Data mining is the process of sorting through large sets of data to find relevant information that can be used for a specific purpose
What is data mining?
Data mining is the process of sorting through large sets of data to find relevant information that can be used for a specific purpose. Data mining is essential for both data science and business intelligence, and is fundamentally about patterns.
Once data is collected and stored, the next step is to understand it – otherwise it is meaningless. Data analysis is performed in several ways, including using concepts such as machine learning, where complex, adaptable algorithms are used to analyze data artificially.
Traditional methods of data mining involve data scientists — experts specially trained to understand complex information — writing reports for management to act on.
How does data mining work?
Data mining involves examining and analyzing large amounts of information to find meaningful patterns and trends. The process works by collecting data, setting a goal, and applying data mining techniques. The specific tactics may vary depending on the goal, but the empirical process of data mining is the same. A typical data mining process might look like this:
- Define your goals: For example, do you want to learn more about customer behavior? Do you want to reduce costs or increase revenue? Do you want to know about fraud? It is important to set a clear goal at the beginning of the data mining process.
- Collect your data: The data you collect will depend on your goal. Organizations usually have data stored in multiple databases, for example, from information provided by customers during transactions, etc.
- Clean the data: Once you have identified the data, you will usually need to clean it, reformat it, and validate it.
- Investigating data: At this stage, analysts become familiar with the data by performing statistical analyzes and building visual graphs and charts. The goal is to identify important variables for the purpose of data mining, and to form preliminary hypotheses that lead to a model.
- Build a model: There are different data mining techniques – see below – and at this stage, the goal is to find a data mining approach that produces the most useful results. Analysts may choose to use one or more of the techniques summarized in the next section, depending on their goal. Building a model is an iterative process and may require reformatting the data, as some models require the data to be formatted in specific ways.
- Validate the results: In this stage, analysts will check the results to ensure the accuracy of the results. If not, it's a case of rebuilding the model and trying again.
- Execute the model: The insights uncovered can be used to achieve the goal set at the beginning of the process.
Types of data mining
There are a variety of data mining techniques and the technique you use will depend on your overall goal. There are different data models, and each of these models relies on different data mining techniques. The main data models are called descriptive, predictive, and prescriptive:
Descriptive modeling
This modeling reveals similarities or clusters within historical data to understand reasons for success or failure, such as classifying customers by product preferences or sentiment. Sample techniques include:
- Correlation rules: This is also known as market pattern analysis. This type of data mining searches for relationships between variables. For example, association rules might review a company's sales history to see which products are most often purchased together. The company can use this information for planning, promotion and forecasting.
- Clustering analysis: Clustering aims to identify similarities within a data set, separating data points that share common traits into subgroups. Aggregation is useful for identifying attributes within a data set, such as segmenting customers based on purchasing behavior, need state, life stage, or preferences in marketing communications.
- Anomaly factor analysis: This model is used to identify anomalies - that is, data that does not fit neatly into patterns. Anomaly analysis is particularly useful in fraud detection, network intrusion detection, and forensic investigations.
Predictive modeling
This modeling goes deeper to classify future events or estimate unknown outcomes – for example, using credit scores to determine an individual's likelihood of repaying a loan. Sample techniques include:
- Decision Trees: They are used to classify or predict an outcome according to a specific list of criteria. A decision tree is used to request input from a series of sequential questions that sort a data set according to specific responses. A decision tree is sometimes displayed in a tree-like visual form, and allows for specific guidance and user input when drilling down into the data.
- Neural networks: These networks process data through the use of nodes. These nodes consist of inputs, weights, and outputs. Similar to how the human brain is wired, the data is mapped through supervised learning. This model can be suitable for giving threshold values to determine the accuracy of a model.
- Regression Analysis: Regression analysis aims to understand the most important factors within a data set, factors that can be ignored, and how these factors interact.
- Categorization: involves assigning data points to groups or categories, based on a specific question or challenge to be addressed. For example, if a retailer wants to improve the discount strategy it uses for a particular product, it might look at sales data, inventory levels, coupon redemption rates, and consumer behavior data to guide its decisions.
Directive modeling
With the growth in unstructured data from the Internet, email, comment fields, books, PDF files, and other text sources, the adoption of text mining as a related discipline to data mining has also grown. Data analysts need the ability to analyze, filter, and transform unstructured data for inclusion in predictive models to improve forecast accuracy.
Data types in data mining
Types of data that can be mined include:
- Data stored in a database or data warehouse
- Transaction data – for example, flight bookings, website clicks, in-store purchases, etc.
- Engineering design data
- Sequence data
- Chart data
- Spatial data
- Multimedia data
Why is data mining important?
Most organizations are becoming more digital. As a result, many companies find that they possess vast amounts of data that, if properly analyzed, have the potential to be as valuable as their core products and services.
Data mining gives companies a competitive advantage by helping to find insights in data from digital transactions. By understanding customer behavior in greater depth, companies can create new products, services or marketing techniques. Here are some of the advantages that data mining can bring to a business:
Pricing optimization:
By using data mining to analyze various pricing variables, such as demand, elasticity, distribution and brand perception, companies can set prices at a level that maximizes profit.
Marketing improvement:
Data mining allows companies to segment their customers by behavior and need. This, in turn, allows them to deliver personalized ads that perform better and are more relevant to customers.
Greater productivity:
Analyzing employee behavior patterns can contribute to HR initiatives to improve employee engagement and productivity.
Greater efficiency:
From customer purchasing patterns to suppliers' pricing behavior, companies can use data mining and data analysis to improve efficiencies and reduce costs.
Increase customer retention:
Data mining can reveal insights that help you understand your customers more deeply. In turn, this can improve your interactions with customers, increasing customer retention.
Enhanced products and services:
Using data mining can identify and fix areas where quality is poor and result in reduced product yields.
Using data mining
Data mining is used for many purposes, depending on the organization and its needs. Here are some possible uses:
The sales:
Data mining can help increase sales. For example, think of a point of sale booklet in a high street store. For each sale, the retailer records the time of purchase, which products were sold together, and which products were the most popular. The retailer can use this information to improve its product line.
Marketing:
Companies can use data mining to improve their marketing activity. For example, insights from data mining can be used to understand where potential customers see ads, which demographics to target, where to place digital ads, and what marketing strategies work best with customers.
Manufacturing:
Companies that produce their own goods can use data mining to analyze the cost of raw materials, whether materials are being used more efficiently, how time is spent throughout the manufacturing process, and what obstacles affect the process. Data mining can be used to support just-in-time delivery by predicting when new supplies will be ordered or when equipment will need to be replaced.
Fraud detection:
The purpose of data mining is to find patterns, trends, and correlations that link data points together. An organization can use data mining to identify anomalies or correlations that should not exist. For example, a company may analyze its cash flows and find recurring payments to an unknown account. If this is unexpected, the company may wish to conduct an investigation to check for possible fraud.
HR:
HR departments often have a wealth of data available to process, including data on employee retention, promotions, salary ranges, company benefits, how those benefits are used, and employee satisfaction surveys. Data mining can correlate this data to gain a better understanding of why employees leave and what motivates new employees to join the company.
customers service:
Customer satisfaction is shaped by a variety of factors. Here, for example, is a retailer shipping goods. The customer may become dissatisfied with the delivery time, delivery quality, or communication about delivery expectations. This customer may become frustrated with slow email responses or long wait times on the phone. Data mining collects operational information about customer interactions and summarizes the results to identify weak points as well as areas where the company is performing well.
Maintaining customers:
Companies may use data mining to identify characteristics of customers who go to competitors, then offer special deals to retain other customers with those same characteristics.
Safety:
Intrusion detection techniques use data mining to identify anomalies that could be network intrusions.
Entertainment:
Streaming services use data mining to analyze what users are watching or listening to and to provide personalized recommendations based on their habits.
health care:
Data mining helps doctors diagnose medical conditions, treat patients, and analyze X-rays and other medical imaging results. Medical research also relies heavily on data mining, machine learning, and other forms of analytics.
The future of data mining
Cloud computing technologies have greatly influenced the growth of data mining. Despite cloud security issues and challenges, cloud technologies are suitable for the massive, high-speed amounts of semi-structured and unstructured data that many organizations now collect. The elastic resources of the cloud have the ability to meet these big data requirements. Thus, as the cloud holds more data in different formats, it requires more tools to mine the data to turn that data into insight. In addition, advanced forms of data mining such as artificial intelligence and machine learning are offered as services in the cloud.
Future developments in cloud computing will likely continue to fuel the need for more powerful data mining tools. Artificial intelligence and machine learning are growing, as is the amount of data. The cloud is increasingly used to store and process data for business value. It seems likely that data mining approaches will become increasingly cloud-based.
Data mining FAQs
Frequently asked questions about data mining, how data mining works, and the importance of data mining include:
Where is data mining used?
Data mining is used to explore large amounts of data to find patterns and insights that can be used for specific purposes. These purposes may include improving sales and marketing, improving manufacturing, detecting fraud, and enhancing security. Data mining is used across a wide range of industry sectors, such as banking, insurance, healthcare, retail, gaming, customer service, science, engineering, and many more.
How does data mining work?
Data analysts generally follow a certain flow of tasks throughout the data mining process. A typical data mining process may begin by defining the goal of data analysis, then working to understand where the data will be stored, how it will be collected, and what analysis is required. The next steps are to prepare the data for analysis, build the model, evaluate the model results, then implement the change and monitor the results.
Why is data mining used?
Data mining is used to identify organizational challenges and opportunities. It can be used to improve product pricing, improve productivity, increase efficiency, enhance customer service and retention, and assist in product development. Data mining gives companies a competitive advantage by helping to find insights in data from digital transactions.
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