Why Identify the Decision Before Modeling?
A request to “predict which cases are risky” does not yet explain who will use the result or what operational choice is at stake. A team may be able to build a model for that request, but without a decision context it cannot tell whether the work addresses a real business need. Naming the decision-maker and the choice gives the modeling task a practical setting.
The decision-maker is not necessarily the person who requests the analysis, owns the data, or writes the code. It is the person or role accountable for making the operational choice the analysis is intended to inform. In some organizations, that responsibility belongs to an individual; in others, a committee or team makes the choice under an established process.
The word “operational” matters: the decision affects how work, resources, or business processes are carried out. Examples include deciding whether to refer an application for additional review, how many claim handlers to schedule, or which maintenance requests receive a limited inspection slot. A broad objective such as “improve service” is not itself a decision because it does not state a choice.
Other people may have important roles. An executive may sponsor a project, a data steward may provide records, and an analyst may produce a score. Those contributions do not, by themselves, make any of them the decision-maker. The key test is whether the role has legitimate authority and accountability for the actual choice being described.
Describe the Decision Context Precisely
A useful initial description names the role, the choice, and the operating setting. It should be specific enough that a reader can tell who is responsible and what they are deciding, while avoiding details that belong to other parts of problem definition. For example, “the claims manager decides whether a disputed property claim is assigned to a specialist team” is more informative than “the insurer wants better claims predictions.”
Identify the person or role expected to make the choice, rather than the person who merely requested or built the analysis.
Describe what the role can decide, such as whether to approve, refer, schedule, or allocate. Avoid vague verbs such as “optimize” unless the choice is also specified.
Confirm that the role is permitted to make the choice and that the alternatives are available in the stated operating process.
Record the decision context without assuming that a particular score, target, or model is already justified.
This separation is useful because the same requested prediction can be associated with different choices in different settings. Conversely, several technical teams may contribute to one analysis while only one operational role is accountable for a particular choice. The decision context is therefore a practical anchor for evaluating whether a proposed modeling task is relevant.
Worked Examples
Worked Example: Finding the Accountable Role in Underwriting
A hypothetical insurer is considering a model related to commercial property applications. A product director requests the work, an analytics team will build the model, and branch underwriters currently decide whether an application is accepted within their delegated authority. Applications outside that authority are referred to a regional underwriting manager. Identify the decision-maker and decision for a task intended to support branch-level operations.
Step 1: Separate the project roles. The product director requested the analysis, so the director is a sponsor. The analytics team produces the analysis, so it is the technical user or developer. Neither role is stated to make the application-level underwriting choice.
Step 2: Identify the existing choice. At branch level, an underwriter decides whether to accept an application within delegated authority or refer it to the regional manager. That is a concrete operational choice.
Step 3: Check authority. The branch underwriter has authority over the within-limit applications described. The regional manager, rather than the branch underwriter, is accountable for applications outside that authority.
Conclusion. For a task supporting branch-level operations, the decision-maker is the branch underwriter and the decision is whether to accept an application within delegated authority or refer it. The answer changes for cases outside that authority: there, the regional manager is the decision-maker. The method avoids treating the project sponsor as the operational decision-maker merely because the sponsor requested the work.
Worked Example: Distinguishing a Request From a Decision
A hypothetical insurer’s service executive asks for a model to “find customers likely to call repeatedly.” The contact-center director controls staffing plans. Team supervisors assign available agents to queues each morning. The stated proposal is to support daily queue assignments. Identify the relevant decision-maker and decision, and explain which role is not the decision-maker for that choice.
Step 1: Interpret the request. “Find customers likely to call repeatedly” describes a requested prediction, not a choice. It does not name what someone will decide.
Step 2: Use the stated operating purpose. The proposal is meant to support daily queue assignments, so the operational choice is how to assign available agents across queues for the day.
Step 3: Identify who makes that choice. Supervisors assign the agents each morning. The contact-center director controls staffing plans, but the prompt does not say that the director makes daily queue assignments.
Conclusion. The supervisors are the decision-makers for the stated daily assignment choice. The contact-center director is responsible for staffing plans, a separate choice; the service executive is the project requester. The method avoids promoting a senior or visible stakeholder to decision-maker when another role actually controls the operational choice. The prediction request alone would not have been enough to identify the decision.
Worked Example: Clarifying a Resource-Allocation Decision
A hypothetical claims unit must decide whether to schedule one additional specialist shift on each of the next 10 business days. The claims operations manager has authority to change the schedule. A finance analyst provides these estimated daily costs, in dollars: with no additional shift, the cost is $1,200 if incoming specialist work is low and $3,600 if it is high; with an additional shift, the cost is $2,000 if work is low and $2,700 if it is high. For this illustration only, suppose the planning team assigns probability 0.75 to low work and 0.25 to high work. Identify the decision-maker and decision, then compare the expected costs using the stated estimates.
Step 1: Name the choice and authority. The choice is whether to schedule the additional specialist shift. The claims operations manager is the decision-maker because that manager has authority to change the schedule. The finance analyst supplies estimates but does not control the schedule.
Step 2: Calculate expected cost without the shift. Multiply each cost by its stated probability and add:
Step 3: Calculate expected cost with the shift.
Step 4: Compare and interpret. On these estimates, the expected cost is $2,175 − $1,800 = $375 higher per day with the additional shift. The difference is based only on the probabilities and costs supplied in the hypothetical; it is not a recommendation about real staffing. The decision context remains the manager’s scheduling choice. The calculation illustrates why naming the choice matters: it tells the organization what operational question the estimates could inform. It does not establish how any future model output should be used.
Check. The probabilities sum to 0.75 + 0.25 = 1.00, and each expected cost is between its corresponding low- and high-work costs. The method avoids comparing one scenario’s costs while ignoring the stated uncertainty.
A Short Justification: Why the Decision Changes the Comparison
For a finite set of possible outcomes, let \(Y\) denote the outcome and \(L(a,Y)\) the consequence (for example, cost) of choosing alternative \(a\). If the possible outcomes are \(y_1,\ldots,y_k\), with probabilities \(p_1,\ldots,p_k\), the expected consequence of alternative \(a\) is
This expression follows by partitioning the possible outcomes into the mutually exclusive cases \(Y=y_1,\ldots,Y=y_k\). The expectation of \(L(a,Y)\) is the sum of its value in each case multiplied by the probability of that case. The formula assumes the listed outcomes cover the possibilities being considered and that the probabilities and consequences are defined for the same decision context. If the choice, relevant consequences, or probability assumptions change, the comparison can change too.
The calculation does not tell us who has authority to make the choice. That must be established from the operating process. It does show why a vague request is insufficient: without named alternatives and consequences connected to a real choice, there is no well-defined comparison to make. In the staffing example, the same work conditions could matter differently if the decision were about a longer-term budget rather than a daily schedule.
Common Mistakes and Exam Traps
- Equating seniority with decision authority. Wrong reasoning: “The executive requested the model, so the executive is the decision-maker.” Correction: identify the person or role with authority over the specific operational choice. A sponsor and a decision-maker may be different.
- Calling a prediction a decision. Wrong description: “The decision is to predict repeat callers.” Correction: prediction is an analytical task. State the operational choice the task is intended to support, if that choice is known.
- Using a broad goal instead of a choice. “Reduce delays” states an objective, not what anyone can choose. Specify a feasible choice, such as how to allocate a limited number of appointments, and name the accountable role.
- Ignoring delegated authority. A role may make a choice only within defined limits. If cases outside those limits go to another role, identify the decision-maker separately for each context rather than claiming one person decides everything.
- Assuming a model is already needed. A clearly stated decision does not by itself prove that a predictive model is the right tool. Establishing the decision context is a framing step, not evidence that modeling will improve the process.
This framing discipline is aligned with the SOA Exam PA (Predictive Analytics) syllabus. It helps keep a predictive analytics task grounded in a real operational context before questions about specific model design are addressed.
Check Your Understanding
For each scenario, identify the decision-maker and the operational decision. Explain any role that is easy to confuse with the decision-maker.
- A hypothetical insurer’s chief actuary requests an analysis of policies that may need a rate review. A pricing committee has authority to approve rate changes, while analysts prepare the supporting work. Who is accountable for the rate-change decision, and what is the decision? (Answer: The pricing committee; whether to approve a rate change. The chief actuary requested the analysis, and the analysts prepare it.)
- A hypothetical claims director asks for a prediction of which repair estimates may need closer attention. Senior adjusters decide whether to assign an estimate to a specialist, while the director controls the department budget. Identify the decision-maker and choice for the stated review process. (Answer: The senior adjusters; whether to assign an estimate to a specialist. The director controls the budget, not the stated assignment choice.)
- A regional service manager requests an analysis to improve appointment availability. Branch coordinators choose how to distribute available appointment slots each morning, and the regional manager can change staffing levels monthly. For a task intended to support morning slot distribution, who makes the decision and what is it? (Answer: The branch coordinators; how to distribute the available appointment slots. The regional manager controls a separate monthly staffing decision.)
- A hypothetical insurer proposes a model for identifying claims “with unusual patterns.” The compliance officer can require an investigation, but the claims operations manager decides whether to reassign a claim to a specialist. If the stated purpose is specialist reassignment, identify the decision-maker and explain why the compliance officer is not the answer. (Answer: The claims operations manager; whether to reassign a claim to a specialist. The compliance officer has authority over investigations, a different choice.)
- A hypothetical facilities team must choose between scheduling a preventive inspection or leaving equipment on its current schedule. The maintenance supervisor approves the schedule; the analytics lead supplies a report, and a vice president funds the project. State the decision-maker and decision, and name the two non-decision-maker roles. (Answer: The maintenance supervisor; whether to schedule the preventive inspection or keep the current schedule. The analytics lead supplies the report, and the vice president funds the project.)