Data mining. You’ve heard the term before because your organization is beginning to focus on internal business intelligence (BI) projects. You know it has something to do with searching data, but you’re not exactly sure what it is, who does it, and for what reason. And now, you’ve just been told your department is going to team up with IT to implement a data mining project.
So, what is data mining, anyway?
Let’s start with the absolute basics. What is data? On the most basic level, data is text (numbers, characters, words, etc.), video, and audio that is available to analyze. The mining process takes a large pool of the collected data with the intent to extract the data that’s most valuable.
A quick visit to Merriam-Webster dictionary to ask “What is data mining?” informs us that it’s “the practice of searching through large amounts of computerized data to find useful patterns or trends.”
There are many benefits for intelligent data mining, such as time efficiency and a reduced risk of error. In regard to time savings, not only is the extraction of data completed much faster than it would if a human were sifting through it, but the swiftness in which this occurs also allows more time for employees to instead focus on other work.
Additionally, intelligent data mining projects are more efficient and produce better results. When well-designed, the final results yield significantly fewer — if any — mistakes, which would be impossible in manual research because of simple human error.
The final results help to improve decision-making, provide more accurate forecasting, create new potential revenue streams, and improve customer experience (CX), among many other benefits.
Industries using data mining (and how)
Don’t think that data mining is only for techy professionals. There are many industries that benefit from implementing data mining projects.
A human resource department, for example, can use data mining to explore a large pool of applicants and extract the best candidates for the job. Where previously HR professionals would spend hours pouring over resumes, data mining now uses keyword targeting and other methods to cull down the candidate pool.
When it comes to making the decision on whether or not to approve a line of credit, predictive modeling, a form of data mining, allows banks to determine the likelihood of an applicant following the terms of the loan.
In the education field, college admissions offices are using data mining to predict whether an applicant will accept an admissions offer. In an article for Forbes, Peter Zimmermann, a previous Stanford admissions officer and consultant for Solomon Admissions, says, “As the admissions process becomes more digital, it makes sense that data analytics would play an increasing role in the admissions office determining the depth of a student’s interest.”
And let’s not forget about retail, where access to massive data helps brands gain better insight into their customers, allowing the retailers to create more personalized campaigns, which in turn provides a better experience to the consumer.
What is data mining’s relationship with CX?
Improving CX is a goal for many businesses, and one way to do that is by using data mining to learn more about your customers so that you can improve and enhance their experiences. In an article for Convince and Convert, Jay Baer says, “Good CX professionals use their instinct and experience to find and fix. Great CX professionals augment that expertise with big data and machine learning, peering into the numbers to find larger truths.”
It’s time to stop asking “What is data mining?” and instead use it to become a truly great CX professional.
To learn more about data and CX, visit smartercx.com/cxtech/data.