Stabilize performance metrics before they are used to justify decisions, funding, or action.

Organizations rarely realize their metrics are unstable until they are forced to defend them—during audits, funding reviews, leadership decisions, or public reporting. By that point, rework is expensive, timelines are compressed, and confidence in the data has already eroded.

ARB is an advisory engagement designed to contain measurement and interpretation risk before unreliable metrics are used to drive decisions. The work establishes a defensible, empirical foundation for analytics, benchmarking, and improvement—so results can be trusted, explained, and used responsibly.

ARB is not about producing more dashboards or accelerating reporting. It is about ensuring that what already exists is stable enough to support action.

When Organizations Engage ARB

ARB is most useful when leaders are being asked to act on data they don’t fully trust, including situations such as:

• External reporting, audits, or funding reviews
• Multi-site or multi-source data with inconsistent definitions
• Performance metrics that are debated, revised, or repeatedly reworked
• Dashboards that exist but are not used confidently
• Pressure to explain variation without a stable analytic baseline

If reporting feels reactive, fragile, or difficult to defend, ARB provides the structure needed to restore credibility.

What ARB Delivers

ARB helps organizations move from contested or fragile metrics to shared understanding and defensible use of data. The focus is on readiness, appropriate benchmarking, and explicit boundaries around what the data can—and cannot—support.

ARB provides:

• Assessment of analytic readiness and measurement risk

  Identify where data structure, definitions, cadence, or governance undermine confidence in results.

• Empirically grounded benchmarks

  Establish defensible baselines that define what “typical” performance actually looks like—before targets or interventions are set.

• Structured insight into variation and patterns

  Surface meaningful differences across sites, programs, or populations without overstating conclusions.