Data Mining History and Current Advances

The process of digging through data to discover hidden connections and predict future trends has a long history. Sometimes referred to as "knowledge discovery in databases," the term "data mining" wasn't coined until the 1990s. But its foundation comprises three intertwined scientific disciplines: statistics (the numeric study of data relationships), artificial intelligence (human-like intelligence displayed by software and/or machines) and machine learning (algorithms that can learn from data to make predictions). What was old is new again, as data mining technology keeps evolving to keep pace with the limitless potential of big data and affordable computing power.

Why is data mining important?

So why is data mining important? You've seen the staggering numbers - the volume of data produced is doubling every two years. Unstructured data alone makes up 90 percent of the digital universe. But more information does not necessarily mean more knowledge.

Data mining allows you to:

Sift through all the chaotic and repetitive noise in your data.
Understand what is relevant and then make good use of that information to assess likely outcomes.
Accelerate the pace of making informed decisions.
Learn more about data mining techniques in Data Mining From A to Z, a paper that shows how organizations can use predictive analytics and data mining to reveal new insights from data.