Explain the difference between descriptive, diagnostic, and predictive analyses in a consulting project.

Explain the difference between descriptive, diagnostic, and predictive analyses in a consulting project.

• A. Descriptive summarizes what happened, diagnostic explains why, and predictive estimates what will happen under scenarios.
• B. Descriptive explains why, diagnostic summarizes what happened, predictive describes data quality.
• C. Descriptive predicts future, diagnostic describes past, predictive explains causes.
• D. All three are identical.

Answer: (A)

In a consulting project, you use three analytic approaches to answer different questions about data. Descriptive analytics focuses on summarizing what happened—pulling together the data to describe past performance with concrete figures, trends, and visuals. Diagnostic analytics goes a step further to explain why things happened by uncovering causes and drivers, testing hypotheses, and identifying root factors behind observed results. Predictive analytics looks forward, using historical patterns to estimate future outcomes and to explore how different scenarios might play out. For example, you might start with descriptive analysis to report last quarter’s sales totals, average order value, and month-to-month trends. Then you’d use diagnostic analysis to explore why there was a spike in sales—perhaps a new promotion, seasonal effect, or competitive action. Finally, predictive analysis would forecast next quarter’s sales under varying scenarios, like changing marketing spend or pricing, so leadership can plan accordingly. The other options mix up these roles: describing data quality is a separate concern, not the core task of descriptive analytics; saying predictive describes data quality or that descriptive explains why misplaces the purpose of each approach; and claiming all three are identical ignores the distinct questions and methods each one uses.

In a consulting project, you use three analytic approaches to answer different questions about data. Descriptive analytics focuses on summarizing what happened—pulling together the data to describe past performance with concrete figures, trends, and visuals. Diagnostic analytics goes a step further to explain why things happened by uncovering causes and drivers, testing hypotheses, and identifying root factors behind observed results. Predictive analytics looks forward, using historical patterns to estimate future outcomes and to explore how different scenarios might play out.

For example, you might start with descriptive analysis to report last quarter’s sales totals, average order value, and month-to-month trends. Then you’d use diagnostic analysis to explore why there was a spike in sales—perhaps a new promotion, seasonal effect, or competitive action. Finally, predictive analysis would forecast next quarter’s sales under varying scenarios, like changing marketing spend or pricing, so leadership can plan accordingly.

The other options mix up these roles: describing data quality is a separate concern, not the core task of descriptive analytics; saying predictive describes data quality or that descriptive explains why misplaces the purpose of each approach; and claiming all three are identical ignores the distinct questions and methods each one uses.