Companies use data mining processes to turn a large amount of data into actionable information.

1. Goal identification: Prior to implementing new data mining tools, first examine your business’s problems and needs. Prioritize which business problem you’d like to solve first and what you hope to achieve by solving it.
2. Data collection: Once you understand the scope and goals of your data mining project, collect the necessary data—both structured and unstructured. Structured data is clearly organized, quantitative, and searchable (think sales numbers in a spreadsheet). Unstructured data comes from a variety of sources and tends to be more qualitative (video, audio, and social media, for example).
3. Data preparation: After finishing the data collection process, begin data preparation by consolidating your data into a large database or data warehouse.
4. Data categorization: Use open-source programming languages like Python and R software to create data mining algorithms. Some examples of data mining and modeling techniques include decision trees, regression models, and association rule learning.
5. Data evaluation: Create visualizations of your data, using graphics to organize and understand sequential patterns and outliers.
6. Data application: Implement new business strategies based on your set of data by using predictive models that anticipate future trends.

Data Mining Explained: How Data Mining Works for Businesses