"Data mining" sounds pretty self-explanatory, but it's more complicated than you might think.
Data mining is more than just extracting or mining data. It also involves turning raw data into insights that can be used to make decisions. And while that definition seems vague, it has to be because data mining is a process that can be applied to many industries to help them chart a better path to the future.
Every day, more and more businesses turn to data mining because the data we store only continues to grow. It increased dramatically when desktop computers became commonplace in the 1980s. Then came the Internet. Then, smartphones made it possible for almost everyone to generate even more data with a computer they carried around with them.
And now, IoT devices that constantly gather data about the world around them are taking off. Currently, the total volume of this data doubles every year and a half.
In this article, we'll look at what data mining does, the processes it uses to extract information and patterns, its benefits, the industries that use it, and more. Then, we'll show you how to start exploring data mining on your own.
The process of data mining
Data mining almost always starts with data collection. This data can be pulled from records, logs, website analytics, customer and sales data, IoT sensor data, and more.
The type of data available determines what kind of information and insights can be drawn from it. So, the data mining process must be planned strategically from the beginning to help a business answer questions, solve problems, or meet goals.
A popular guideline among Data Scientists for implementing this process is the Cross-Industry Standard Process for Data Mining (or CRISP-DM). The CRISP-DM provides a flexible set of general steps for data mining efforts and has six phases. Let's take a look at these phases.
Any data mining process must start with a goal in mind.
The first phase of data mining focuses on understanding the business, its objectives, and the project's requirements. In this phase, business stakeholders help determine what questions data mining can answer or problems it can solve. This discovery step will become the foundation of all the steps that follow.
Once a data mining project has a goal and the business' needs are understood, it's time to determine what type of data is needed. This is followed by collecting the data and interpreting it.
This data may exist in multiple databases, data stores, and file systems and could either be raw or structured. In this step, the shape, quality, and location of the data are determined. Data visualization tools may also be used to identify how to apply the data to the goal.
Once the data is understood, it's time to prepare it for modeling. This often involves some amount of data cleaning — such as addressing missing values, inconsistent formatting, or other issues — so that these errors in the data set don't skew results.
The next step in this phase is transforming the data into a useful form since much of it may be raw. Or it may need to be converted to another unit of measure. Since data often comes from multiple sources, it may also be combined into a unified data set in this phase.
This phase is where machine learning enters the data mining process. Data Scientists determine which modeling algorithms will work best to gather the needed insights from the data.
The techniques used can include linear regression, deep learning, clustering, classification, and more. In this phase, various models will be created, tested, modified, and compared against each other to determine which models will work best based on the test data.
This phase builds on the modeling completed with a comprehensive assessment of the modeling results. Their success at answering the questions the business needs answered is also determined. It could be that some things weren't accounted for when asking the question or creating the models. So, either one may need to be modified.
Once the best model has been determined, it's time to deploy it to the live environment. Up to now, everything in the process was done in a testing environment, most likely with some manual steps. This phase is focused on streamlining that process, so the model and any software related to it can be deployed quickly to a production environment with no hitches.
Maintenance and monitoring plans for the model are also set up in this phase, and the process of delivering the results to stakeholders is created. Reports can be delivered by email, visiting a web application, or some other method.
What are the benefits of data mining?
Data mining benefits modern businesses by allowing them to discover insights in their data and use those insights to modify their processes and decisions. Some of data mining's other benefits include:
Businesses often use data mining to increase profit. This could come from marketing campaigns that are targeted more effectively, better-suggested products at checkout, or other results that come from better understanding customer data through data mining.
Many businesses also use data mining for fraud detection. A Data Scientist can harvest known fraud cases from a business's historical transactions and use the resulting data to generate models for real-time machine learning. These models can stop fraud before a transaction completes and money is lost.
Another fraud detection method detects anomalies and outliers in data and flags them for review just in case the abnormal behavior results from fraud or other malicious activities.
Data mining can also help businesses save money. Retailers use data mining to spot market trends and forecast product demand so they can keep their inventories stocked appropriately. Modern manufacturing plants use the data from sensors on industrial machines to schedule maintenance before a machine breaks down and brings the manufacturing process to a halt.
Modern web analytics can tell you a lot about a website's visitors. Site tracking data is often used to understand the differences between users who become customers and those who don't. This data can be used to perfect a website's design and conversion rate.
Another benefit of data mining that applies to just about everything it's used for is less guesswork. Data mining uses the patterns found in historical data to predict future trends with amazing accuracy. This type of information can improve the decision-making process in many industries and scientific fields.
Where is data mining used?
Data mining has been utilized across many sectors to evaluate, refine and scale business practices. Here are some examples of how data mining is used:
Data mining is used to take some of the guesswork out of marketing, using constantly growing databases of personal data collected in marketing campaigns to improve market segmentation.
Marketing agencies collect details like customer gender, age, education level, location, tastes, and more to predict future behavior. Then, they use this data to create personalized marketing campaigns for specific audiences, determine their interests based on their searches, and more.
Data mining is used in the healthcare industry to allow healthcare professionals to diagnose illnesses more effectively.
By having all of a patient's data available, including medical records, physical exams, and prescription information to compare to the historical data of other patients with similar symptoms, doctors can prescribe more effective treatments.
Data mining is also used in healthcare to analyze X-rays, predict illnesses and their spread through social media, and forecast the length of hospital admissions for more cost-effective resource management.
In the banking and financial industries, data mining is used to better understand market risks.
Machine learning models created from historical purchasing patterns, card transactions, and financial data are used by anti-fraud systems to prevent business losses. Credit reporting agencies also use the same type of data to determine credit ratings.
Banks also use data mining in their marketing efforts to optimize the return on campaigns and determine how sales channels are performing.
Both online retailers and brick-and-mortar stores use data mining to analyze business metrics and improve sales. E-commerce sites use customer data and records of clicks and actions on their pages to help them create more effective marketing campaigns, ad campaigns, and offers targeted at specific customers.
Retailers also mine data from the past orders of all their customers to find upselling and cross-selling opportunities. In physical stores, data mining helps determine the placement of products in the store and on the shelves.
The entertainment industry uses real-time data mining to measure audiences and learn more about their tastes and behaviors. They also provide much of this information to advertisers so they can target potential customers more effectively. Online services use this data to create personalized recommendations for their users to keep them engaged.
Getting started with data mining
Data mining is a diverse discipline that combines database management with statistics and machine learning and touches just about every industry you can think of. So, there are quite a few paths you can take to become a data mining specialist.
SQL is definitely a good place to start. Much of the data used in the data mining process is stored in relational databases, and our Learn SQL course will teach you how to use the language to manage them.
For the data understanding and data preparation phases of data mining, you'll need other skills. Our Visualize Data with Python course will teach you how to use charts and graphics to better understand your data before creating models.
How to Clean Data with Python will show you how to pull the data you need from unstructured and inconsistent data sets. And, How to Transform Tables with SQL will teach you how to turn the data you have into the data you need.
For the modeling phase of data mining, there are a few options. Python is one of the popular programming languages used for data mining, and Analyze Data with Python will teach you to use this language in your data mining pipeline. Plus, Build a Machine Learning Model in Python will get you started with machine learning.