Data Mining Explained In-Depth:
What Is Data Mining?
Data is becoming increasingly important for today’s businesses. Organizations gather 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 to help organizations classify this data and find patterns or relationships between data pieces.
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 it is.
What Is Data Mining?
It isn’t about the act of collecting data—it’s about finding relationships or discovering patterns in the raw data you’ve already collected. So, the key is to find knowledge discovery 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.
Applications
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.
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1 Marketing
Firstly, it helps the marketing team 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.
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2 Fraud Detection
By tracking spending habits, banks or financial institutions can detect fraudulent transactions. When a data mining model detects a suspicious transaction, the transaction will be flagged and halted for investigation. This is a great application to detect and even prevent fraudulent transactions.
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3 Spam Filtering
Mail providers often offer spam filters. Using data mining techniques on the thousands of emails processed daily, they can learn spam messages’ common characteristics. Some mail providers go as far as immediately removing a message before it even reaches the user’s inbox.
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4 Recommendation Systems
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. Of course, these recommendation systems’ goal is to sell more products by showing consumers products they may want to buy or may be interested in.
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5 Sentiment Analysis
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 derives their feelings. Often, social media posts serve as the input for sentiment analysis models. Besides, 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 techniques.
Techniques
Here are four of the most important techniques.
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1 Finding Patterns
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 set of data. For example, you might want to know when and why a particular product’s sales 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 determine 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.
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2 Classification
Secondly, instead of collecting huge large data sets, the classification technique looks only at the collected data’s specific attributes. For example, say you are tasked with discovering patterns in the relationship between a customer’s financial knowledge and their investments’ risk level. 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 particular data properties. In this example, we require only the purchase history and the customer’s level of financial knowledge.
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3 Association
Next, the association technique is a commonly used discovery pattern used in cross-selling products online. To give an example, you might find out that customers who buy football often buy sports shoes. Importantly, this is great for designing a shop layout because you could place the sports shoe section next to the sports equipment section. In short, the association technique is focused on finding linked properties that occur regularly.
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4 Prediction
Finally, the prediction technique tries to predict the relationship between independent variables. For instance, the prediction model helps to predict future profits. We have to feed this technique with historical sales and profit data to use this model.
In addition to the models we’ve discussed, many more techniques exist, including the following:
• Decision trees
• Sequential patterns
• Clustering
Benefits
The following are some of the most important benefits of data mining:
- Helps companies find trends or habits in their data.
- It helps companies predict the future.
- Supports decision making.
- It can increase the company’s revenue by using cross-selling or targeting
people with more personalized advertisements or offers. - Finally, it helps companies gain a competitive advantage over their
competitors.
In short, data mining brings a whole bunch of benefits to organizations. However, there are also challenges attached to the concept. Let’s find out what they are!
Challenges
First, data mining often involves collecting data about customers or users on a platform. Unfortunately, you could be violating a user’s privacy by using tools. Besides that, new regulations like GDPR make it more difficult to gather the needed data. Always make sure to mention exactly how you’re using customer data in your company’s privacy policy.
Also, 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 what data you want to collect, and you’ll want to define which technique will 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 these techniques can more easily process.
Datameer
Datameer’s SaaS data transformation platform provides the perfect platform to support the front-end of your data mining processes. With Datameer DTaaS, you can:
- Leverage an agile ELT process, using Datameer for your T directly inside of Snowflake which then provides power query compute and expansive storage for your data mining,
- Allow your non-technical staff to participate in data mining by easily transforming your data to shape and organizing it for data mining with our no-code or low-code interfaces,
- Use Datameer’s rich array of wizard-driven formulas and functions to enrich data without coding for data mining processes such as classification, association, and pattern finding
- Generate rich data documentation, attributes, tags, and other information about the data mining models to share knowledge across your entire analytics team.
With Datameer, your can make data mining an agile component of your overall analytics processes and engage your non-technical staff in the process by removing the need for highly technical programming in python and SQL.
It is 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 detecting and preventing fraud. Banks and financial institutions often use it to detect malicious transactions.
Personally, I expect it to incorporate machine learning, natural language processing, and artificial intelligence to reach its true potential. So, let’s see what the future brings.
Michiel Mulders wrote this post. 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!