Working draft for review. A plain-language summary of our intended data-handling approach — finalized in the study’s confidentiality agreement and subject to counsel review. Not legal advice.
Resource Workplace Benchmark · Data Security & Confidentiality
Built to clear your internal approval
Taking part means a short, well-defined set of corporate metrics — not personal data, not your raw systems. This page is written for the people whose sign-off you need: Information Security, Legal, Finance, and Procurement.
Forward it to them. It lays out exactly what we ask for, what we never collect, how the data is protected, how confidentiality is enforced, and how the study is structured to be antitrust-safe — so “yes” is an easy call.
Corporate aggregates only — no employee-level personal data
n ≥ 5
No cut is ever shown unless it pools enough companies to prevent re-identification
Never named
No company-level data is attributed — not in the report, not to sponsors
You own it
You keep ownership of your data and can withdraw it before publication
Who has to say yes
The questions each reviewer will ask — answered up front
Participation is usually championed by the Workplace or Real Estate lead, then routed for approval. Here is what each gate tends to ask, and the short answer.
Information Security
How is our data sent, stored, and deleted — and does AI touch it?
“Where does it live and who can see it?”
Encrypted in transit and at rest, access limited to the named study team, no participant data fed to third-party or public AI tools, and deleted on a defined schedule. We’ll complete your security questionnaire.
Legal / Privacy
What’s the agreement, who owns the data, and is there antitrust risk?
“What are we signing, and can we get out?”
A mutual confidentiality agreement; you retain ownership and a right to withdraw before publication; and the study is run by a neutral third party on aggregated, historical data — the standard antitrust-safe pattern.
Finance / IR
Could our cost or revenue figures be identified or leak?
“Is any of this sensitive or market-moving?”
Figures are normalized and pooled into ranges with a minimum cohort size before anything is shown. We collect historical actuals, not forecasts, and never publish a company-level number.
Procurement / Vendor risk
Is the vendor diligenced — questionnaire, insurance, references?
“Does this clear our vendor process?”
We respond to standard security questionnaires, can provide proof of insurance on request, and offer prior-study references. Our footprint is deliberately small: one defined dataset, one named team.
Data minimization
A narrow set of corporate metrics — and a short list of what we never collect
The dataset is intentionally small and company-level. Keeping it aggregate is what keeps the study out of personal-data scope and easy to approve.
What we ask for
Company scale: revenue, employee count, office-assigned population
Real estate cost and a high-level TCO component split
Average and peak utilization, with the source-system noted
CRE operating model: internal FTE, outsourcing mix, function scope
One work contact to coordinate — a business email, nothing more
What we never collect
Employee-level or personal data of any kind
Customer, payroll, or compensation/wage data
Direct access to your systems, badge feeds, or dashboards
Lease documents, contracts, or other source records
Forward-looking forecasts or unannounced plans
Anything you decide is too sensitive to share — fields are optional
Why this matters for approval
No personal data means a lighter privacy review
Because the study collects corporate aggregates and a single business coordinating contact — not employee-level information — it stays largely outside the scope of personal-data regimes like GDPR and CCPA. That alone removes the heaviest part of most privacy reviews. Site-level detail is optional; portfolio totals are the default so that physical-security-sensitive specifics never have to leave your walls.
Confidentiality by design
Anonymized is a promise. Here is the control behind it.
“Aggregated and anonymized” only means something with a rule attached. This is the rule.
Anti-attribution
Minimum cohort size. No metric, cut, or peer comparison is ever shown unless it pools at least five companies — small cells are suppressed, not exposed.
No re-identification. Cuts are reviewed so a company can’t be backed out by deduction (e.g., the only company of its size in a slice).
No company-level attribution. The final deliverable never names a participant’s figures — results are ranges and distributions.
Sponsors don’t see raw data. Shaping the cohort or metrics never grants access to another participant’s named numbers.
How your data is kept
Raw kept separate. Your raw response is stored apart from the normalized analysis and is never published.
Blinded outputs. Everything that leaves Resource — reports, sponsor cuts, readouts — is blinded and aggregated.
Attribution only on request. Your name or figures appear only if you give explicit written approval — the default is always blinded.
No marketing use. We won’t use your company name or logo in any promotional material without written consent.
Information security
How the data is handled, technically
Written for your InfoSec reviewer. We’re happy to complete your standard security questionnaire against these same points.
Transport, storage & access
Encrypted in transit and at rest. Data moves over secure channels and is stored encrypted.
Least-privilege access. Limited to the named study team on a need-to-know basis; no general or public access.
Isolated from web properties. Participant data is never placed in any public-facing site, form, or application.
Controlled correction log. Edits are logged with original value, change, reason, owner, and date — nothing silently overwritten.
AI tooling & retention
No AI training on your data. Participant data is not submitted to public or consumer AI tools and is never used to train third-party models.
Defined retention. Raw data is retained only as long as the study requires, then deleted on a set schedule.
Deletion on request. You can ask us to return or destroy your raw submission; we confirm in writing.
Legal & antitrust
The agreement, your rights, and why competitor benchmarking is safe here
The points your Legal team will look for — and the structural reasons peer benchmarking among similar companies stays on the right side of the line.
The agreement & your rights
Mutual confidentiality agreement in place before any data is shared.
You retain ownership of the data you submit — you grant only the limited use needed to produce the benchmark.
Right to withdraw your data any time before publication, and to request deletion afterward.
Defined use of results. Where outputs appear, and the no-naming and no-marketing-use defaults, are written into the agreement.
Structured to be antitrust-safe
Neutral third party. Resource — not a participant — administers collection and aggregation; companies never exchange data directly.
Aggregated only. Outputs are pooled ranges and distributions (n ≥ 5), never individualized company data.
Historical, not forward-looking. We collect past actuals, not pricing, forecasts, or future plans.
No competitively sensitive exchange. No compensation, wage, or customer-pricing data is collected.
Plain-language note
Why the structure is the safeguard
Benchmarking among comparable companies is well-established practice. The recognized guardrails — a neutral administrator, aggregation with a minimum number of participants, historical rather than forward-looking data, and no exchange of pricing or wage information — are built into this study by design, so your Legal team can confirm the structure rather than reconstruct it. We’ll work from your paper or ours.
Getting internal sign-off
A short path to “yes” — and we’ll support every step
A suggested routing for the internal champion. We can join a call with any of these reviewers or respond to their questions directly.
1
Champion confirms the value
The Workplace, Real Estate, or Finance lead decides the benchmark supports a decision worth participating for.
2
Information Security review
Forward this page; we complete your security questionnaire and answer follow-ups. Scope is small and well-defined.
3
Legal & Privacy review
Mutual NDA and study terms; confirm ownership, withdrawal rights, and the antitrust-safe structure. We’ll work from your template or provide ours.
4
Finance / data-owner sign-off
The cost and headcount owner confirms the fields and that figures are historical and pooled before any output.
5
Procurement, if required
Vendor onboarding, proof of insurance, and references — provided on request to clear the vendor process.