It's easier than ever to collect a lot of data. But having this data isn't enough to provide value — you need a way to make sense of the information you collect. Data mining allows you to sift through information, separate out anomalies, find patterns and gain insight.
Organizations in many industries, such as retail, health care, and manufacturing, use data mining extensively to strengthen their customer relationships, maximize functionality and save money. Take a closer look at what data mining is used for and various data mining applications.
Data mining uses automation, machine learning, and computers to unveil patterns and discover valuable information in large sets of data. It's more involved than a simple search of data. The process uses available information to develop analyses and determine probabilities. Through data mining, a business can make predictions and develop insights.
Multiple data mining techniques exist, but at their heart, they have two primary goals. One goal involves the use of machine learning algorithms to make predictions. The other focuses on creating a description of the target data.
Three disciplines provide the backbone for modern-day data mining:
Data mining as data collection has grown as computing power has become more affordable. Data mining is automated and quick as it mostly eliminates manual and time-consuming tasks, allowing for the collection of ever-more complex data sets. Thanks to data mining, industries can gain faster insights and reveal connections that allow them to optimize prices, target particular demographics and understand risk and competition.
The name “data mining” might be relatively new, but the concept is old. Data mining dates back to a time before computers. Sometimes known as knowledge discovery in databases, the precursor of data mining might be Bayes' Theorem. Bayes' Theorem is a formula that allows you to determine conditional probability.
The theorem is named after Thomas Bayes, a mathematician from the 18th century. It was developed in the mid-1700s and is used to determine the likelihood that something will occur, based on previous occurrences in similar situations. As new data enters the picture, Bayes' Theorem allows for the revision of predictions. Like modern-day data mining, Bayes' Theorem has multiple applications.
Data mining was also jumpstarted by the development of the Method of Least Squares, a type of regression analysis, in the early 1800s. Regression analysis estimates the relationship between dependent and independent variables using a set of statistical methods. It also allows for the modeling of potential future relationships between variables.
Jump forward to the 20th century, and the scene was laid for data mining as it exists today. One example of early 20th-century data mining is the Turing Universal Machine. Developed by Alan Turing, the “father of modern computer science,” the Turing machine uses a rote method to accomplish any task. It was a revolutionary idea in the 1930s, even though it seems commonplace today.
Near the end of the 20th century, the development of databases, algorithms and knowledge discovery in databases, combined with ever-faster computer processors and increasingly large data storage capabilities, transformed data mining into a powerful and prolific process.
Data mining typically follows a six-step process, called the Cross-Industry Standard Process for Data Mining. The process is circular and allows steps to be repeated when and as needed. The steps are as follows:
The business understanding phase of the process typically involves reflecting on the organization's goals and objectives. One way to think of this phase is as an opportunity to zero in on your business's primary area of concern. Some questions to ask in this phase include:
In the second phase of the process, you begin collecting data. Ideally, the data you gather will appropriately address your goals and allow you to reach them. This information can come from multiple sources, such as surveys, geolocation data, and sales. Evaluate data quality at this time, familiarize yourself with it and discover any initial insights.
Once you have the relevant data, you need to prepare it. Along with business understanding, the data preparation phase can be the most time-consuming. Data preparation contains three parts — extraction, transformation and loading (ETL).
During extraction, the data is collected from the sources and put into a staging area. It's then cleaned, or transformed. During transformation, errors are corrected, duplicates eliminated and null sets populated. The data then gets allocated into appropriate tables. During loading, the data gets placed into a database.
The next step, data modeling, decides how best to solve the problem or address your organization's problem. Data modeling techniques include clustering, regression analysis and classification. You might use multiple models on the same type of data, depending on your overall goals.
Data evaluation takes place after you build and test your models. The goal of evaluation is to assess the efficiency of each model to see how it addresses the problems and goals you identified during the business understanding step. If a model doesn't appropriately address or meet objectives, you can develop a new one or attempt to use a different data set.
Finally, if all goes well and the data model is successful, it's time to deploy it. Deployment can take multiple forms, depending on the overarching goals. A company might develop a new sales approach or put measures into place to reduce risk.
Data mining tools include algorithms and rules that transform abundant data into usable information. Several of the more commonly used techniques and tools include:
No matter your industry, data mining offers several benefits, including:
Data mining has applications across multiple industries. Some industries stand to particularly benefit from data mining projects.
Whether large or small, retailers can use data mining in many ways to improve sales, increase customer retention and manage inventory levels. Retailers can also use data mining to track the effectiveness of sales and promotions.
A retailer can use data mining to sort its customers into categories based on their purchase habits and frequencies. The retailer can then target those customers with promotions and marketing that are most relevant to their needs and buying style. Often, customers get sorted into groups based on how recently they purchased, how frequently they purchase, and how much they spend per purchase.
To determine who goes where, a retailer needs data on frequency, time, and date of purchase and purchase amount. Customers who made a purchase within the past week go into one group. Customers who haven't purchased within the past year fall into another. The retailer might send an email to the customers who haven't bought anything in a year or more, providing them with a coupon or discount. Customers in the recent-purchase category might get an email that thanks them and offers them a coupon for their next purchase.
A retailer can also use data mining to determine staffing levels at a particular location. Based on sales volume, a retailer might decide to have more employees on the clock in the late afternoon to accommodate a higher volume of customers during that time.
Beyond retail, any industry that works with customers or uses a customer relationship management (CRM) system can benefit from data mining. Using data mining, you can make predictions about your customer's behavior. It's an excellent way to forecast future sales. Looking at past sales volume or service requests, you can pinpoint exactly when people are likely to buy products or schedule services. You can then adjust your inventory to accommodate an uptick or downtick in sales.
Data mining also allows you to identify customer issues, such as a sudden drop-off in orders or sales or an increased rate of complaints. The data you gather allows you to make changes to your processes to keep customers happy and increase retention.
Data mining for CRM can also lead to higher loyalty levels, reduced fraud, and better marketing segmentation.
Data mining in health care can lead to an improved quality of care for patients. During a visit, a doctor gathers the necessary information about a patient, including their past medical history, current symptoms, allergies and medications. Data mining automates the analysis of the patient's information, helping a doctor pinpoint a diagnosis more quickly.
Data mining also streamlines treatment and can potentially reduce patient risk. A patient with a particular condition or taking a certain medication might not be a good candidate for the standard treatment for another illness. Analysis of the patient's data, compared to other details and information, allows a doctor to quickly detect any potential drug interactions or issues. It allows them to choose a treatment that will be more effective and less risky.
In a broader sense, data mining can help the healthcare industry discover larger patterns, such as disease clusters in certain regions. It can also reduce fraud in the industry by ensuring providers only bill for services completed or that providers don't bill for excess treatments.
Data mining has multiple uses in the manufacturing industry. It can help streamline the manufacturing process by allowing companies to identify areas of inefficiencies. It can also reduce costs by allowing an organization to compare the difference between using one type of material or working with one supplier compared to another.
Similarly, data mining allows manufacturers to develop a maintenance plan for machinery and equipment that minimizes downtime and increases efficiency. A manufacturing company can analyze data regarding the breakdown timeline for equipment and the recommended maintenance frequency to keep machinery operational for as long as possible.
To get the most out of data mining, you need a tool that's intuitive, efficient, flexible, and scalable when used for data testing, validation, and reconciliation. Dextrus's Data Wrangler allows you to prepare & analyze, compare datasets, reconcile and validate data, and report your results. As a no-code platform, both tools are also user-friendly.
Dextrus can help sift through any data anomalies, which reduces financial risk, as well as credibility and compliance damages. You can use Dextrus - Data Wrangler and RDt's testing suite for the following:
If you're ready to start data mining or want to simplify your data journey, RightData can help. With Dextrus, you gain valuable insights into your data through advanced analytics, machine learning, and reporting.
See how the platform works for you by scheduling a demo today.