Since birth, we are inundated with data. Our body's sensors—the eyes, ears, nose, tongue, and nerves—are continually assailed with raw data that our brain translates into sights, sounds, smells, tastes, and textures. Using language, we are able to share these experiences with others.

From the advent of written language, human observations have been recorded. Hunters monitored the movement of animal herds, early astronomers recorded the alignment of planets and stars, and cities recorded tax payments, births, and deaths. Today, such observations, and many more, are increasingly automated and recorded systematically in the ever-growing computerized databases.

The invention of electronic sensors has additionally contributed to an explosion in the volume and richness of recorded data. Specialized sensors see, hear, smell, taste, and feel. These sensors process the data far differently than a human being would. Unlike a human's limited and subjective attention, an electronic sensor never takes a break and never lets its judgment skew its perception.

Between databases and sensors, many aspects of our lives are recorded. Governments, businesses, and individuals are recording and reporting information, from the monumental to the mundane. Weather sensors record temperature and pressure data, surveillance cameras watch sidewalks and subway tunnels, and all manner of electronic behaviors are monitored: transactions, communications, friendships, and many others.

This deluge of data has led some to state that we have entered an era of Big Data, but this may be a bit of a misnomer. Human beings have always been surrounded by large amounts of data. What makes the current era unique is that we have vast amounts of recorded data, much of which can be directly accessed by computers. Larger and more interesting data sets are increasingly accessible at the tips of our fingers, only a web search away. This wealth of information has the potential to inform action, given a systematic way of making sense from it all.

The field of study interested in the development of computer algorithms to transform data into intelligent action is known as machine learning. This field originated in an environment where available data, statistical methods, and computing power rapidly and simultaneously evolved. Growth in data necessitated additional computing power, which in turn spurred the development of statistical methods to analyze large datasets. This created a cycle of advancement, allowing even larger and more interesting data to be collected.

A closely related sibling of machine learning, data mining, is concerned with the generation of novel insights from large databases. As the name implies, data mining involves a systematic hunt for nuggets of actionable intelligence. Although there is some disagreement over how widely machine learning and data mining overlap, a potential point of distinction is that machine learning focuses on teaching computers how to use data to solve a problem, while data mining focuses on teaching computers to identify patterns that humans then use to solve a problem.

Virtually all data mining involves the use of machine learning, but not all machine learning involves data mining. For example, you might apply machine learning to data mine automobile traffic data for patterns related to accident rates; on the other hand, if the computer is learning how to drive the car itself, this is purely machine learning without data mining.