What is the difference between data mining and predictive analyses?
What will be an ideal response?
Data mining refers to efforts to identify patterns that exist within data and that may identify unrecognized causal mechanisms that can be used to enhance decision making. To identify these causal mechanisms, data mining uses correlation and multiple regression methods to identify patterns of relationships in extremely large datasets. An example would be the identification of a correlation between employee job satisfaction and employee turnover.
Predictive analysis involves attempts to develop models of organizational systems than can be used to predict future outcomes and understand the consequences of hypothetical changes in organizations—for example, a change in existing organizational systems or processes. To use the example above, if the organization discovered a correlation between employee job satisfaction and turnover, HR could use these data to suggest modifications to the employees’ work situation or their benefits with the goal of increasing employee satisfaction.
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