Data mining is a powerful tool that can help companies in a variety of ways. From cutting costs, optimizing processes to improving sales, data mining is a game-changing tool that every company should have on their arsenal. There’s no shortage of benefits that data mining brings to organizations everywhere. But what are the dangers?
While data mining can put anyone at the forefront of every industry, it can quickly go badly for companies that don’t do it properly. From the collection process to the actual implementation, there are various entry points for the dangers of data mining. Let’s dive into each one.
While data mining on its own doesn’t pose any ethical concerns, leaked data and unprotected data can cause data privacy concerns. Through the years, there have countless campaigns on stolen data that have caused an uproar in various parts of the world.
Very personal information like intimate photos, credit scores, or bank account log-in details have been leaked and caused real-life distress to users. People can lose reputations, their life savings, and maybe even their peace of mind in the process.
As big data creates a better view of who people are and what they want, it begs the bigger question of whether or not monetizing sensitive data is ethical.
By accessing personal records to exploit people in the name of profit, we can blur the boundaries between what is acceptable or not.
Information such as medical records, location tracking, or even search history, used to manipulate users into buying things that are not clinically proven, they don’t need or cannot afford raises a lot of pertinent questions.
At any given time, there are two main kinds of data available to data miners– bad data and good data. Unfortunately, the internet is rife with the former more than the latter. When companies don’t sift through data properly, they’re prone to using incomplete, duplicated, or outdated data.
Companies can be stuck with a half-baked analysis that won’t add value to their businesses and unnecessarily waste a ton of money in the process.
Overvaluing the Output
While data can help make decisions, it’s also not everything. When it comes to things like management or leadership, the most useful data can sometimes be the unstructured ones.
Not every great decision can be attributed to data, especially when the algorithms behind the process are not yet as refined. As time passes and algorithms get smarter, it may become more reliable.
To get the most of data mining, data miners need to know the difference between the actual business and data. Data gives a picture of a business, but it’s never the whole picture. To get the most out of data mining, it needs to be in collaboration with business units that can serve as a check and balance.
Data miners also have to remember that behind the 1’s and 0’s, there are actual people who will be affected by their recommendations.