If there were a single element most businesses, organizations, and decision-makers had to rely on in their daily work, it would be data. In the modern digital world, data is the essence of everything. Every transaction you make and every search engine query is a data point that can benefit someone.
But all this data would be worthless without data mining – a process that serves to find sense in all the information that’s out there.
Summary: Fata mining strives to identify and track patterns in data sets. Without data mining, companies wouldn’t be able to transform raw data into meaningful information. Different types of mining techniques include clustering, classification, association rule learning, regression, summarization, and others. Common data mining examples can be found in retail, marketing, health and insurance, manufacturing, banking and finance, and many other sectors.
What is Data Mining?
Data mining is the process of identifying patterns and relationships in data sets. In simpler words, data mining helps companies transform raw data into vital information.
Analysts use a range of data mining techniques and tools to predict trends and make better business decisions. The process often requires using software platforms that identify patterns in data batches.
Data mining is an essential part of general data analytics and is also one of the core data science disciplines.
If we were to dive deeper, data mining is also important for the process of knowledge discovery in databases (KDD), data processing, and data gathering.
Data mining mostly depends on warehousing, effective collection of data, and computer processing. Corporations can use this process to gain insights into just about anything – what their potential clients want to buy, what they’re interested in, but also to filter spam or detect fraud.
How Does Data Mining Work?
The data mining process is mainly about analyzing and exploring large data sets in search of meaningful trends and patterns. This process is used in almost every industry for a range of insights, like database marketing campaigns, fraud detection, credit risk management, spam filtering, and discerning user opinion.
Data mining is usually performed by business intelligence analysts, data analysts, analytics professionals, executives, and employees who work as citizen data science specialists in organizations.
The key elements of data mining include statistical analysis and machine learning. Of course, data management is another core part that serves to prepare data for analysis.
Machine learning algorithms and AI have made it possible to automate most data mining processes and work with larger data sets. This includes transaction records, customer databases, web server logs, and more.
A usual data mining process consists of the following steps:
First, companies and organizations work on collecting data and loading them into company-owned warehouses. They identify critical data and assemble it. This data can be located in various source systems (data lakes, data warehouses, external data, etc.).
After gathering information, companies go through the process of cleaning it. This part is important because it removes unnecessary or “dirty” data that isn’t relevant to the research. In short, this step is about removing incomplete data from a data set.
Data integration is when different data sources such as data cubes, databases, or files get combined for analysis. Data integration is important because it helps improve the speed and accuracy of the process.
Microsoft SQL, Oracle Data Service Integrator, and other tools often perform data integration.
Data reduction occurs when analysts want to get relevant data for their analysis from one data collection. This step is often performed with Naive Bayes, neural networks, or decision trees (more on them below).
Popular data reduction strategies include:
Dimensionality reduction – controlling the number of attributes
Numerosity reduction – replacing original data with smaller data representation forms
Data compression – compressing original data
At this point, data gets transformed into a suitable format for data mining. Data analysts can use different strategies to achieve results:
Smoothing – clustering and other regression techniques
Normalization – applying summary operations to data
Aggregation – scaling data to fall in a smaller range
Discretization – replacing raw numeric data values with intervals
In this step, analysts identify insightful patterns as a result of the research. They use summarization and visualization techniques to create presentations about their findings.
The next step in the process is data analysis or interpretation. The results of data mining serve as a basis for creating analytical models that help make better business decisions and take more informed actions.
Data scientists or other team members communicate their findings to their executives at this stage. They can rely on data visualization and storytelling techniques to present the results in an easy-to-grasp matter.
Warehousing and Data Mining Software
Data mining programs analyze patterns and relationships in data sets based on the user’s request. Companies can use software to create information classes. For example, a restaurant may want to use data mining to decide whether it should offer some specials. They can assess the collected data and create classes depending on when their clients visit and what they order.
Warehousing is a crucial data mining aspect for companies that centralize their data into programs or database systems. A data warehouse allows an organization to spin off some data segments for specific users to use and analyze.
Many companies use cloud data warehouses to store their data. This way, smaller companies can leverage digital solutions for analytics, storage, and security.
Data Mining Techniques
Data scientists rely on a range of techniques to perform their work. The most common data mining techniques include:
Clustering is about identifying groups with similar characteristics. Marketers often engage in clustering to identify subgroups within a particular target market. You can also use clustering when uncertain about the similarities that may exist inside a data set.
Classification allows for splitting data set elements into different categories during data mining. With this technique, pre-defined classes get assigned to objects inside a set. The classes describe specific characteristics of the items in that set that they have in common. This great technique allows for better data categorization and summary across product lines.
Classification often comes after clustering, but this is not always the case.
Some classification methods include decision trees, logistic regression, Naive Bayes classifiers, and more.
Association Rule Learning
Association rule learning (also known as market basket analysis and relation technique) consists of if-then statements that serve to identify connections between elements inside a data set. Data scientists rely on a range of support and confidence criteria to assess those relationships.
Support is the measure of how often an element is mentioned in a data set, while confidence measures the number of times if-then statements are accurate.
For example, an association rule technique could review a sales history to identify products most often purchased together. This way, the business can promote and plan future sales accordingly.
Decision trees can predict or classify an outcome based on a list of decisions or criteria. This technique is used for creating classification models that look like trees.
Analysts use decision trees to decide on the process they want to proceed with: A or B. They often require less effort to prepare data during the pre-processing stage than other algorithms, making them beneficial in data mining.
Regression is an advanced statistical tool used by data scientists in predictive analysis. It’s a popular technique that helps boost engagement in social media or mobile apps and helps forecast future sales.
Data scientists often use regression together with classification.
The summarization technique serves to group data into easy forms. Data scientists can use summarization to create a graph, calculate average values from a data set, and more.
Sequence or path analysis includes data mining that seeks patterns in which events or values lead to later ones.
Neural networks are algorithms that simulate human brain activity. These networks find their use in complex pattern recognition applications that include deep learning.
Data is processed in neural networks using nodes. Usually, a node is made of weights, input data, and output data. This data is then mapped through deep learning.
Text mining is a technique used to analyze how often certain words are used. This is especially useful for marketing purposes for sentiment analysis, personality analysis, and social media post analysis. Text mining can also be used to identify potential data leaks coming from the employees.
Predictive analysis leverage historical data to create mathematical or graphical models for forecasting future outcomes. This analysis type often overlaps with regression analysis, and it’s made to support unknown future figures based on the available data.
Anomaly detection is a technique for treating data pieces that don’t fit the regular pattern. This technique is beneficial for fraud detection.
Importance of Data Mining
Data mining is essential for successful analytics processes and initiatives for companies and organizations. The information available upon data mining is used in advanced analytics applications, business intelligence, real-time applications that treat data streaming, and more.
Also, data mining is important for creating business strategies and managing and performing operations. This can include all functions oriented towards the client, including advertising, marketing, sales, and customer support, as well as supply chain management, manufacturing, HR, and finance.
Additionally, fraud detection, risk management, and other business elements depend on effective data mining.
Last but not least, data mining is often used in governmental institutions, healthcare, maths, science, sports, and other fields.
Data Mining Examples
In the modern era of information, almost all industries and departments can benefit from data mining. This vague process encompasses an array of applications as long as there’s enough data.
Below are common examples of data mining in different settings.
The shopping and retail market is packed with data. Analysts in this sector need to be skilled in managing and using large data sets under different patterns. These analyses are often done with the market basket technique. It relies on the theory that if a person buys a specific group of items, they are more likely to buy another group of products.
Using data mining techniques, retailers can understand the purchase behavior of their buyers. And with the differential analysis, they can compare results from different stores, customers from different demographics, etc.
Another famous example of data mining in retail is the Target case where the company started sending a teenage girl baby product coupons when the girl didn’t even know she was pregnant. The company used data mining to note a change in the teenager’s purchasing habits, which led them to conclude that she may be pregnant. The teenager only discovered that she indeed was carrying a baby after she started seeing the ads.
Media and Entertainment
There are plenty of data mining examples in the media. In fact, this happens every time you watch a Hulu or Netflix show. These streaming platforms rely on viewer data to recommend movies or shows they may appreciate. The platforms also use their databases to develop program characteristics that would be particularly popular. Then they go ahead and produce that type of program.
Some would argue that data mining is what allowed Netflix to become more popular than Hollywood when it comes to determining the kind of content viewers want to see.
Traditional fraud detection methods often take too much time to consume and come with so much data. That’s why data mining is better at providing insight into patterns and turning data into useable information. Fraud detection data mining is used by online store owners, banks, governments, etc.
In computerized banking, every new transaction comes with incredible amounts of information. Data mining helps solve finance and banking problems by identifying the data’s causalities, patterns, and correlations.
Banks also use data mining to create financial risk models, identify fraudulent transactions, and vet credit applications. Different techniques can also be used to identify potential upsells to existing clients.
Google, Facebook, and similar platforms rely on data mining techniques to help advertisers reach more customers by targeting content. Have you ever looked at an item to purchase on a retail website, only to see an ad for that product on your Instagram feed? This is an example of how data mining helps marketers reach buyers.
Data mining plays a vital role in helping solve criminal investigations. Crime analysis deals with the discovery and detection of crimes and the relationship with the criminals who commit them. Since crime volume is increasing and crime datasets are becoming more complex, data mining helps identify connections between crimes and criminals.
Data mining helps make marketing efforts more effective. This process is crucial in understanding where the customers see ads, which demographics are best to target, where to place ads, what strategies work better with specific customers, etc.
Marketing campaigns, cross-sell offers, promotional offers, and programs work much better with data mining.
Health care and insurance companies implementing data mining techniques have a higher chance of success than competitors who neglect data mining. The insurance industry can only grow if it can convert data into knowledge or intelligence about its customers and competitors. That’s exactly what data mining helps them achieve.
Insurance companies benefit from data mining because it helps them decide on policy application approvals, manage prospective customers, and improve their risk modeling.
In healthcare, doctors can use data mining to diagnose certain medical conditions, analyze X-rays, treat patients, and examine numerous other medical imaging results. Modern-day medical research is also tightly connected to data mining and machine learning.
Data mining also finds its use in the manufacturing industry. Data mining applications boost operational efficiency and uptime in the plants, ensure product safety, and increase supply chain performance.
Data mining helps optimize customer service work efficiency. For example, suppose customer satisfaction drops significantly due to long ship times, improper communication, shipping quality, etc. In that case, data mining can gather essential information about customer interactions and determines the company’s weak points.
Data mining can be used in human resources in a range of areas, including promotions, retention, salary ranges, utilization of company benefits, satisfaction surveys, and more. HR experts can correlate the data using data mining to understand better why employees leave the company and what makes them want to join.
Data Mining Limitations
Data mining is a highly complex field, and its complexity is also its drawback. Some data analytics require specific software tools or technical skillsets to perform the work, which can be a barrier for small companies.
Also, data mining doesn’t always bring results. Faulty findings, changes in the market, model errors, and other factors can compromise the results. That’s why sometimes data mining can only guide decisions but not ensure their outcomes.
Finally, data mining can also be costly. This can be a huge issue for companies on a smaller budget. Some tools only work with subscriptions, and other data types may be super expensive to obtain. Additionally, storing the data requires a secure infrastructure and strong computational power to manage and analyze.
Data Mining Related Concepts
The term data mining is often used interchangeably with many related terms. Some of the most commonly related concepts include:
Knowledge discovery in databases (KDD) was a term often discussed by academics in the 1980s and 1990s. By definition, KDD included selection, pre-processing, transformation, data mining, and evaluation of data.
According to this framework, data mining is a subcomponent of KDD and is equivalent to data analysis. However, people often use the two terms (KDD, data mining) interchangeably. Today, data mining has become the preferred term to describe both processes.
Machine learning belongs to the deep learning and artificial intelligence branch. Its main goal is to allow computers to learn without being programmed. Some data mining techniques like classification, clustering, and regression can also be used in machine learning. This is why some people may think of machine learning as a data mining subset.
Still, there are many differences between the two. While data mining looks for data patterns, machine learning uses the data mining results to acquire new knowledge about the data.
Big Data Analysis
Data mining, data analytics, and big data analytics are three terms often used to refer to the same thing. Some say that data mining can be performed on both small and big data. Also, some say data analytics incorporates techniques different from data mining, making the latter a subset of data analytics.
There’s not much difference between the terms in practice. Data mining was a more popular term in the early 2000s, while analytics is a more widely used term today.
Privacy Issues of Data Mining
Customers worldwide are becoming more uncomfortable with how companies use their data. In the U.S., the Federal Trade Commission and Congress often have to deal with data privacy hearings, but there haven’t been any effective legislative changes on the subject.
In Europe, on the other hand, there are already regulations that went into effect that concern every organization in the EU and the way they treat data. This law says that organizations need to obtain consent on behalf of the customer to process their data. They also need specific measures to protect data and notify customers if their data was inside a data breach.
Organizations that don’t comply with these regulations can pay up to 4% of their global revenue.
Understanding Data Mining
Data mining has quickly evolved into a crucial process for business and organizational operations. Data analysts and other experts apply different data mining techniques to gain insights into a range of issues. Without data mining, business owners and stakeholders would be much more limited in making the right business decisions.
Frequently Asked Questions
What is data mining explain?
Data mining is the process of discovering patterns in large data sets, extracting them, and using the findings to help stakeholders make more informed business decisions.
Why is data mining important?
Data mining is important for businesses and organizations because it allows them to better understand their clients, oversee their business operations, boost customer acquisition, and get new business opportunities.
What is an example of data mining?
Examples of data mining can be found in all industries. Marketing experts can use it to understand customer behavior patterns better, while bankers can benefit from it for fraud detection or risk management.
Author: Tibor Moes
Founder & Chief Editor at SoftwareLab
Tibor is a Dutch engineer and entrepreneur. He has tested security software since 2014.
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