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Author: Admin | 2025-04-28
How processes unfold. Process mining applies data science to discover bottlenecks, and then validate and improve workflows.BPM generally collects data more informally through workshops and interviews and then uses software to document that workflow as a process map. Since the data that informs these process maps is often qualitative, process mining brings a more quantitative approach to a process problem, detailing the actual process through event data.Information systems, such as enterprise resource planning (ERP) or customer relationship management (CRM) tools, provide an audit trail of processes from log data. Process mining uses this data from IT systems to assemble a process model or process graph. From there, organizations can examine the end-to-end process with the details and any variations outlined. How data mining works The data mining process involves several steps from data collection to visualization to extract valuable information from large data sets. Data mining techniques can be used to generate descriptions and predictions about a target data set.Data scientists or business intelligence (BI) specialists describe data through their observations of patterns, associations and correlations. They also classify and cluster data through classification and regression methods, and identify outliers for use cases, such as spam detection.Data mining usually includes five main steps: setting objectives, data selection, data preparation, data model building, and pattern mining and evaluating results.1. Set the business objectives: This can be the hardest part of the data mining process, and many organizations spend too little time on this important step. Even before the data is identified,
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