Data mining. You may read that and see scary images about hackers getting access to your information or people spying on you. But the truth is, data mining has a very important and positive role in our everyday lives. Data mining helps professionals and researchers learn about how to help with humanitarian work in many countries. They can learn about the spread of diseases, climate change, discrimination, and more. Without data mining it would take months or years to get the data we need to make predictions and solve problems around the world. Organizations around the globe use data mining for projects with all kinds of applications and meaning for the business world.
Data mining is an important role for IT professionals, and a degree in data analytics can help you be qualified to have a career in data mining. But everyone in business also needs to understand data mining—it is vital to how many business process are done and how information is gleaned, so current and aspiring business professionals need to understand how this process works as well.
This guide will help you learn more about what data mining is, how it’s done, and what it means for businesses.
Simply put, data mining is the process that companies use to turn raw data into useful information. They utilize software to look for patterns in large batches of data so they can learn more about customers. It pulls out information from data sets and compares it to help the business make decisions. This eventually helps them to develop strategies, increase sales, market effectively, and more.
Data mining sometimes gets confused with machine learning and data analysis, but these terms are all very different and unique.
While both data mining and machine learning use patterns and analytics, data mining looks for patterns that already exist in data, while machine learning goes beyond to predict future outcomes based on the data. In data mining, the “rules” or patterns aren’t known from the start. In many cases of machine learning, the machine is given a rule or variable to understand the data. Additionally data mining relies on human intervention and decisions, but machine learning is meant to be started by a human and then learn on its own. There is quite a bit of overlap between data mining and machine learning, machine learning processes are often utilized in data mining in order to automate those processes.
Similarly data analysis and data mining aren’t interchangeable terms. Data mining is used in data analytics, but they aren’t the same. Data mining is the process of getting the information from large data sets, and data analytics is when companies take this information and dive into it to learn more. Data analysis involves inspecting, cleaning, transforming, and modeling data. The ultimate goal of analysis is discovering useful information, informing conclusions, and making decisions.
Data mining, data analysis, artificial intelligence, machine learning, and many other terms are all combined in business intelligence processes that help a company or organization make decisions and learn more about their customers and potential outcomes.
Almost all businesses use data mining, and it’s important to understand the data mining process and how it can help a business make decisions.
Business understanding. The first step to successful data mining is to understand the overall objectives of the business, then be able to convert this into a data mining problem and a plan. Without an understanding of the ultimate goal of the business, you won’t be able to design a good data mining algorithm. For example, a supermarket may want to use data mining to learn more about their customers. The business understanding is that a supermarket is looking to find out what their customers are buying the most.
Data understanding. After you know what the business is looking for, it’s time to collect data. There are many complex ways that data can be obtained from an organization, organized, stored, and managed. Data mining involves getting familiar with the data, identifying any issues, getting insights, or observing subsets. For example, the supermarket may use a rewards program where customers can input their phone number when they purchase, giving the supermarket access to their shopping data.
Data Preparation. Data preparation involves getting the information production ready. This is the biggest part of data mining. It is taking the computer-language data, and converting it into a form that people can understand and quantify. Transforming and cleaning the data for modeling is key for this step.
Modeling. In the modeling phase, mathematical models are used to search for patterns in the data. There are usually several techniques that can be used for the same set of data. There is a lot of trial and error involved in modeling.
Evaluation. When the model is complete, it needs to be carefully evaluated and the steps to make the model need to be reviewed, to ensure it meets the business objectives. At the end of this phase, a decision about the data mining results will be made. In the supermarket example, the data mining results will provide a list of what the customer has purchased, which is what the business was looking for.
Deployment. This can be a simple or complex part of data mining, depending on the output of the process. It can be as simple as generating a report, or as complex as creating a repeatable data mining process to happen regularly.
After the data mining process has been completed, a business will be able to make their decisions and implement changes based on what they have learned.
So why is data mining important for businesses? Businesses that utilize data mining are able to have a competitive advantage, better understanding of their customers, good oversight of business operations, improved customer acquisition, and new business opportunities. Different industries will have different benefits from their data analytics. Some industries are looking for the best ways to get new customers, others are looking for new marketing techniques, and others are working to improve their systems. The data mining process is what gives businesses the opportunities and understanding for how to make their decisions, analyze their information, and move forward.
Now that you understand why data mining is important, it’s beneficial to see how data mining works specifically in business settings.
Classification. This data mining technique is more complex, using attributes of data to move them into discernable categories, helping you draw further conclusions. Supermarket data mining may use classification to group the types of groceries customers are buying, like produce, meat, bakery items, etc. These classifications help the store learn even more about customers, outputs, etc.
Clustering. This technique is very similar to classification, chunking data together based on their similarities. Cluster groups are less structured than classification groups, making it a more simple option for data mining. In the supermarket example, a simple cluster group could be food and non-food items instead of the specific classes.
Association rules. Association in data mining is all about tracking patterns, specifically based on linked variables. In the supermarket example, this may mean that many customers who buy a specific item may also buy a second, related item. This is how stores may know how to group certain food items together, or in online shopping they may show “people also bought this” section.
Regression analysis. Regression is used to plan and model, identifying the likelihood of a specific variable. The supermarket may be able to project price points based on availability, consumer demand, and their competition. Regression helps data mining by identifying the relationship between variables in a set.
Anomaly/outlier detection. For many data mining cases, just seeing the overarching pattern might not be all you need. Data needs to be able to identify and understand the outliers in your data as well. For example, in the supermarket if most of the shoppers are female, but one week in February is mostly men, you’ll want to investigate that outlier and understand what is behind it.
These data mining techniques are key for businesses to be able to understand the information they have and better their practices.
DataMelt. DataMelt performs mathematics, statistics, calculations, data analysis, and visualization. Many scripting languages and Java packages are available in this system.
ELKI Data Mining Framework. ELKI focuses on algorithms with a specific emphasis on unsupervised cluster and outlier systems. ELKI is designed to be easy for researchers, students, and business organizations to use
Orange Data Mining. Orange data mining helps organizations do simple data analysis and use top visualization and graphics. Heatmaps, hierarchical clustering, decision trees, and more are used in this process.
The R Project for Statistical Computing. The R Project is used in statistical modeling and graphics and is utilized on many operating systems and programs
Rattle GUI. Rattle GUI presents statistical and visual summaries of data, helps prepare it to be modeled, and utilizes supervised and unsupervised machine learning to present the information.
Weka 3. Weka is a great machine learning software that is used for teaching, research, and industrial applications.
There is a steep learning curve with data mining tools, and it’s important to study and research so you’re prepared for all the data mining techniques and options that are available. A degree program in data analytics could be the perfect key to helping you learn the skills, scripting, languages, operating systems, and more to make sure you’re prepared for a data mining career.