Data Mining in Analytic Systems
As we learned earlier, the primary goal of data mining is to extract valuable knowledge from raw data, uncovering hidden patterns and relationships that can inform decision-making and drive business strategies. Data mining plays a crucial role in transforming raw data into actionable insights in analytic systems. It enables organizations to sift through vast amounts of data to identify meaningful patterns, anomalies, and correlations that might not be apparent through traditional analysis methods. By leveraging data mining techniques such as clustering, classification, regression, association rule mining, and anomaly detection, analytic systems can uncover valuable insights, improve decision-making processes, optimize operations, and gain a competitive edge in the market. Data mining also facilitates predictive modeling, allowing organizations to forecast future trends and outcomes based on historical data, ultimately enhancing strategic planning and resource allocation. Overall, data mining is a powerful tool within analytic systems, enabling organizations to unlock the full potential of their data for informed decision-making and actionable insights. Watch the video and note how data mining was used to support life sciences and drug development analytic models. How could you extend these ideas to a problem that you are familiar with? What kinds of analytic models do you use? How could data mining help you?
Source: Doug Davis and Theodore Alexandrov, https://www.youtube.com/watch?v=GVaqPr707hY
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