Data mining is the use of computers to pour through vast quantities of data to draw conclusions or predict outcomes. We explain what it is and how it’s used.
Data mining is a buzz term that many people have heard in recent months or years. However, this tool for understanding the world that we live in remains underappreciated and generally misunderstood by many people in the general public. Simply put, data mining is the process of using algorithms and other digital analysis techniques to analyze enormous volumes of data (via IBM). With this massive data set, researchers are able to consider patterns that may arise naturally or by design.
In the world of business, data mining has become a critical resource in understanding customer behavior and consumer sentiment, predictive maintenance on key infrastructure and equipment, and even a tool for understanding marketing and other internal best practices.
Data mining also plays a role in institutional investing and sits centrally in the underlying processes of social media connectivity. In truth, data mining is something that everyone should familiarize themselves with because it affects the daily lives and overall happenings that we all experience individually and collectively.
Data mining is both fascinating and sometimes startlingly troubling (data mining bots played a central role in illicit data-gathering efforts on Linkedin in 2013, for instance). Continue reading to learn more about this technologically advanced process of understanding the core cogs in the world that we inhabit.
In the same way that you learned about the scientific method in middle school or high school, applying it to generalized problems in math or science class, data mining involves an integral process, too. In order to leverage data mining for your own purposes, you’ll need to apply a similar set of parameters to the classic scientific method model. Science Buddies reports that the scientific method involves six steps, starting with an observation and a related question that you want to be answered. Data mining is somewhat different in that there may not be a specific underlying question that kick-starts the process of data collection and analysis. Businesses or individuals who are using data mining can certainly leverage this toolkit to solve specific problems or explore predetermined areas of interest, but often times data mining is used to simply collect and make sense of massive troves of information without preset agendas.
At any rate, once a research focus (blurry or otherwise) has been established, teams using the data mining toolbox will begin collecting and preprocessing captured information. For instance, when evaluating customer information at a business, not all customers will provide email addresses, phone numbers, or other sought-out information that may eventually make its way into a data mining analysis process.
After the information has been identified and brought into the analysis process, researchers design and test algorithms to identify patterns of behavior, both in terms of human action and beyond it. As an example, data scientists might consider how people purchase and use a certain product based on its price (something that can be manipulated), or as it relates to the weather or seasonality (something that can’t).
