What are the best practices for sensitivity analysis on large data sets with multiple variables?

1 Choose the right method

When performing sensitivity analysis, the best method to use depends on the type and size of data, the structure and purpose of the model, and the desired level of detail and accuracy. For example, one-way sensitivity analysis changes one variable at a time and is simple to implement, but it does not capture interactions and correlations between variables. Multi-way sensitivity analysis changes two or more variables simultaneously, but increases computational complexity. Monte Carlo simulation generates random values for input variables based on probability distributions and runs the model multiple times for a range of outputs; however, it requires advanced tools and techniques and may be difficult to interpret the results. Ultimately, you should consider factors such as data availability and quality, model complexity and flexibility, input variable uncertainty and variability, as well as time and resource constraints when choosing a method.

2 Select the relevant variables

When conducting sensitivity analysis, it's essential to select the input variables that are most relevant, uncertain, and influential for your model. To do this, you can seek expert judgment from stakeholders who have knowledge and experience in your field or domain. Additionally, you can analyze historical data to determine which variables have had the biggest impact on the output in the past. Furthermore, you can calculate sensitivity coefficients - numerical measures of how much the output changes in response to a unit change in an input variable - with formulas, graphs, or software. By choosing the right variables, you can reduce the dimensionality and complexity of your sensitivity analysis and hone in on the key factors that affect your model.

3 Define the ranges and scenarios

After selecting the input variables, you need to define the ranges and scenarios that you want to test in your sensitivity analysis. These ranges and scenarios are the values or combinations of values that you assign to the input variables to see how they affect the output. When defining these ranges and scenarios, it's important to consider realistic and plausible assumptions, as well as different levels of uncertainty and risk. You can use benchmarking to compare your input variables with those of other similar or comparable models, projects, or organizations. Additionally, statistical analysis can be used to calculate the mean, standard deviation, confidence intervals, or percentiles of your input variables. Scenario planning is also a useful tool for creating stories or narratives that describe possible future situations or outcomes. Techniques such as SWOT analysis, PESTEL analysis, or scenario matrix can be used to generate and evaluate different scenarios. By defining the ranges and scenarios, you can explore the sensitivity of your model to various changes and uncertainties, and assess the potential implications and consequences.

4 Perform the analysis and visualize the results

The final step in sensitivity analysis is to run your model with different values or combinations of values and record the corresponding output values. Additionally, you need to present and communicate the results effectively, using tables, charts, dashboards, reports, software, and tools. For instance, data tables and pivot tables can display the output values for different input values or scenarios, while dashboards and reports can summarize and organize the results of your sensitivity analysis. You can also use software and tools such as Excel, Power BI, Tableau, R, Python, or SAS to automate and simplify the process of performing and visualizing your sensitivity analysis. By doing so, you can understand and communicate the impact of your input variables on your output, as well as identify opportunities and risks for your model.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?