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Glossary term
Data mining refers to the process of “digging through” (meaning analyzing with computers) large volumes of data in order to identify interesting anomalies, patterns, and correlations. This type of analysis has its roots in statistical techniques like Bayes’ Theorem that were initially calculated by hand. Today’s data mining is increasingly sophisticated, though, reflecting a blend of practices from statistics, data science, database theory, artificial intelligence, and machine learning.
With data mining tools, organizations of any size can extract valuable insights from their datasets, including information about consumers, costs, and future trends. This process can be employed to (a) answer business questions that were traditionally too time-consuming to address and (b) make knowledge-driven decisions based on the absolute best data available.
Detailing the techniques that power data mining is a useful way to explain how this type of analysis can best be applied and which tools are likely to be most useful for your organization. Before we dive into specific tools for data mining, let’s take a look at some common data mining techniques.
Data mining encompasses a wide range of techniques and practices, but we can essentially sort them into two main types: descriptive and predictive.
Descriptive data mining techniques are used to determine the similarities in data and to identify patterns. Examples include:
Association: This function is used to find interesting relationships and associations (hence the name) between items or values within datasets. For instance, it may be beneficial to know if certain products are often purchased together, as these items could be placed closer together in physical stores or offered as promotional packages in digital marketplaces.
Clustering: Cluster analysis is used to group together items into clusters that share common characteristics. This technique can be applied to everything from biology to climate science to psychology. In business, clustering can be used to segment customers into small groups who may be receptive to particular marketing activities.
Predictive data mining techniques are used to model future results using identified variables from the present. Examples include:
Classification: Classification generally involves a machine learning model which assigns items in a collection to predefined categories or classes. This may sound like a descriptive function, but the goal of classification is often to predict particular outcomes based on existing data. A classification model could, for instance, be used to identify loan applicants as low, medium, or high credit risks.
Regression: Regression is a statistical technique often employed in supervised machine learning that is used to (a) determine the relationship between a dependent variable and independent variables and (b) use that relationship to predict a range of numeric values, given a particular dataset. Regression can, for instance, be used to predict the cost of a product or service when variables like the cost of fuel are considered.
Your choice of technique will be determined by the use case and desired outcome.
Data scientist Clive Humby coined the catchphrase “data is the new oil” way back in 2006.
At that point, research firm IDC estimated that the amount of digital information created, captured, and replicated was roughly 1.6 exabytes or 3 million times the size of the information contained in every book ever written.
Since then, the sheer amount of digital data created and stored has, well… exploded. IDC now estimates that by 2025 the global datasphere will reach 175,000 exabytes.
The rapid growth in digital data has been driven by three main sources:
Datasets from these discrete sources are stored on servers owned (or leased) by companies large and small. And if data really is the new oil, then data mining tools are the drills we use to tap into these reserves and unlock value.
More specifically, we can say that data mining provides the backbone for both business intelligence and advanced analytics. The key difference being that business intelligence explains why something happened in the past, and advanced analytics explains why something is happening in the present and predicts what will happen if trends continue.
There are countless examples of how this can play out in practice. Here are just a few:
Data mining tools can help you learn more about consumer preferences, gather demographic, gender, location, and other profile data, and leverage all of that information to optimize your marketing and sales efforts. Correlations in purchasing behavior, for instance, can be used to create more sophisticated buyer personas that can, in turn, help you create more targeted messaging.
Financial institutions rely on data mining to help detect (and even anticipate) fraud and support other risk management functions. Transaction activity can be analyzed to spot fraudulent transactions before a customer even knows their card or account has been compromised.
Data mining and other business intelligence tools can provide insights about your entire supply chain and can even predict out-of-stock forecasts at the store/product level.
With data mining, you can unlock insights about processes and trends that never would have been available otherwise. This information can help you make more informed and ultimately data-driven decisions about key matters. For example, your intuition may be that a product isn’t selling because it’s priced too high, but data mining may reveal that it’s not being marketed to the right demographics.
HR departments in large organizations can use data mining to track employee information and uncover insights that may be useful regarding hiring, retention, and compensation planning. Data mining is especially useful in recruiting, as it can uncover important information in résumés and applications that simple keyword screening may miss.
However you choose to deploy data mining, you’ll need to be equipped with the right tools to see the highest return on value. So how do you go about choosing the best data mining tool for your needs? Let’s take a look at how you can evaluate the various options available to make the right decision.
With so many free tools available, one of the most difficult tasks in the entire data mining process is simply choosing the right tool for your business. Open source tools are a good place to start, as they are constantly being updated (towards greater flexibility and efficiency) by an extensive development community
Open source data mining tools share many of the same characteristics, but there are several key distinctions. Here are a few things to consider when choosing the best data mining tools for your organization.
Tools may offer different models for integrating new data, with possible limitations on data format and data size. Some tools are better suited for large datasets, others for smaller sets. Consider the types of data you’ll be working with most frequently when evaluating your options. If your data currently lives in many different systems or formats, your best bet is to find a solution that can handle that variance.
Each tool will offer different user interfaces to facilitate your interaction with the work environment and engagement with the data. Some tools are more geared towards education, and focus on providing general knowledge of analytical techniques. Others are optimized for business applications, guiding users through the process of solving a specific problem.
Most (but not all) open source programs are written in Java, but many can also use R and Python scripts. It’s important to think about the languages your programmers will be most comfortable in and whether they’ll be working with non-coders on data analysis projects.
Whatever tool you choose, you want to ensure that it will be able to handle your data and, ultimately, deliver results for your desired application.
RapidMiner is a powerful data mining tool that enables everything from data mining to model deployment, and model operations. Our end-to-end data science platform offers all of the data preparation and machine learning capabilities needed to drive real impact across your organization.
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