Data shapes every corner of our digitalized world, from healthcare and retail to education, sports, entertainment, and finance.
Each year, we collect more data. According to Earth Web, we create around 2.5 quintillion bytes of data daily. In most cases, organizing and filtering such large data sets using traditional methods isn't feasible.
For this reason, many organizations worldwide have adopted Data Mining practices and tools. In this article, we’ll provide you with a beginner’s introduction to the topic. We’ll also share a few examples of tools you can use (some of which need no coding knowledge).
What is Data Mining?
Data Mining is a process of filtering and organizing large data sets to find valuable patterns or relationships.
It is at the intersection of Machine Learning (ML), statistics, and Artificial Intelligence (AI). With these, you can extract information from data sets to spot current trends or predict future events. As a result, you can make more data-driven business decisions.
One of the main approaches to Data Mining is a 6-step process called CRISP-DM (Cross-Industry Standard Process for Data Mining). To illustrate this better, take a look at the graphic below:
If you want to take a deep dive into each step of the CRISP-DM process, then check out our comprehensive guide on this subject.
Data Mining vs. Text Mining – how are they different?
We have spoken in depth about Text Mining before. So what is the difference between that and Data mining?
Well, essentially- Text Mining is a subset of Data Mining that focuses on textual data. Data Mining is an all-encompassing term that includes Text Mining (data in the form of text). Traditional Data Mining was common only with structured data in the past, but advancements in technology have meant that now mining works more than effectively with unstructured data also.
Learn more about the difference between structured and unstructured data.
Text Mining is a subset of Data Mining that focuses on textual data. Text Mining is usually more free-form than Data Mining, however, Data Mining is not only possible on structured data but can also be done on unstructured data.
Data Mining is more than just using filters on structured data, it is based on searching for insights and findings from the data, whether structured or unstructured.
You might have heard the term ‘data lake’ before. This is essentially a collection of raw data, structured or unstructured. Data Mining is very powerful to use in this case as the data in data lakes is not meaningfully organized, yet mining this data can provide many insights.
If you’re a beginner then it might be a bit difficult for you to conceptualize these two concepts. That’s why we included a side-by-side comparison based on the concept, data retrieval, and the mined data type to make it a little easier.
Now that you know the Data Mining definition and what it involves, let's dive into some day-to-day examples that showcase its usage.
Examples of Data Mining in day-to-day life
Whether it's a supermarket or retail shop, Data Mining can help marketing teams and business owners understand shopping trends among customers. Businesses can look at the purchase history and the Data Mining tool will help them understand their clients’ buying preferences and trends.
With the help of these tools, supermarkets can optimize product placement, and discounts, and prepare more accurate marketing materials. All because they understand who is buying what, when, and where.
A study by the Middle East College is a great example of how this works in practice. They’ve used Data Mining on a database provided by Lulu Supermarket, a retail chain. By running an analysis of the buying patterns and habits of Lulu customers, the researchers were able to tell exactly how the company can better tailor its offering. Gaining access to such insights can greatly impact the customer experience.
When using Data Mining to understand trends for product recommendations, typically an RFM system (Recency, Frequency, and Monetary grouping) is used. It splits customers into three different segments. As a result, they’re able to easily spot consumers who spend above average and provide them with that extra level of attention.
Social media optimization
Social media is a highly competitive space for brands worldwide. Businesses use Data Mining to optimize their marketing content, product development, and future product releases to get the upper edge.
While it still is the case that a lot of Data Mining that takes place centers on structured data, on social media like Facebook, LinkedIn, and Twitter, the information is unstructured.
Technological advancements in the past few years have enabled traditional Data Mining tools to expand into the realm of unstructured data, allowing us to use the data valuably.
There are many Data Mining examples for social media optimization, with the most popular being McDonald's. By using Data Mining on their social media, they uncover many valuable insights which help understand what consumers want.
For instance, as a result of one of their analyses of customer opinions on social media, the fast-food chain brought back their clients' favorite Szechuan sauce. Thanks to Data Mining, McDonald’s was able to quantify the demand for Szechuan sauce and make a data-driven business decision on bringing it back for a limited time.