What Is Data Mining? A Beginner's Guide (2022) | Rutgers Bootcamps (2024)

The more data we produce, the more difficult it becomes to make sense of all that data and derive meaningful insights from it. Think of standing among trillions of trees; where do you start analyzing the forest?

Data mining provides a solution to this issue, one that shapes the ways businesses make decisions, reduce costs, and grow revenue. As a result, a variety of data science roles leverage mining as part of their daily responsibilities.

Data mining is often perceived as a challenging process to grasp. However, learning this important data science discipline is not as difficult as it sounds. Read on for a comprehensive overview of data mining’s various characteristics, uses, and potential job paths.

Explore this article:

• What is data mining?
• The history of data mining
• The differences between data mining and machine learning
• Phases of data mining
• Most common types of data mining
• Best uses of data mining
• Data mining careers
• Tips for considering a data science career
• Data mining FAQs

What Is Data Mining?

Data mining is most commonly defined as the process of using computers and automation to search large sets of data for patterns and trends, turning those findings into business insights and predictions. Data mining goes beyond the search process, as it uses data to evaluate future probabilities and develop actionable analyses.

History of Data Mining

Did you know that the concept of data mining existed before computers did? The statistical beginnings of data mining were set into motion by Bayes’ Theorem in 1763 and discovery of regression analysis in 1805. Through the Turing Universal Machine (1936), the discovery of Neural Networks (1943), the development of databases (1970s) and genetic algorithms (1975), and Knowledge Discovery in Databases (1989), the stage was set for our modern understanding of what data mining is today. And, as the growth of computer processors, data storage, and technology exploded during the 1990s and 2000s, data mining became not only more powerful, but also more prolific in all kinds of situations.

In 2003, the book Moneyball introduced data mining to a much broader audience through the story of a professional baseball team’s analytics-driven approach to roster building. Now, with companies employing big data solutions in a growing variety of situations, data mining plays a critical role in countless industries.

Differences Between Data Mining and Machine Learning

Data mining and machine learning are unique processes that are often considered synonymous. However, while they are both useful for detecting patterns in large data sets, they operate very differently.

Data mining is the process of finding patterns in data. The beauty of data mining is that it helps to answer questions we didn’t know to ask by proactively identifying non-intuitive data patterns through algorithms (e.g., consumers who buy peanut butter are more likely to buy paper towels). However, the interpretation of these insights and their application to business decisions still require human involvement.

Machine learning, meanwhile, is the process of teaching a computer to learn as humans do. With machine learning, computers learn how to determine probabilities and make predictions based on their data analysis. And, while machine learning sometimes uses data mining as part of its process, it ultimately doesn’t require frequent human involvement on an ongoing basis (e.g., a self-driving car relies on data mining to determine where to stop, accelerate, and turn).

How Does Data Mining Work?

To fully answer the question “What is data mining?” a working knowledge of the overall process is needed. Data mining follows a fairly structured, six-step method known as the Cross-Industry Standard Process for Data Mining (CRISP-DM).

This process encourages working in stages and repeating steps if necessary. In fact, repeating steps is often essential to account for changing data or to introduce different variables.

Phases of Data Mining

Let’s take a closer look at each phase of the CRISP-DM:

Business Understanding

To get started, first ask these questions: What is our objective? What problem are we trying to solve? What data do we need to solve it?

Without a clear understanding of the proper data to mine, the project can produce errors, inaccurate results, or results that don’t answer the correct questions.

Data Understanding

Once the overall objective is determined, proper data needs to be collected. The data must be relevant to subject matter and usually comes from a variety of sources such as sales records, customer surveys, and geolocation data. This phase’s goal is to ensure the data correctly encompasses all necessary data sets to address the objective.

Data Preparation

The most time-consuming phase, the preparation phase, consists of three steps: extraction, transformation, and loading — also referred to as ETL. First, data is extracted from various sources and deposited into a staging area. Next, during the transformation step: the data is cleaned, null sets are populated, duplicative data is removed, errors are resolved, and all data is allocated into tables. In the final step, loading, the formated data is loaded into the database for use.

Modeling

Data modeling addresses the relevant data set and considers the best statistical and mathematical approach to answering the objective question(s). There are a variety of modeling techniques available, such as classification, clustering, and regression analysis (more on them later). It’s also not uncommon to use different models on the same data to address specific objectives.

Evaluation

After the models are built and tested, it’s time to evaluate their efficiency in answering the question identified during the business understanding phase. This is a human-driven phase, as the individual running the project must determine whether the model output sufficiently meets their objectives. If not, a different model can be created, or different data can be prepared.

Deployment

Once the data mining model is deemed accurate and successful in answering the objective question, it’s time to put it to use. Deployment can occur in the form of a visual presentation or a report sharing insights. It also can lead to action such as generating a new sales strategy or implementing risk-reduction measures.

Most Common Types of Data Mining

Data mining is most useful in identifying data patterns and deriving useful business insights from those patterns. To accomplish these tasks, data miners use a variety of techniques to generate different results. Here are five common data mining techniques.

Classification Analysis

With this technique, data points are assigned to groups, or classes, based on a specific question or problem to address. For instance, if a consumer packaged goods company wants to optimize its coupon discount strategy for a specific product, it might review inventory levels, sales data, coupon redemption rates, and consumer behavioral data in order to make the best decision possible.

Association Rule Learning

This function seeks to uncover the relationships between data points; it is used to determine whether a specific action or variable has any traits that can be linked to other actions (e.g., business travelers’ room choices and dining habits). A hotelier might use association rule insights to offer room upgrades or food and beverage promotions to attract additional business travelers.

Anomaly or Outlier Detection

In addition to searching for patterns, data mining seeks to uncover unusual data within a set. Anomaly detection is the process of finding data that doesn’t conform to the pattern. This process can help find instances of fraud and help retailers learn more about spikes, or declines, in the sales of certain products.

Clustering Analysis

Clustering looks for similarities within a data set, separating data points that share common traits into subsets. This is similar to the classification type of analysis in that it groups data points, but, in clustering analysis, the data is not assigned to previously defined groups. Clustering is useful for defining traits within a data set, such as the segmentation of customers based on purchase behavior, need state, life stage, or likely preferences in marketing communication.

Regression Analysis

Regression analysis is about understanding which factors within a data set are most important, which can be ignored, and how these factors interact. With this technique, data miners are able to validate theories such as “when a lot of snow is predicted, more bread and milk will be sold before the storm.” While this seems obvious enough there are a number of variables that need to be verified and quantified for the store manager to make sure enough stock is available. For example, how much is “a lot” of snow? How much is “more milk and bread”? Which types of weather forecasts tend to cause consumer action and how many days before the storm will consumers start buying? What is the relationship between inches of snow, units of bread, and units of milk?

Through regression analysis, specific inventory levels of milk and bread (in units/cases) can be recommended for specific levels of snow forecasted (inches), at specific points in time (days before the storm). In this way, the use of regression analysis maximizes sales, minimizes out-of-stock instances, and helps avoid overstocking which results in product spoilage after the storm.

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Best Uses of Data Mining

Businesses use data mining to give themselves a competitive advantage by harnessing the data they collect on their customers, products, sales, and advertising and marketing campaigns. Data mining helps them sharpen operations, improve relationships with current customers, and acquire new customers.

Businesses that don’t employ data mining techniques may fall behind their competitors. These are some of the primary ways businesses use data mining to avoid such shortcomings.

Basket Analysis

In its most basic application, retailers use basket analysis to analyze what consumers buy (or put in their “baskets”). This is a form of the association technique, giving retailers insight into buying habits and allowing them to recommend other purchases. A less familiar application is one used by law enforcement, where vast amounts of anonymous consumer data is analyzed looking for combinations of products one would use in bomb-making or the production of methamphetamine.

Sales Forecasting

Sales forecasting is a form of predictive analysis to which businesses are devoting more of their budgets. Data mining can help businesses project sales and set targets by examining historical data such as sales records, financial indicators (e.g., consumer price index, S&P 500, inflation markers), consumer spending habits, sales attributed to a specific time of year, and trends which may impact standard assumptions about the business. According to a recent MicroStrategy survey, 52 percent of global businesses consider predictive data their most important form of analytics.

Database Marketing

Businesses build large databases of consumer data that they use to shape and focus their marketing efforts. These businesses need ways to manage and harness this data to develop targeted, personalized marketing communications. Data mining helps businesses understand consumer behaviors, track contact information and leads, and engage more customers in their marketing databases.

Inventory Planning

Data mining can provide businesses with up-to-date information regarding product inventory, delivery schedules, and production requirements. Data mining also can help remove some of the uncertainty that comes with simple supply-and-demand issues within the supply chain. The speed with which data mining can discern patterns and devise projections helps companies better manage their product stock and operate more efficiently.

Customer Loyalty

Businesses — particularly retailers — generate an enormous amount of data through loyalty programs. Data mining allows these businesses to build and enhance customer relationships through that data. For example, by clustering customers according to basket totals, shopping frequency, and likely grocery spend per week, retailers can offer customers discounts to “ratchet” them up to a spending level (e.g., spend $50 get $5 off; spend $75, get $10 off). This not only provides the customer with an incentive to shop, but it also helps to retain dollars being targeted by competitors.

Careers That Use Data Mining

Employment opportunities are growing for those skilled in data mining. Jobs in computer and information technology are projected to increase by 11 percent through 2029, according to the U.S. Bureau of Labor Statistics. Careers that focus on big data, database administration, and information security all employ data mining methods.

The following are a few top positions that use data mining techniques.

Database Administrator

Database administrators play vital roles in storing, securing, and potentially restoring a company’s data; they ensure that analysts can access the right data when they need it. Database administration is an expanding field (with 10 percent projected job growth, according to the BLS) with strong salary potential. The median annual salary in the U.S. for this profession is $98,860.

Computer and Information Scientist

Computer and information scientists design new technology (computer languages, operating systems, software, etc.) in a rapidly expanding space and are always searching for new ideas. They work in fields like finance, technology, healthcare, and scientific exploration. Job opportunities are abundant (15 percent projected growth by 2029, per the BLS), and the median annual salary is $126,830.

Market Research Analyst

Research analysts conduct marketing studies to help companies target new customers, increase sales, and determine the sales potential of new products. The growth of ecommerce is fueling growth in this field; CareerOneStop projects an 18 percent increase in job opportunities by 2029. The median U.S. salary is $65,810, with salaries in the New York/New Jersey region reaching $81,270.

Computer Network Architect

Network architects design, build, and maintain a company’s data communications network, which can range from a few computers to a large, cloud-based data center. Healthcare is contributing to the profession’s expanded job options (a 5 percent projected job growth by 2029, per the BLS) as providers digitize more health records. The median annual salary is $116,780.

Information Security Analyst

Digital security experts have become indispensable to almost any organization needing to protect sensitive data and prevent cyberattacks. In fact, with 31 percent projected employment growth, even more jobs in this field will likely become available in the future. The field is also reasonably accessible for those entering from other industry concentrations. For example, database administrators can be strong candidates for roles in database security. Information security carries a median salary of $103,590.

Tips for Considering a Data Science Career

Interested in pursuing a career working with data? Consider these helpful tips as you work toward landing a job in the field:

What Role Do You Want to Pursue?

Data mining is a valuable skill for a variety of industries. As a result, having data-specific knowledge of a particular industry can help pave a clearer path. For instance, if you’re familiar with banking, healthcare, or marketing, you can apply data mining techniques to those fields and pinpoint which roles are available.

Familiarize Yourself With the Basics

Become more familiar with the data mining industry’s common tools and technology. Knowing more may help spark a particular interest and help you determine your ideal career path. Refresh your knowledge of statistics, study a basic programming language, or dig deeper into machine learning.

Join a Data Science Bootcamp

A data science bootcamp can provide an introduction to data mining and a path to a new career. Bootcamps specialize in delivering concentrated learning opportunities in coding, data science, and cybersecurity, among other disciplines. In a 24-week data science program, students learn fundamental statistics, multiple programming languages, and big data analytics.

For professionals looking to expand their roles and transition to a technology career, a data science bootcamp can be a great entry point. According to a HackerRank 2020 survey, more than 70 percent of hiring managers said bootcamp graduates were as qualified as (or more than) other hires.

Programs like Rutgers Data Science Bootcamp offer a curriculum entailing a variety of crucial industry skills. These skills are learned through practical instruction simulating real-world experience. To begin your journey as a data miner, consider applying to Rutgers Data Science Bootcamp.

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