Data mining is different from traditional business reporting in several ways. First, traditional business reporting is focused on collecting facts about past circumstances while data mining uses predictive analytics to predict future events based on current data sets. This allows businesses to optimize operations by predicting customer behaviors, market movements, resource demands, etc., thus allowing them to better allocate resources and plan for the future with greater accuracy than ever before.
Another difference between these two approaches is that traditional business reporting relies heavily on manual processes while data mining automates everything from feature selection to model building. Automating these processes eliminates human errors associated with manually programming models as well as reduces overall costs since there isn’t a need for personnel dedicated solely to creating models or running simulations manually.
Compare “Data Mining” to “Traditional Business Reporting”.
Overall, both methods are valuable tools for understanding complex environments but each serves a different purpose; where traditional business reporting focuses more on static analysis of historical facts related directly or indirectly with businesses’ goals/operations (such as product sales across months/years), modern data-mining techniques leverage automated algorithms/simulations combined with sophisticated visuals which can greatly increase our ability understand what’s going inside any given environment – giving us much more insight into how customers behave or how markets move than would have been possible without its help.