Framework for problem-solving that involves five steps: defining the problem, collecting data, building the model, evaluating and critiquing the model, and presenting results and benefits.
- Define the problem: The first step is to clearly define the problem you want to solve. This involves identifying the key issues and goals, determining the scope and boundaries of the problem, and defining the key metrics and outcomes you want to achieve.
- Collect data: The second step is to collect relevant data to inform your analysis. This may involve gathering data from internal and external sources, such as surveys, interviews, financial statements, and market research.
- Build the model: The third step is to build a model that captures the key drivers of the problem and provides a framework for analysis. This may involve using statistical techniques such as regression analysis, machine learning, or simulation modeling to develop a predictive model.
- Evaluate and critique the model: The fourth step is to evaluate and critique the model to ensure its accuracy and validity. This may involve testing the model against historical data or conducting sensitivity analysis to test its robustness.
- Present results and benefits: The final step is to present the results and benefits of your analysis. This may involve communicating your findings to stakeholders and decision-makers, and highlighting the key insights, recommendations, and potential benefits of your solution.