direction to go in next.

Data mining tools search for meaning in all this information. Data mining goes deeper than the human mind can go, finding patterns in seemingly unrelated data and putting it together to predict future outcomes.

Defining machine learning

Machine learning is a branch of artificial intelligence devoted to guiding robots in their understanding of human behaviour. Scientists and engineers hope machine learning will eventually help machines make unguided choices by independently interpreting input from the world around them.

Current AI applications use datasets fed to them informing them of what type of behaviour to anticipate. Other algorithms guide them towards the correct source of information to address each request put to them.

Scientists continue to explore another aspect of machine learning, known as deep learning, modelled after the workings of the brain itself. They hope to see automation anticipating behaviour on its own, freeing it from the need to be fed information at all.

The key differences

Data mining pulls together data based on the information it mines from various data sources; it doesn’t drive any processes on its own. It exists to be used by people or data tools in finding useful applications for the information uncovered.

Machine learning uses datasets formed from mined data. Algorithms take this information and use it to build instructions defining the actions taken by AI applications. Without this information, AI would not know how to respond when someone makes any type of request.

Think of machine learning as the food source, and machine learning as an organism that consumes it to drive the functions it’s asked to perform. It takes different skill sets to successfully manage each one.

Data mining in the real world

Here’s an overview of the industries heavily invested in using data mining to drive their business processes.


Retailers use information gathered from data mining in much of their decision making. It’s helpful in telling them which type of customer finds their wares appealing.

They also learn what products sell best and which ones might need refining. Mined data helps marketing teams direct appealing promotions to target customer groups.


The information gathered from our online purchases and social media habits holds great value, and e-commerce companies tap into data mining to gain deeper insights into their customers. That’s how companies like Amazon and eBay know what products to recommend to you that compel you to make a purchase.

Recently purchased a pair of slippers? You’ll suddenly see suggestions about robes, pyjamas, and anything else related to sleepwear. Thousands of hours go into finding the details of what drives our online habits and appealing to consumers’ unique likes and interests.

Criminal agencies

Data mining even helps police departments and other authorities decide where to focus resources. They use data patterns to find surges in specific crimes in certain areas, at certain times of the year – allowing them to turn the process of policing into a strategic, scientifically-driven practice. Armed with crime data, they decide if more manpower or other efforts need to be directed to certain areas. It also tells them how well those efforts pay off.

Local, state, and federal agencies use data mining in their tracking efforts. It finds spikes in drug activity and pinpoints weak spots when it comes to drugs entering the country.

Machine learning in the real world

Here’s how machine learning drives a lot of the AI in the devices and processes we use each day.

Business intelligence

Companies use AI-powered business intelligence tools in deriving sales results, choosing business initiatives, and managing transactions. They look for trends outlining the health of their company outlook and deciding what direction to take the company in.

Some help businesses look for untapped markets that might be pursued to bring in new clients. They’re also used to track operational efficiency and how well the entire enterprise manages costs.

Online customer service

Many website owners use AI chatbots to gain information from visitors. Chatbots ask questions related to frequent searches or purchases you’ve made on the site. Customer service centres also use their own version of chatbots to respond to common email requests or direct phone customers to the correct department to cut down on hold times.

This is just a small glimpse into how data mining and machine learning already impact our daily lives. The use of these technologies will only continue to grow as businesses discover new ways to leverage data to improve processes, automate once human-led tasks, and gain a deeper understanding into how their customers think – and how they can tap into that understanding to boost customer retention and increase profits.

Ian Matthews, Data Evangelist, NGDATA
Image source: Shutterstock/alexskopje

NGDATA® helps brands in data-rich industries, such as financial services, telecom, media/entertainment, utilities and hospitality, to achieve data-driven customer centricity by enabling them to deliver smarter, more connected customer experiences.