Situation
Members wanted to benchmark their performance to plan and improve, but the association could not share data without exposing each member's numbers to competitors. Sensitive operational data made a naive shared dashboard a non-starter.
All case studies
AssociationsSoftware & DataExecutive decision support engagement
An Association Sold Benchmarking Without Exposing Member Data
BCA
See the full projectRisk
Per-member
data isolation, enforced at the row level
Quality
Cohort
de-identified benchmarking across members
Revenue
New
benchmarking line for the association
The arc
Situation, work, outcome.
Work
We built a secure, multi-tenant analytics platform with strict per-member data isolation, so each member only ever sees their own data. On top of that we built a benchmark methodology for production planning and execution, letting members compare themselves against a de-identified cohort. Zyos served as the solution provider behind the performance benchmarks: an executive and operational decision-support engagement.
Outcome
Each member sees only their own figures, benchmarked against the cohort. The association can deliver decision-support to members and monetize benchmarking insights, all without ever exposing one member's data to another.
The operating-model arc
What discovery surfaced, what we built, what the QBR recalibrated.
Every engagement runs the same three-phase shape, foundation before automation, measured every cycle.
Phase 1, Model
Discovery- Mapped the member data model and the isolation requirements.
- Designed the per-member security model and the benchmark methodology.
Phase 2, Build
Build- Built the multi-tenant analytics platform with row-level isolation.
- Wired the executive and operational decision-support dashboards.
Phase 3, Operate
Ongoing- Members benchmark against the de-identified cohort.
- The association extends benchmarking as a member offering.
Case study, FAQ
Questions about this engagement.
Published as FAQPage schema for AI Overview + People Also Ask citation.
How do you let members benchmark without seeing each other's data?
With strict per-member data isolation enforced at the data layer (row-level security), so a member's view only ever contains their own records. Benchmarks are computed against a de-identified cohort, so members see how they compare without any individual member's numbers being exposed.
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Start with a measurement.
The value-impact OKRs we set together at kickoff become the case study when the engagement closes. One vendor, one roadmap, measured every quarter.