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What Is Data Mining?
Data mining is the application of machine learning (ML) and statistical analysis to large data sets aiming to identify patterns and derive insights. It has grown rapidly over the last twenty years thanks to advances in data warehousing and big data analytics.
What does data mining help discover? It can be used to summarize and describe the contents of a data set or it can predict outcomes by modeling different scenarios. Advances in generative AI (genAI) are enabling data mining processes to become more automated, accelerating analysis.
Key Takeaways
- Data mining is issued to identify trends and patterns in large data sets.
- Often combined with data analytics in one solution, data mining can identify business risks or bring hidden opportunities to the surface.
- Use cases are usually industry-driven, with financial services and healthcare at the forefront.
- Some of the most widely used data mining tools are made by Oracle, SAS, and IBM.
- While data mining can raise concerns about privacy, it can also be used to identify fraud, stop cyberattacks, and help prevent identity theft.
History of Data Mining
The term data mining was first coined in the 1990s. However, the practices that underpin it have an extensive history in mathematics. Early forms of data pattern recognition include Bayes theorem, developed in the 1700s, and the evolution of linear regression, which was invented and further refined in the 1800s.
With the advent of personal computing and the growth of data science, the ability to collect, store, manage, and manipulate large data sets has created opportunities to apply these techniques at scale. In the 1990s, companies realized that analyzing large volumes of customer and transactional information could uncover patterns that would give them a business edge.
How Data Mining Works
Data mining involves four essential phases: objective setting, selecting data, preparing data, and pattern mining:
Objective setting
In this phase, data scientists work with business stakeholders to define a use case, which helps determine the questions and parameters for a data mining exercise.Selecting data
Once objectives for a data mining project have been agreed upon, data scientists can identify which data sets are most suitable for analysis and determine the best system for storing and securing the data.Preparing data
The selected data sets then need to be collected and quality-checked to remove duplicates and ensure completeness. Data scientists may also opt to reduce the number of dimensions in the data set, as too many could slow down the mining process.Pattern mining
Data mining techniques can now be used to identify trends or bring interesting data relationships to the surface. These can include sequential patterns, correlations, and associations. Data mining will usually focus on finding high-frequency patterns, however, identifying low-frequency deviations in data can be used to identify anomalies and security concerns.
Data mining examples can include market basket analysis, where companies attempt to pinpoint products that are frequently purchased together, or predictive analytics that seek to forecast future events and outcomes.
Data Mining Tools
Popular data mining tools include solutions like SAS Enterprise Miner, RapidMiner, IBM SPSS Modeler, and Oracle Data Miner.
Types of Data Mining Techniques
These are some of the most frequently used data mining techniques:
Data Mining vs. Data Analytics vs. Data Warehousing & Crypto Mining
Data mining is easy to confuse with similar and related terms like data analytics, data warehousing, or crypto mining, but there are notable differences:
- Data mining is the process of extracting important information from a large dataset.
- Data analytics involves interpreting data to find trends and patterns and is sometimes offered as part of a comprehensive data mining solution.
- Data warehousing refers to the centralized storage, management, and retrieval of data from various sources.
- Crypto mining involves analyzing and verifying transactions so they can be added to a blockchain network like Bitcoin or Ethereum.
Data Mining Use Cases
While the potential list of data mining use cases is unlimited, the most common are industry-specific.
These include:
Data Mining Pros & Cons
Pros
- Leads to cost savings from finding new efficiencies or identifying new sales and revenue opportunities
- Helps businesses identify fraud and identity theft or detect behaviors that could indicate a cybersecurity attack is underway
- Clarifies key factors for addressing challenges and making difficult management decisions
Cons
- Requires collecting and analyzing large amounts of data, often including personal information
- Data mining tools can be difficult to use, requiring a high degree of technical knowledge
- It uses significant computing and storage resources
The Bottom Line
Data mining can be a powerful tool that helps organizations derive valuable insights, better understand their operations, or give definition to murky and shifting market environments.
By extracting key elements from large sets of data, businesses can apply analytics and discover patterns, trends, associations, and relationships that can help them become more efficient, drive sales, or improve customer service.
