Complete SAS Programming Guide - Learn SAS and Become a Data Ninja - Analytics Challenges

The video discusses the challenges and value of predictive modeling in business analytics. It highlights the complexity involved in predictive modeling due to the extensive steps required, such as data preparation, algorithm selection, and model deployment. The need for experts like data scientists and programmers is emphasized, given the complexity and cost associated with these tasks. Despite these challenges, predictive modeling is valuable as it allows businesses to predict future trends based on historical data, surpassing traditional business intelligence methods.

Why is predictive modeling considered to be on the higher end of the complexity spectrum?

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What are some of the key steps that a data team must go through when implementing predictive modeling?

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What types of experts are needed to successfully implement predictive modeling in a business?

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How does predictive modeling add value to an organization beyond traditional management information systems?

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In what ways can predictive modeling help businesses anticipate future events?

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