Key takeaways
Data mining involves using computers, automation technology, and intelligent automation, such as robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML) to extract useful information from large, raw datasets. The extracted information is cataloged, organized, and presented in a data analysis process businesses use to make informed, data-driven decisions.
The internet, personal computers, and mobile devices helped accelerate the digital age that required further advancements in technologies like data mining. Using automated data mining tools became necessary because the large quantities of raw data made it unrealistic for humans to process raw data in a reasonable time. Additionally, using AI tools combined with RPA processes on raw structured, unstructured, and semi-structured data allowed the processing of these data types for 24 hours a day with minimal errors and no breaks.
The value of data mining and the automated technology used drastically reduces the time-consuming effort while minimizing human errors when processing large sets of raw data. Automated data mining allows businesses to make faster and more accurate decisions using relevant data after data analysis and interpretation. This article focuses on the data mining techniques, applications, and challenges of data mining.
Read more: Business Intelligence vs. Data Analytics: Know the Difference
Understanding data mining
Data mining searches and analyzes large data sets to find patterns, trends, anomalies, and correlations that can help businesses make better decisions, cut costs, increase revenues, reduce risks, or improve customer relationships. It aims to improve various aspects of a business’s operations continuously.
Data mining is a critical component of the data analysis process. It uses advanced analytical methods like artificial intelligence, machine learning, and neural networks combined with statistical methods and association rules (if-then statements) to extract relevant information. Some advanced methods require an algorithm to distinguish different data points and categorize them correctly before any data is analyzed.
The Data Mining Process
The first step in the data mining process is to define business goals and objectives. Once the business goals and objectives are defined, a business needs to select the appropriate data sources that will address the business goals and objectives. After the data sources are selected, the following steps occur:
- Data transformation: Converts raw data into a usable format for analysis and modeling
- Data cleaning: Prepares the data for data mining
- Model creation: Testing the model against a known hypothesis
- Publish the model: For use in a data analysis process
Mining data can also be used in business intelligence and data analysis processes or projects to help businesses improve upon one or more business operations. Mining data is one of the essential phases of the data analysis process.
Techniques in Data Mining
Advanced analytical techniques are critical in extracting relevant information from data analysis methods and techniques. The typical advanced techniques used are the following:
Clustering
Clustering is a statistical method used to group items that are closely related. Clustering aims to group similar data points into the same cluster.
Businesses use clustering in different ways. Companies can use cluster analysis to identify their most valuable customers and forward personalized offers or rewards in advertisements. Clustering is used for fraud detection by identifying fraudulent activity patterns or predicting sales using cluster data to determine which products sell the best in different locations.
Association rule analysis
Association rules find relationships between two data points in large data sets. Association rules use if-then statements to show how different data points correlate when one data point influences some action on another data point routinely.
For example, a grocery store may place peanut butter and jelly in the same shopping aisle due to the association rule showing a high percentage of those two products being purchased together. Association rules show how two data points are connected in a large data set.
Classification
Classification uses item attributes or features to put items in predefined groups or categories. Multiple methods are used to classify data points, and two examples are a support vector machine (SVM) and a random forest. Random forest uses multiple decision trees, but it and SVM both train on ML using a supervised learning model. Businesses can use the classification technique for spam detection or help marketers better understand customer behavior.
Regression
Regression is a statistical method associating a dependent variable with one or more independent variables. The independent variable can explain or predict the numeric value of the dependent variable. Regression analysis is a popular tool used in the financial industry to determine the value of a dependent product based on independent variables like interest rates and taxable income considerations.
Decision Trees
Decision trees are flowchart-type diagrams trained and tested using an ML algorithm to separate complex data into manageable parts. Decision trees are used by businesses to analyze customer data and make decisions.
Machine learning and neural networks are AI techniques like descriptive, diagnostic, predictive, and prescriptive analysis used in data mining. Other techniques are anomaly detection, network analysis, and outlier detection.
Data Mining Software Recommendations
Data mining solutions exist for different levels of user experience and different types of business industries. Listed are some recommendations for the different levels of user knowledge and business types:
Data Mining software for beginners
RapidMinder is an ideal data science platform for businesses with employees with different knowledge and skill sets. RapidMinder can perform all the expected actions of a data science platform, such as data preparation, ML, and predictive modeling.
Data Mining software for advanced data mining needs
GoodData provides advanced features like microservice architecture and React, Python, and JavaScript Software Development Kits (SDKs) while still allowing engineers to use their coding skills, data analysts to use their limited coding knowledge, and consumers to use AI-supported tools that require no coding skills.
Oracle Healthcare is a platform that lets healthcare providers seamlessly exchange healthcare records with authorized medical professionals using an Electronic Health Records (EHR) system, making comprehensive medical information available in real-time.
Applications of Data Mining
Data mining can benefit any industry by exploring data sets and extracting meaningful data. It can help businesses improve operations or make better decisions based on analyzed data. Different industries use data mining to meet or exceed specific business goals or objectives.
Healthcare
Healthcare industries use data mining to help medical staff make better decisions. They mine large quantities of patient data to identify trends that can be analyzed and help healthcare providers make better decisions about care and treatment. Data mining can help improve diagnoses and provide personalized medical treatment to specific patients.
Financial and banking industries
Financial businesses use data mining to help forecast the stock market, the currency exchange rate, and better understand financial risks, including detecting money laundering schemes.
The banking industry also uses data mining to prevent money laundering, detect fraud, and make better loan decisions. Banks use predictive data mining to assess a customer’s creditworthiness and identify potential customers with good credit ratings.
Manufacturing
Manufacturing industries use data mining to optimize production processes, forecast the demand for a product or service, identify inefficiencies in supply chain operations, streamline warehouse operations, and perform predictive maintenance.
Retail
Retailers use data mining to learn purchasing habits, study customer preferences, and customers’ shopping patterns. Using analyzed data, retailers can improve pricing, gain new customers, and increase customer loyalty. Customer segmentation allows retailers to categorize customers based on shared characteristics using analyzed data.
Insurance
The insurance industry uses data mining for risk management, fraud detection, and improved decision-making. Data mining also helps insurance companies understand customer buying patterns and behavior to minimize fraud and set insurance rates or price optimization, including customer segmentation.
Telecom and utility companies
Telecom and utility industries use data mining to predict when customers will likely terminate their services. These utility companies also use this information to improve marketing campaigns, identify fraud, and manage networks.
Challenges and ethical considerations
Despite the benefits and pros of data mining, businesses need to be aware of the cons. Businesses data mining large quantities of raw data must be mindful of the challenges and ethical concerns when processing data to avoid any security, legality, or compliance violations.
Legal issues can arise if personally identifiable information (PII) is compromised, including the time-consuming effort of notifying customers while resolving the breach, which costs several thousand, if not millions, dollars. Regulatory compliance protections for intellectual property rights, privacy, security, Payment Card Industry, Data Security Standard (PCI DSS), Health Insurance Portability Accountability (HIPAA), and General Data Protection Regulation (GDPR) are all compliance regulations that must not be violated.
Extracting complex data from multiple sources using automated technology combined with AI and ML algorithms is challenging for businesses because complex data is stored in various formats that must be transformed into one format within the target data set. Complex data is unstructured data that can be text, images, audio, and video. Data mining tools tcpdump and Snort can process complex data, and preprocessing tools like Dask are available.
Ethical concerns can be a slippery slope if consent, ownership, and maintaining customer information privacy are violated. Businesses using data mining tools to access user information must inform the customer of the reason for accessing a customer’s personal information. Transparency and the protection of customer’s data are crucial. Other ethical concerns are third-party risks and the convenience versus privacy of customers’ data. Protecting customers’ data and getting consent to collect customer information helps address moral concerns.
Another con associated with data mining is there is no guarantee that whatever business goal you are trying to accomplish may not be successful for many reasons. Failures can be caused by a lack of training or knowledge, inaccurate or inadequate data analysis, and the inability to correctly interpret the processed data, leading to a wrong decision. Data mining can be costly if it doesn’t produce the desired results.
The pros of data mining are beneficial to any business that understands the criticality of data mining, selecting the appropriate technique, and correctly interpreting the analyzed data can reap several benefits from data mining and data analysis.
Good data mining leads to the following benefits:
- Businesses can make accurate predictions
- Build better risk models
- Detect fraud and credit risks
- Improved customer service
- Increased production uptime
- Lower costs
- Increase revenues
- Efficient and effective sales and marketing
Overall, data mining helps businesses become more profitable and operationally efficient.
Data mining and your business
Regardless of the business industry, data mining and data analysis can improve overall business operations when used correctly. The transformative potential of data mining begins with extracting meaningful and valuable information from large data sets to find patterns and insights leading to better data-driven decisions. Processing accurate and relevant data in the analysis process can lead to increased revenues and optimized business operations when the analyzed data is interpreted correctly.
Clean, analyzed data leads to good decision-making. However, businesses must always be aware of the ethical concerns that can arise and create significant issues. A comprehensive data governance program can highlight any analyzed data that can cause ethical problems before use.
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