Data mining is the exploration and analysis of large data to discover meaningful patterns and rules.
It differs from predictive analytics because it describes historical data, while data mining aims to predict future outcomes. Additionally, data mining techniques are used to build machine learning (ML) models that power modern artificial intelligence (AI) applications such as search engine algorithms and recommendation systems.
Applications of data mining include database marketing, credit risk management, training and support, fraud detection, healthcare bioinformatics, sentiment analysis, qualitative data mining, and spam filtering.
Benefits of Data Mining
- Automated Decision-Making: Data Mining allows organizations to continually analyze data and automate both routine and critical decisions without the delay of human judgment. Deployed within a firm’s operational algorithms, these models can collect, analyze, and act on data independently to streamline decision making and enhance the daily processes of an organization.
- Accurate Prediction and Forecasting: Planning is a critical process within every organization. Data mining facilitates planning and provides managers with reliable forecasts based on past trends and current conditions.
- Cost Reduction: Data mining allows for more efficient use and allocation of resources. Organizations can plan and make automated decisions with accurate forecasts that will result in maximum cost reduction.
- Customer Insights: Firms deploy data mining models from customer data to uncover key characteristics and differences among their customers. Data mining can be used to create personas and personalize each touchpoint to improve overall customer experience.
Learn more about data mining here: https://www.microstrategy.com/us/resources/introductory-guides/data-mining-explained
