So you are a brand-new manager or executive standing at the crossroads of your first major project decision. You’ve got a Decision Analysis (DA) support team backing you, but you’re responsible for the final call. To guide that conversation and ensure you’re making a high-quality decision, you need a structured approach.
Here are five sets of questions a new manager should ask their DA team followed by a Decision Analysis Summary Template tailored to help new managers ask the right questions, surface key risks and uncertainties, and make confident, data-informed choices.
1. What alternatives have we considered? Did the team cover all options that are available to us in these alternatives? Why is the recommended one the best?
Why it’s important: Decision quality is tied to the quality and quantity of alternatives considered. This question tests whether the team is choosing from a robust set of options or settling prematurely.
Follow-up probes:
- Were creative or hybrid alternatives explored?
- Was a phased approach considered?
Be Aware:
- Developing and reviewing a full set of options that covers the available space is critical to creating value.
- Teams have a tendency to “settle” for the first option that seems reasonable without further exploring other possibilities.
2. In reviewing the Tornado Diagrams, what are the key uncertainties, who were assessed as subject matter experts, and does the team feel the assessments were done well?
Why it’s important: This helps identify what could most impact the decision — either positively or negatively.
Follow-up probes:
- Have we modeled these uncertainties with ranges or probabilities?
- How sensitive is the decision to each of them?
Be Aware:
- The Tornado Diagrams should be the primary tool to use for determining which uncertainties impact the decision.
- Review the “pros and cons” of Tornado Diagrams (see previous article).
- A “plus or minus” assessment on an uncertainty will not provide insight into the actual impact on the decision. Ensure that the uncertainties/risks have been properly assessed.
3. Has the team considered the value of additional information or the value of control? Is it possible to obtain further information before making the decision?
Why it’s important: Applying Value of Information and Value of Control tools can add significant value to the decision. Before approving, it’s crucial to know if further analysis, data, or testing could change the decision and thereby increase its expected value.
Follow-up probes:
- Would new data change our recommendation?
- Is the cost of waiting or learning justified?
Be Aware:
- Further information or analysis may not add value, especially if obtaining the information causes a delay or significantly increases the cost of the decision. This is not intuitive as all teams desire to reduce uncertainty by gaining more data, but if the decision choice will not change after gaining the new information, the information adds no value to the decision.
- This is a second level decision analysis skill, and your team might need to consult with experts or use specific software to make these calculations properly.
4. What are the assumptions behind our model, and how sensitive are we to them?
Why it’s important: Models are only as good as their assumptions. This question encourages transparency and reveals where overconfidence or bias might skew results.
Follow-up probes:
- What are the most critical assumptions?
Be Aware:
- It is critical to review the assumptions “baked into” your model. These are not assumptions related to the uncertainties that are handled in question set 2 above, but they related to base line assumptions that might not have been modeled, e.g., pricing will not experience a price war, government subsidies will continue as before, taxes will not change, our partners will perform as promised, etc.
5. What does the expected value and risk profile look like for all the alternatives?
Why it’s important: This question brings together the probabilistic assessment of outcomes (not just best-case or average-case) and helps frame the project in terms of value vs. risk.
Follow-up probes:
- Does the recommended alternative meet our objectives and hurdles?
- Is there an unacceptable risk hidden within the numbers?
- How can we mitigate the downside risk?
Be Aware:
- In a probabilistic analysis, answers to the questions above should be framed as probabilities, e.g., we meet or exceed our IRR hurdle rate in 80% of the cases, the risk of a negative NPV is X%, etc.
- An unacceptable risk might “lurk” in a Black Swan type event. A full discussion of the downside risk with mitigation and signposting should occur once the decision choice has been made.
These questions are not just about validating the project — they’re about fostering a culture of rigorous, transparent, and collaborative decision-making.
Here’s a Decision Analysis Review Template that a new manager can use with their Decision Analysis (DA) support team before approving a project. It’s designed to guide a structured discussion, ensure decision quality, and expose risks, assumptions, and value drivers. It also will serve to document a summary of the decision for future reference and to enable look-back reviews.
1. Project Overview
- Project Name:
- Decision Owner:
- DA Team Lead:
- Date of Review:
- Decision to Be Made:
E.g., approve funding, proceed to next phase, select alternative, etc.
2. Alternatives Considered
Alternative | Description | Status (Retained/Discarded) | Reason for Inclusion/Exclusion |
A | |||
B | |||
C |
- Recommended Alternative:
- Why is this the best choice?
3. Key Uncertainties & Sensitivities
Uncertainty | Range/Distribution | ||
- Sensitivity analysis completed? □ Yes □ No
- Which uncertainties are decision-critical?
4. Value of Additional Information And Value of Control
- Would new data change the decision? □ Yes □ No
- What is the value of perfect or imperfect information?
- Is it possible to obtain further information and what would be the cost and delay in obtaining it?
- Is it possible to influence any of the critical uncertainties and what would be the value of control to the decision?
5. Assumptions & Model Validation
Assumption | Basis | Confidence Level | |
- What are the primary assumptions in our analysis?
- What happens if key assumptions are invalid?
- What is the mitigation plan in the event one of the key assumptions is wrong?
6. Expected Value & Risk Profile
- Base Case NPV (or equivalent metric):
- Range of Outcomes (pessimistic to optimistic):
- Probability-weighted Expected Value:
- Key risk factors:
- Risk-adjusted metrics (e.g., ENPV, downside risk):
- Visuals attached: □ Tornado Diagram □ Decision Tree □ Probability Distribution □ Risk Matrix
7. Recommendation & Readiness
- Is the team aligned with the recommendation? □ Yes □ No
- Open issues or unresolved disagreements:
8. Manager’s Decision
- □ Approved
- □ Deferred – needs more analysis
- □ Rejected
- Comments or Conditions: