We’ve watched as artificial intelligence moved from the realm of science fiction into applications that not only help businesses get better results but are now helping ordinary people manage mundane tasks in their daily lives. Businesses continuously strive to find the latest AI breakthrough, 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 behavior. 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.
Definition of Data Mining
Think of data mining as a search for information. It could be about people, concepts, behavior, 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.
Definition of Machine Learning
Machine learning is a branch of artificial intelligence devoted to guiding robots in their understanding of human behavior. 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 behavior 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, modeled after the workings of the brain itself. They hope to see automation anticipating behavior on its own, freeing it from the need to be fed information at all.
Key Differences Between Data Mining and Machine Learning
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 data mining 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 Use Cases
Here’s an overview of the industries heavily invested in using data mining to drive their business processes.
Retail
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.
E-commerce
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, pajamas, 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 Use Cases
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 does managing costs.
Managing Email Spam
AI is what drives those annoying emails away from your main inbox and into folders labeled for spam, saving professionals and ordinary email users from wading through junk to find the messages that are important to them.
AI also looks for any attachments or other aspects of an email that seem suspicious, recognizing and flagging messages that contain something potentially harmful. Some programs remove the offending information entirely before you get a chance to accidentally click it.
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 centers 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, and 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.
