When we discuss about Artificial Intelligence (AI) and Machine Learning (ML), the common knowledge lands somewhere between understanding the computers and getting information from a Jackie Chan hit. There's a lot of misinformation and misunderstanding around what computers can and cannot do. Unfortunately, while AI might not be as sensational as a summer blockbuster, it's just as exciting in the market research industry. A quick education on the difference between data mining, artificial intelligence, and machine learning (and how they play together) can give you a basic understanding of why they're the real stars of market research, and, if used together, can present a formidable tactic that one can use to conquer any data questions or confusions.
Data mining is actually one of the newer methods that market research companies are employing, but it serves as a foundation for both artificial intelligence and machine learning. Data mining, as a practice, is more than just culling supersets of information from various sources. Data mining can cull, and then aggregate information to alert you to patterns and correlations that you hadn't even thought of.
That means that data mining isn't as much a method to prove a hypothesis as it is a method for framing various hypotheses. Data mining can find the answers to questions that you hadn't thought to ask yet. What are the patterns? Which statistics are the most surprising? What is the correlation between A and B? The mined data (and the accompanying patterns and hypotheses) can then be used as the basis for both artificial intelligence and machine learning.
Though people like to think that artificial intelligence is something vague that deals with NASA and aliens, it's actually rather mundane in the world of research when looked at in the prism of other methods of collection. In fact, data mining, artificial intelligence, and machine learning are so intertwined that it's difficult to establish a ranking or hierarchy between the three. Instead, they're involved in symbiotic relationships by which a combination of methods can be used to produce more accurate results.
Take artificial intelligence, for example: It's a broad term referring to computers and systems that are capable of essentially coming up with solutions to problems on their own. The solutions aren't hard-coded into the program; instead, the information needed to get to the solution is coded and AI (used often in medical diagnostics) uses the data and calculations to come up with a solution on its own.
Data mining is an integral part of coding programs with the information, statistics, and data necessary for AI to create a solution.
Often confused with artificial intelligence, machine learning actually takes the process one step further by offering the data necessary for a machine to learn and adapt when exposed to new data. Think of it as training a machine: It depends on the other two methods by reading mined data, creating a new algorithm through AI, and then updating current algorithms accordingly to "learn" a new task.
Machine learning is capable of generalizing information from large data sets, and then detects and extrapolates patterns in order to apply that information to new solutions and actions. Obviously, certain parameters must be set up at the beginning of the machine learning process so that the machine is able to find, assess, and act upon new data.