Data is becoming increasingly important for today’s businesses. Organizations are gathering more and more data from various information sources like websites, social media, mobile devices, IoT devices, and applications.
But what can businesses do with all their collected data? Data mining provides several techniques that can help organizations classify this data and try to find patterns or relationships between pieces of data.
In this article, I’ll guide you through the concept of data mining and dive deeper into use cases and techniques. First, let’s find out what exactly data mining is.
Many people misunderstand the concept of data mining. Data mining isn’t about the act of collecting data—it’s about finding relationships or patterns in the data you’ve already collected. So, the key is to find new knowledge of the gathered data.
Therefore, we could argue that data mining intersects database management, machine learning, and statistics to infer new knowledge from the collected data.
Next, let’s discover some interesting use cases for data mining.
Data mining is a technique that can be applied in almost every field. Let’s explore some interesting use cases, like marketing, fraud detection, and spam filtering.
Firstly, data mining helps the marketing team to better understand the different types of people who visit a particular website. This allows them to gain intelligence about each group and target them individually with customized promotions. Some grocery shops go as far as targeting each customer with different discounts based on their buying behavior.
By tracking spending habits, banks or financial institutions can detect fraudulent transactions. When a suspicious transaction is detected by a data mining model, the transaction will be flagged and halted for investigation. This is a great application of data mining—to detect and even prevent fraudulent transactions.
Mail providers often offer spam filters. By using data mining techniques on the thousands of emails they process daily, they can learn the common characteristics of spam messages. Some mail providers go as far as immediately removing a message before it even reaches the user’s inbox.
Recommendation systems can be found everywhere. Certainly, most people have received movie recommendations from Netflix or suggested products from Amazon. Recommendation systems try to predict consumers’ buying behavior using data mining models. The goal of these recommendation systems is, of course, to sell more products by showing consumers products they may want to buy or may be interested in.
One of the most common fields of study for data mining is sentiment analysis. Sentiment analysis is based on text mining. It tries to aggregate people’s thoughts and derive their feelings. Often, social media posts serve as the input for sentiment analysis models. In addition, a data mining engineer often uses natural language processing to find the contextual meaning behind a tweet or Facebook post.
Next, let’s learn about different data mining techniques.
Many approaches to data mining can be found. Here are four of the most important techniques.
Firstly, one of the most basic approaches is finding patterns. Patterns can be easily found by tracking certain types of data or specific values in your data set. For example, you might want to know when and why the sales for a particular product have risen. You might find a pattern that indicates that sales for certain products rise when holidays are approaching or when the summer starts.
Another great example concerns the relationship between salty food and beer. A bar owner might want to find out whether guests will buy more drinks if the bar provides them with complimentary salted nuts. It’s a classic, simple example to detect patterns in your guests’ ordering behavior.
Secondly, instead of collecting huge chunks of data, the classification technique looks only at specific attributes of the collected data. For example, say you want to discover the relationship between a customer’s financial knowledge and the risk level of their investments. By looking at your customers’ purchase history, you might find out that most well-educated customers opt for medium-risk purchases. The great thing about this technique is that it focuses on really specific data properties. In this example, we require only the purchase history and the customer’s level of financial knowledge.
Next, the association technique is a commonly used pattern in cross-selling products online. To give an example, you might find out that customers who buy a football often buy sports shoes as well. Importantly, this is great intel for designing a shop layout because you could place the sports shoes section next to the sports equipment section. In short, the association technique is focused on finding linked properties that occur regularly.
Finally, the prediction technique tries to predict the relationship between independent variables. For instance, the prediction model helps predict future profits. To use this model, we have to feed this data mining technique with historical sales and profit data.
In addition to the data mining models we’ve discussed, many more techniques exist, including the following:
The following are some of the most important benefits of data mining:
In short, it’s clear that data mining brings a whole bunch of benefits to organizations. However, there are also challenges attached to the concept of data mining. Let’s find out what they are!
In addition, another challenge involves collecting relevant information. Often, companies gather any data they can find and don’t think about whether the collected data is relevant. However, if you collect too much data, you’ll find it more difficult to classify data and find patterns. So, you’ll want to plan in advance what data you want to collect, and you’ll want to define which data mining technique is going to use this data.
Finally, avoid collecting “complex data” that’s hard to analyze, like images, audio, video, or spatial data. Instead, focus on collecting textual data that data mining techniques can more easily process.
Certainly, data mining will continue to evolve in the future. It has gained an important role in online marketing. Online stores use data mining techniques to find buying patterns or provide users with cross-selling opportunities.
In addition, it’s important to know that data mining has also gained a lot of attention in other domains, like fraud detection. It provides a reliable approach to detect and prevent fraud. It’s often used by banks and financial institutions to detect malicious transactions.
Personally, I expect data mining to incorporate machine learning, natural language processing, and artificial intelligence to reach its true potential. So, let’s see what the future brings!
This post was written by Michiel Mulders. Michiel is a passionate blockchain developer who loves writing technical content. Besides that, he loves learning about marketing, UX psychology, and entrepreneurship. When he’s not writing, he’s probably enjoying a Belgian beer!