What Is Data Mining?

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).

Data mining finds itself as a useful tool in the arsenal of investors. Because data mining centers on the establishment of patterns and predictive capacity, it features heavily in the daily tasks of institutional investors and financial analysts. At the business scale of investment management, professional investors rely heavily on data-driven analysis (via Goldman Sachs). This is dramatically different from the gut-led approach that a typical recreational saver might employ when evaluating stocks and other investment assets.

Data-driven market analysis helps take the emotion out of investing. This is a crucial skill for anyone looking to achieve professional success over the long term, and it's something that all investors regardless of skill level or experience should strive toward, according to Money Crashers. With data mining techniques playing an important role in your investment strategy, this trait becomes easier to cultivate and establish as a habit.

One thing that many recreational investors won't immediately notice is that data mining processes actually play a role in routine investment decision-making that affects them, whether or not they employ it directly or actively. Anyone who invests in index funds or ETFs likely enjoys the benefits that come along with data mining tactics because big data analysis lies at the heart of indexing and automated fund management processes.

In the business world, executives may want to aim these efforts at overarching goals of process efficiency, subscriber capture, or increased sales. For instance, data mining of customer information may reveal that the typical car buyer seeks out used vehicles that are less than perhaps five years old or that Amazon shoppers typically view, say, four products before selecting one to purchase – more specifically, Big Commerce notes that 90% of shoppers utilize Amazon as a quasi-price checking service when shopping for virtually anything.

This reality gives Amazon a unique edge in the marketplace when it comes to understanding consumer sentiment and shopping habits. Amazon users build profiles and browse through tens or even hundreds of products at a time. With data mining techniques, Amazon data scientists are able to uncover patterns in spending, product research, and much more.

The manufacturing sector also relies on data analysis and data mining processes to make sense of internal machinations and chase after improvements across the board. Businesses involved in manufacturing can use data mining specifically to improve their operations by locating and eliminating production inefficiencies, forecasting product demand, and more.

The ability to forecast demand for the things a business produces can transform its productive capacity in incredibly meaningful ways. Decision-making processes lie at the heart of efficient productive capacity, and data mining makes this a less daunting task (via Expert Systems with Applications). With a highly efficient manufacturing center that utilizes data mining comes a sort of upgrade to all manner of processes involved in the fabrication of products. Better core knowledge of customer needs and target production quotas leads to safer working conditions for employees, more effective management, and less overall stress for everyone involved.

Data mining isn't without its drawbacks, however. In addition to the significant benefits that data mining can provide to individuals and companies trying to gain a better understanding of the world they inhabit, these tools for analysis can be misused or corrupted through the implicit bias that sways the results in unexpected ways. Lacking U.S. privacy laws combine with overzealous users to place individuals in the firing line from time to time.

For an individual researcher who is trying to develop a more effective investment strategy, inaccuracies or biases that generate misleading results can lead to a potentially harmful investment strategy rather than one that generates increased profitability.

The biases that may find their way into a data mining process can also produce lasting effects for certain consumer groups, though. For instance, Wired reports that algorithms designed to improve facial recognition software (and heavily dependent on data mining techniques) struggle immensely to identify the faces of dark-skinned individuals as accurately as fair-skinned ones. This inaccuracy can lead to higher incidences of misidentification in relation to criminal activity, in particular, something that can profoundly affect the future of an innocent person. This same dataset bias can be seen in tracking with consumer habits of certain grouped individuals and more.

In the world of business, too, an increase in efficiency may lead to improved working conditions and wages for employees, or it may allow for a shrinking of benefits and jobs as the corporate culture continues to lead more heavily into ever-increasing efficiency and cost-cutting measures.

Even as problems remain in the blueprint of what data mining is and can be, this powerful analysis tool offers a glimpse into a potentially supercharged future of human development and potential. In essence, data mining takes the natural neural ability of a human to group like things together and seek out patterns that can help them solve future questions or complete similar tasks more effectively and speed up the process by many orders of magnitude. Instead of relying on the brainpower of a single human to solve problems through trial and error, data mining and its corresponding parts can simulate thousands of iterations of analysis in mere seconds. In the same way that researchers explore genetic mutations in plants by rapidly cultivating multiple generations (via The National Center for Biotechnology Information), data mining swiftly works through the layers of complex problems with ease.

As humans continue to develop more efficient data mining techniques, these processes will flow through all phases of life with ever-increasing speed. Today, investors gain the benefit of big data analysis either directly or through the use of indexing funds and drivers see real-time updates on their commute through Google Maps or Waze (via Google). But over the horizon of tomorrow, humans might employ simple data mining algorithms to map out even the smallest of questions (they've already made their way into the Spotify music matrix, after all, via Half as Interesting).