Once-Only Data and Joined-Up Government: Why Tax Administrations Become a National Data Utility—and What the Caribbean Must Do Next

Executive Summary

Modern tax administrations are no longer just collectors of returns; they are increasingly becoming data utilities—institutions that ingest, reconcile, and operationalise high-value data across the economy to deliver better compliance, better service, and better policy outcomes. A defining feature of this shift is the move toward once-only data and joined-up government: taxpayers should not have to submit the same information repeatedly to multiple agencies when government already holds it, and public institutions should be able to share data (lawfully and securely) to reduce duplication, leakage, and administrative friction.

The OECD’s 2025 report on tax administration digitalisation highlights how jurisdictions are enhancing data-sharing arrangements and digital transformation capabilities that enable more integrated service delivery and compliance.

For Caribbean countries, the case is compelling:

• Many administrations face limited resources, so reducing duplication can yield outsized gains.

• Informality and fragmented registries create compliance gaps that joined-up data can narrow.

• Better data integration improves VAT accuracy, PAYE enforcement, customs linkages, and targeted audit selection—without increasing burden on compliant taxpayers.

This article defines once-only data, explains how global implementation is evolving, outlines the legal and governance preconditions for safe data integration, and provides practical pathways for Caribbean governments and businesses to prepare for a more connected compliance environment.

1) What “once-only data” means in practice

Once-only data is a principle: government should collect a piece of information from a person or business once, and then reuse it across agencies—subject to lawful authority, consent rules (where applicable), and strong privacy/security controls.

In a tax context, once-only data is not only a citizen convenience goal. It is a compliance and integrity lever. When a revenue authority can cross-check reliable data across agencies, it can:

• validate registrations and de-registrations

• reduce identity and refund fraud

• improve PAYE compliance by matching employees, employers, and wages

• improve VAT integrity by linking sales/purchases and import activity

• support better debt management and targeted enforcement

The OECD report points to enhancements in data capabilities and sharing arrangements as part of digital transformation initiatives.

2) Why tax becomes a “national data utility”

Tax administrations sit at the center of economic life. They interact with:

• individuals (employment, income, benefits, residency)

• businesses (sales, purchases, payroll, profits, ownership)

• trade flows (imports/exports, customs valuation)

• financial flows (payments, refunds, withholding, reporting regimes)

As administrations digitise, the tax authority’s data function becomes a national asset—not for surveillance, but for integrity and service. This is why modern frameworks treat data as a core building block: data supports prefilling, risk analytics, and embedded compliance. (We saw this in the prefilling expansion described in the OECD report.)

Key shift: the authority moves from “waiting for returns” to “operating a connected compliance system” where data is assembled, validated, and acted upon with far less manual intervention.

3) What joined-up government looks like: the core data linkages

Joined-up government does not mean every agency sees everything. It means specific, governed linkages that deliver legitimate outcomes. The common linkages used internationally include:

3.1 Civil registry / national ID ↔ tax registry

Purpose:

• identity verification

• deduplication

• address and status updates

• death notifications (reduces fraud)

3.2 Company registry ↔ tax registry

Purpose:

• faster registration integrity

• verification of directors and beneficial ownership

• lifecycle events: incorporations, dissolutions, mergers

3.3 Social security / NIS ↔ PAYE and payroll reporting

Purpose:

• match wage reporting

• detect under-withholding

• strengthen compliance for statutory deductions

3.4 Customs ↔ VAT and corporate tax

Purpose:

• import VAT validation

• reconcile imports to sales and stock movements

• detect undervaluation or missing declarations

• link importer activity to VAT registration status

3.5 Licensing bodies ↔ registration and compliance status

Purpose:

• enforce compliance conditions for operating licenses

• reduce informal economic activity

• improve fairness across sectors

3.6 Public procurement / government payments ↔ withholding / VAT compliance

Purpose:

• ensure contractors are tax-compliant

• improve withholding integrity

• improve VAT treatment of government suppliers

Caribbean insight: these linkages are achievable in staged programs. The first wins typically come from registry alignment (IDs and company registries) and customs-VAT linkages.

4) Data sharing is not “just IT”: the governance and legal backbone

Joined-up data programs fail when governments treat them as purely technical. They are fundamentally:

• legal

• governance-led

• risk-managed

• privacy and cybersecurity sensitive

4.1 Legal authority and purpose limitation

Effective sharing requires clear legal bases:

• who can share

• what can be shared

• for what purpose

• under what safeguards

• retention, deletion, and audit requirements

4.2 Privacy, proportionality, and trust

Tax data is sensitive. So is health, immigration, and social data. Public confidence is fragile. Programs must be proportionate:

• share only what is necessary

• protect against secondary use

• ensure transparency and oversight

4.3 Security architecture

Once-only data increases connectivity—and connectivity increases risk. Security controls must include:

• strong digital identity and access management

• encryption and secure APIs

• monitoring and anomaly detection

• incident response and breach notification processes

4.4 Data quality management

Sharing bad data creates harm faster. Joined-up programs must invest in:

• deduplication and entity resolution

• master data governance

• data correction workflows

• clear data stewardship responsibilities

5) Global implementation patterns: how countries usually build joined-up data capability

The OECD report notes improvements in data sharing arrangements and digital transformation initiatives.  In practice, countries tend to follow a staged path:

Stage 1: Registry alignment and unique identifiers

• unify identifiers across agencies

• improve address and status data

• deduplicate core registries

Stage 2: Targeted high-value integrations

• customs ↔ VAT

• payroll/PAYE ↔ social security

• company registry ↔ tax registry

Stage 3: Platform and API ecosystem

• standard schemas

• trusted intermediaries

• higher-frequency data exchange
  This aligns with the broader shift toward API enablement in taxpayer touchpoints.

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Stage 4: Proactive and preventative compliance

• prefilling and nudges

• real-time validations

• reduced need for broad, disruptive audits

6) Why this matters to Caribbean tax performance (and fairness)

Caribbean economies often face:

• significant informality

• constraints in audit capacity

• revenue leakage via under-reporting and weak enforcement

• administrative burdens that discourage formalisation

Joined-up data supports a different enforcement model: targeted, evidence-led, less disruptive, and fairer.

Practical gains

• Better VAT integrity: link imports to VAT filings; detect missing output tax

• Better PAYE enforcement: reconcile payroll and contributions; detect non-remittance

• Better registration integrity: stop “ghost businesses” and identity fraud

• Faster service: fewer documents required for routine tasks

• Improved policy: better empirical visibility into sectors and risks

7) The business impact: why CFOs must treat data sharing as a tax risk factor

As governments become more connected, audits become less “document-driven” and more “data-driven.” That means:

• discrepancies between agencies surface quickly

• poor master data becomes compliance risk

• inconsistent VAT coding becomes visible across datasets

• import activity can be compared directly to VAT declarations

• payroll patterns can be compared to statutory contribution records

New reality: Your risk profile will increasingly be determined by data consistency, not only by what you intended or explained.

8) Caribbean roadmap: implementing joined-up data without creating backlash

Here is a practical pathway that balances ambition and legitimacy.

Phase 1 (0–12 months): Establish legitimacy and foundations

• publish a clear data sharing policy with purpose limitations

• map high-value datasets and “use cases” (e.g., import VAT verification)

• build governance: a data sharing steering committee and security oversight

• improve registry quality (deduplication and identifiers)

• implement core access controls and audit trails

Phase 2 (12–24 months): Deliver quick wins with targeted integrations

Prioritise two or three integrations that yield visible gains:

• customs ↔ VAT (import VAT validation and mismatch detection)

• company registry ↔ tax registry (director verification and lifecycle)

• payroll/PAYE ↔ NIS (wage reporting consistency)

Phase 3 (24–48 months): Expand to ecosystem enablement

• establish API standards and schemas

• enable secure data exchange with trusted intermediaries

• introduce assisted compliance services (prefilling components, validation checks)

Phase 4 (48+ months): Proactive compliance and service redesign

• expand prefilling and automated nudges

• reduce repetitive reporting obligations

• establish integrated taxpayer accounts (single view of obligations)

9) Composite case study (anonymised): “Import-to-Retail Chain”

Situation: A retailer imports significant volumes of goods. VAT filings show relatively low outputs compared to import volume.
Traditional audit approach: field audit, invoice sampling, lengthy investigations.
Joined-up approach: integrate customs and VAT data to compare import values to sales patterns; identify discrepancies; issue targeted requests.
Outcome: faster identification of high-risk cases, reduced disruption for compliant firms, improved fairness.

Lesson: joined-up data increases precision. It can improve compliance without increasing blanket burdens.

10) What Dawgen Global provides: governance, controls, and readiness

Dawgen Global supports clients in preparing for connected compliance environments and data-driven audits through:

• tax governance and operating model reviews

• VAT/PAYE and import VAT reconciliations

• master data clean-up and control frameworks

• audit defence packs built on data lineage

• advisory support on compliant data sharing models and risk controls (public and private sector)

Next Step!

If your organisation wants to strengthen tax compliance, reduce VAT and payroll risk, and prepare for the shift toward joined-up data and more data-driven enforcement, Dawgen Global can help with a practical approach tailored to Caribbean realities.

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