Back to Basics: What is Data Mining and What Tools to Use

Back to Basics: What is Data Mining and What Tools to Use

Sorcha Sheridan

Social Bee

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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).

Let’s begin.

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:

CRISP-DM Image

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.

Comparison of Data Mining and Text Mining.

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

Product recommendations

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.

Source: Twitter

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.

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Data Mining tools

Nowadays, there's a multitude of Data Mining tools you can consider implementing into your organization. However, not all are built the same and offer the same features. To help you decide which tools will cater best to your needs, we’re going to discuss some of the most popular options and their primary features below.

IBM® SPSS®

IBM® SPSS® Statistics is a powerful statistical-related software that offers three innovative solutions: Modeler, Amos, and collaboration & deployment services.

IBM® SPSS® Modeler allows Data Mining without code which makes it perfect for users with various levels of tech expertise – from beginners to professionals.

With their modeler solution, you'll receive various Data Mining tools that follow the CRISP-DM model. The best thing is these can get set up in days, not weeks or months – all thanks to their intuitive user interface and their advanced ML algorithms.

Rapid Miner

With over 1,000,000+ global users, thousands of highly-rated reviews, and a platform designed to accelerate a business's progression, Rapid Miner is another recommended tool.

Unlike many other Data Mining tools, Rapid Miner is unique. Instead of following the Data Mining without a coded route, they've gone for both. Therefore, the tool is both no-code and code-based.

Rapid Miner thrives on collaboration, which is why they've catered the platform for everybody. By including a no-code and code-based solution that works together, data scientists of all preferences can collaborate.

The success of this platform is substantial, with the software being recognized by Gartner in their 2022 marketing guide reports for DSML.

Orange

Orange is another ML software that enables Data Mining without code.  The tool is known for its easy-to-understand data analysis and visualization capabilities.

It's good for all types of Data Mining use cases, as it comes with features like heatmaps, MDS, hierarchical clustering, decision trees, and more.

Orange offers beginner-friendly onboarding and hands-on training. You'll be greeted by visual illustrations and widgets explaining the platform, which makes it easier to get your team up to speed.

Additionally, it allows for multiple add-ons. These include the ability to:

  • mine data from external data sources,
  • conduct network analysis,
  • perform Natural Language Processing, Text Mining, and more.

Conclusion

More and more companies treat data as the foundation of decision-making. Still, considering the volume of data out there, it’s impossible to extract it and analyze it manually. For this reason, many companies have now turned to Data Mining for their structured and unstructured data analysis.

Back in the day, Data Mining tools focused on structured data only. However, today’s most advanced solutions also come with Text Mining capabilities. This means that you can also use them to analyze unstructured information like social media comments, emails, or even visuals.

Now that you're here

Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.

If you liked this blog post, you'll probably love Levity.

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