VOL. XCIV, NO. 247

MOAT TYPE BREAKDOWN

NO ADVICE

Tuesday, December 30, 2025

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Network moat

Standards Registry Moat

2 companies · 3 segments

A network moat where a canonical registry, identifier system, or reference dataset becomes the shared source of truth. Everyone references it to reconcile records, transact, report, or integrate systems. Because coordination value rises with adoption, the registry becomes hard to displace once embedded across an ecosystem.

Domain

Network moat

Advantages

5 strengths

Disadvantages

5 tradeoffs

Coverage

2 companies · 3 segments

Advantages

  • Default dependency: integrations and workflows assume the registry, making it the safe choice.
  • Switching friction: replacing the registry implies mass migration, mapping, and reconciliation risk.
  • Data compounding: broad usage improves coverage, correctness, and de-duplication quality.
  • Pricing power (sometimes): if access is required for compliance or reconciliation, fees can be sticky.
  • Adjacency leverage: registries can expand into validation, compliance, analytics, and tooling.

Disadvantages

  • Multi-homing and mapping: users can maintain crosswalks to multiple registries, reducing lock-in.
  • Governance scrutiny: registries can be treated as public utilities and face access/fee regulation.
  • Data quality risk: errors, duplication, or disputes can break trust and trigger alternatives.
  • Commoditization risk: open data initiatives or standards bodies can push toward free registries.
  • Negative network effects: if the registry becomes polluted (spam, fake entities), value declines.

Why it exists

  • Coordination need: participants require a shared naming/ID system to avoid mismatches and disputes.
  • Network adoption: the registry is more valuable when more parties use the same identifiers.
  • Embedding in plumbing: IDs are baked into databases, contracts, workflows, and compliance reporting.
  • Trust and governance: the registry is accepted because it is reliable, audited, and dispute-resolving.
  • High switching costs: changing canonical IDs requires widespread migration and reconciliation.

Where it shows up

  • Financial markets (securities identifiers, legal entity identifiers, reference data)
  • Supply chain and commerce (product identifiers, GS1-like codes, vendor registries)
  • Internet and developer ecosystems (package registries, certificate authorities, naming systems)
  • Healthcare (provider identifiers, drug codes, clinical terminology registries)
  • Government and legal systems (company registries, land registries, licensing registries)
  • Media and advertising (identity graphs, content IDs, measurement registries)

Durability drivers

  • Strong governance and dispute resolution (clear rules for creation, updates, merges, retirements)
  • High data quality and auditability (provenance, validation, versioning, change logs)
  • Deep embedding across systems (APIs, SDKs, vendor integrations, compliance templates)
  • Stable identifiers and backward compatibility (persistent IDs, predictable lifecycle rules)
  • Institutional endorsement (regulators, industry bodies, major platforms and incumbents)

Common red flags

  • High error/duplicate rates and slow dispute resolution undermining trust
  • Adoption is shallow: referenced for lookup but not used as the primary identifier
  • Stakeholders maintain multiple registries routinely (mapping becomes standard practice)
  • Regulators or industry groups push for open/free alternatives and interoperability mandates
  • Identity pollution: fake entities, spam registrations, and weak validation degrade value

How to evaluate

Key questions

  • Is the registry truly canonical, or just one of many that users map between?
  • What forces adoption: regulation/contract, ecosystem convention, or superior quality?
  • How costly is migration for an ecosystem participant (data rewrites, contracts, reconciliations)?
  • How strong is governance: can stakeholders trust changes and resolve disputes fairly?
  • Does the owner capture rents, or is it effectively a commodity utility?

Metrics & signals

  • Adoption breadth: number of institutions/systems referencing the IDs as primary keys
  • Embedding signals: prevalence in contracts, reporting standards, and integration templates
  • Data quality metrics: duplicate rate, error rate, dispute volume, correction speed
  • Update cadence and stability: change log volume, backward compatibility, ID persistence
  • Multi-homing indicators: usage of crosswalk tables, demand for export/mapping tools
  • Pricing and monetization: fee increases, churn response, regulatory constraints
  • Governance health: stakeholder participation, audits, transparency, complaint trends

Examples & patterns

Patterns

  • Identifiers used as the primary key in workflows, reporting, and reconciliation across firms
  • Registry-backed validation services that reduce counterparties’ due diligence burden
  • APIs and SDKs that make integration easy, accelerating adoption
  • Governance structures that make the registry trustworthy and stable over decades

Notes

  • The moat is strongest when the registry is embedded as the primary key in systems and contracts, not just used for occasional lookup.
  • The main risks are governance failures (trust break) and mandated openness (rent capture capped).

Examples in the moat database

Curation & Accuracy

This directory blends AI‑assisted discovery with human curation. Entries are reviewed, edited, and organized with the goal of expanding coverage and sharpening quality over time. Your feedback helps steer improvements (because no single human can capture everything all at once).

Details change. Pricing, features, and availability may be incomplete or out of date. Treat listings as a starting point and verify on the provider’s site before making decisions. If you spot an error or a gap, send a quick note and I’ll adjust.