Data Mining Explained: How Data Mining Works for Businesses

Data mining, also known as knowledge discovery in data (KDD), is the process of sorting through large data sets and converting raw data into useful information. Using a variety of data mining techniques, businesses are able to optimize management processes, detect fraud, and make more effective advertisements. This form of data science is a subset of business intelligence (BI). Data scientists often use artificial intelligence (AI) tools like machine learning and neural networks to perform data analysis quickly and efficiently. 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. Businesses use data mining in a few key ways to improve efficiency and performance. 1. __Fraud detection__: By recognizing patterns and outliers within data, businesses can detect fraudulent activities. 2. __Marketing campaigns__: Some businesses use data mining software to create targeted marketing campaigns for specific demographics. 3. __Optimization__: Managers use data mining tools to optimize product pricing, customer service, and decision-making processes. For a better understanding of how data mining works in the real world, consider these examples. 1. __Streaming services__: Audio and video streaming platforms suggest content to individual consumers based on data mining techniques. 2. __Weather forecasting__: Meteorologists use data mining to predict weather conditions and patterns. 3. __Retail__: Retail companies implement data mining strategies to understand consumer buying behaviors, predict inventory needs, and focus marketing efforts. 4. __Banking__: Banks use data mining methods to assess credit risks and identify fraudulent spending patterns within bank accounts. Get the [MasterClass Annual Membership](https://www.masterclass.com/) for exclusive access to video lessons taught by business luminaries, including Sara Blakely, Chris Voss, Robin Roberts, Bob Iger, Howard Schultz, Anna Wintour, and more. Data mining, also known as knowledge discovery in data (KDD), is the process of sorting through big data sets and converting raw data into useful information.