Some decisions fail not because analytics were poorly executed, but because no one was accountable for determining whether the evidence was sufficient to justify the decision itself.
Analytic governance exists to address that gap.
Decision risk is forward-looking. It concerns whether available evidence can safely support an action that has not yet been taken: pricing moves, portfolio changes, launches, or strategic commitments. Managing that risk requires authority, judgment, and accountability, not just analysis.
Analytic governance should not be confused with data governance or model lifecycle governance. It does not manage data assets, define ownership or stewardship, enforce access controls, or oversee model deployment and monitoring. Those functions address operational and compliance risks.
Analytic governance addresses a different risk: whether analytic evidence is sufficient to justify a specific decision.
How Analytic Governance Applies to Choice Modeling
Choice models are a high-risk domain for decision-making because they often appear precise while masking structural uncertainty.
In this context, analytic governance determines:
- whether the study is aligned with the decision it will be used to make,
- whether the design and data can identify what the decision requires, and
- whether results will be interpreted and applied safely.
These are governance determinations, not modeling tasks.
The Role of Audit Within Governance
Audits and reviews play an important role, but they are inputs, not substitutes, for governance.
An audit can clarify:
- what a design can and cannot identify,
- where power is insufficient,
- where structural confounds exist, and
- where outputs are unstable at the decision margin.
What an audit cannot do is decide whether evidence is sufficient to proceed.
Audits inform analytic governance.
Governance decides whether the evidence is sufficient for the decision.
That authority cannot be distributed across checklists, committees, or metrics.
Why This Distinction Matters
When governance is absent, studies are evaluated using procedural proxies: fit statistics, best practices, or peer review. A study can pass all of these checks and still be unsafe to use.
Analytic governance exists to prevent that failure mode by making decision sufficiency explicit and owned.
In late-stage situations, governance may be applied under constraint, sometimes referred to as decision rescue. In most cases, however, governance is most effective when applied before decisions are locked in.