What it is worth, domain by domain
Running each regulated object as a governed holon, one source of state, rules, history, and role-specific views, is not an architectural nicety. It removes the reconciliation, re-entry, and re-mapping work that quietly consumes most life-science operating budgets. Below, the added value is split by the same domains and holons you met on Practice, described qualitatively and quantitatively, with the calculation shown for every figure.
How every figure is calculated
The mechanism is always the same. Today, the same fact is captured, reconciled, and checked again in every system and for every use. In a holon, state, rules, history, and views are governed once and reused, so the repeated work disappears. Each figure therefore compares today's per-system, per-use effort against a single governed object:
Holon: Clinical Trial
One governed trial record replaces the protocol / EDC / CTMS / TMF patchwork. Every dashboard, monitoring view, and study report reads the same state, rules, and history, so milestones stop waiting on cross-system reconciliation and audit trails never diverge.
How: a milestone reconciliation across 4 systems ≈ 200 h; reading from one governed record ≈ 110 h → ~90 h saved × ~6 milestones ≈ 540 h saved / study.
How: continuous boundary checks (visit windows, eligibility, query aging) replace the manual pre-lock sweep → 6 weeks → ~3.5 weeks.
How: 540 h × $80. Example: a 25-study portfolio ≈ $1.1M / year, before the value of earlier locks.
Holon: Dossier Submission
Submission content is generated from state that was already governed and validated, instead of being assembled module by module against the deadline. Late discovery of missing or inconsistent data, the classic filing-date risk, disappears because validation is always on.
How: module assembly + cross-module consistency QC ≈ 320 h manual; generating from governed state + SHACL validation ≈ 160 h → ~160 h saved / submission.
How: continuous validation removes the pre-submission "consistency sweep" (~15 working days of manual checking against eCTD and controlled terminology).
How: 160 h × $80. Example: plus a protected filing date, each avoided month of delayed approval on a mid-size product is worth far more and is not counted here.
Holon: Batch / Lot
A release view is computed from linked, governed manufacturing state, materials, process version, test results, deviations, instead of being pieced together from LIMS, MES, and paper. Genealogy is traceable end to end in seconds, and the certificate of analysis is a projection, not a rebuild.
How: assembling and checking a batch record across systems ≈ 24 h; a computed release view ≈ 15 h → ~9 h saved / batch.
How: genealogy, CoA, and open deviations are already linked to the batch holon, so there is no manual trace-back across systems.
How: 9 h × $80. Example: 600 batches / year ≈ $430k / year, before faster release frees inventory and working capital.
Holon: Quality Event
A deviation, its investigation, and its CAPA stay connected to every batch and product they touch, and those links never rot. Because events are linked rather than scattered across spreadsheets and the QMS, recurrence and systemic patterns become visible instead of being rediscovered each time.
How: gathering evidence + scoping impact across QMS and spreadsheets ≈ 30 h; with linked context ≈ 18 h → ~12 h saved / event.
How: impacted batches and products are already linked, so the manual impact search that stalls most investigations is removed.
How: 12 h × $80. Example: 500 events / year ≈ $480k / year, before fewer repeat deviations reduce the event count itself.
Holon: Safety Case
A safety case carries its full timeline and its links to related events, and reporting timelines are governed by rule rather than by hand. Because cases stay connected, recurring patterns across them surface in time to act, rather than after the reporting window has closed.
How: case compilation + consistency checks ≈ 8 h; with a governed case holon ≈ 5.5 h → ~2.5 h saved / case.
How: cross-case aggregation is continuous over linked cases, replacing the periodic manual pull that delays signals.
How: 2.5 h × $80. Example: 10,000 cases / year ≈ $2M / year, before the risk avoided by earlier signals.
Holon: Medicinal Product
One product identity exposes the right regional view on demand, instead of product attributes being re-entered per market and per system. "What is the approved strength here?" stops taking days. This is the reuse backbone of the whole model: map once to a shared identity, project everywhere.
How: attributes today are re-entered across ~8 systems and markets; a governed identity is updated once and projected → one update replaces eight.
How: a region/role projection reads one identity, so "approved strength here?" is a query, not a cross-system hunt.
How: removing ~7 duplicate re-entries × ~450 h each × $80 ≈ $252k. This mirrors the ~750× source-to-target consolidation seen at enterprise scale.
Holon: Lab Result
A result carries its provenance and specification checks with it, so instrument provenance and review history never thin out as the value moves from instrument to LIMS to report. What is "official" is decided once and travels with the result.
How: manual transcription + spec check across instrument → LIMS → report ≈ 12 min / result; with checks that travel with the result ≈ 6 min → ~6 min saved / result.
How: specification checks are attached to the result, removing the re-verification handoffs between systems.
How: 6 min ≈ 0.1 h × $80. Example: 200,000 results / year ≈ $1.6M / year.
A Net Promoter Score the graph computes for itself
No survey needed. Because every holon's state, rules, history, and views are linked, shareable, and reusable, each object can in effect be asked “would you recommend this way of working?”, and its four graphs already hold the evidence to answer. We keep the classic scale, respondents score 9–10 as Promoters, 7–8 as Passives, 0–6 as Detractors, and NPS = %Promoters − %Detractors (−100 to +100), but replace the survey question with facts read straight off each holon's graphs.
State completeness
Share of the object's mandatory fields that are populated and valid right now.
Rule conformance
Share of applicable SHACL constraints the instance satisfies without exception.
Provenance completeness
Share of the object's events with an unbroken, attributable lineage.
Reuse reach
Role views and cross-holon links served from this one object, against target.
A trial instance scores 9.4 on state completeness, 9.6 on rule conformance, 9.1 on provenance completeness, and 8.7 on reuse reach → recommendability ≈ 9.2, a Promoter. Repeat this across the whole population of instances: say 78% land as Promoters, 16% Passives, 6% Detractors → per-holon NPS = 78 − 6 = +72.
| Holon | Fact-based NPS |
|---|---|
| Clinical Trial | +72 |
| Dossier Submission | +64 |
| Batch / Lot | +69 |
| Quality Event | +61 |
| Safety Case | +58 |
| Medicinal Product | +81 |
| Lab Result | +66 |
How: pool every instance across all seven holons (equivalent to weighting each per-holon NPS by its instance count), then apply the same %Promoters − %Detractors formula once, at the population level. All NPS figures on this page are illustrative planning estimates, to be re-based on your own graphs.
Each holon saves work on its own, but the real return is shared: the governed product identity feeds the trial, the batch, the safety case, and the submission at once. Map a fact once, and every domain that reuses it stops paying to re-create it, which is why the per-holon figures above are conservative when a whole organization runs this way.
The point: value is not a slide, and it is not a survey either. Because every fact is linked, shareable, and reusable, each holon earns a fact-based NPS straight from its own four graphs, and those seven scores integrate into a master NPS of roughly +70. See it end to end on the Case page, then see how it is delivered on Method.