> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pensionsportal.ie/llms.txt
> Use this file to discover all available pages before exploring further.

# Data Quality

> Why member data quality matters to the Pensions Authority and how PensionsPortal.ie keeps your scheme data accurate and submission-ready.

# Data Quality

Data quality has moved from a back-office concern to a frontline governance obligation. The Pensions Authority is deploying a **new supervisory IT platform in 2026** that will include automated data validation and structured data ingestion — replacing the existing manual submission processes with a system that will flag data anomalies at the point of submission.

Schemes that maintain high-quality, consistent, and complete data will pass through the new platform with minimal friction. Schemes with poor data will trigger automatic queries that could escalate to supervisory engagement.

<Warning>
  The PA's new supervisory platform is expected to go live in 2026. Trustees should treat the period before launch as an opportunity to identify and resolve data quality issues — not as a deadline to rush corrections immediately before submission.
</Warning>

***

## Why Data Quality Matters

### Regulatory Submissions

Annual scheme returns and other statutory submissions are validated by the Pensions Authority against the data held on scheme records. Discrepancies — member counts that don't match, contribution totals that don't reconcile, missing KFH registrations — trigger queries that require trustees to respond within defined timeframes.

A scheme with well-maintained records responds to data queries in hours. A scheme with poor records may take weeks to reconcile discrepancies, creating a compliance gap under supervisory review.

### Member Outcomes

Every member outcome calculation depends on accurate data:

* Benefit statements require accurate contribution history and employment dates
* Transfer values require accurate fund and contribution records
* Retirement calculations require accurate scheme registration and benefit structure data

Inaccurate data leads to member detriment — incorrect benefit statements, overpayments, underpayments — all of which carry regulatory and reputational consequences.

### GDPR

The GDPR requires that personal data is **accurate and kept up to date**. A trustee board that allows member data to become stale — outdated addresses, incorrect names, unverified PPS numbers — is in breach of its data protection obligations as well as its IORP II governance standards.

***

## What the PA Expects

| Data Area                | Expectation                                                                                                                 |
| ------------------------ | --------------------------------------------------------------------------------------------------------------------------- |
| **Member records**       | Accurate names, dates of birth, addresses, PPS numbers. Deferred members must be tracked; no "ghost" members in the system  |
| **Contribution records** | Employer and employee contributions reconciled to payroll records; no unresolved payment exceptions                         |
| **Benefit calculations** | Fund values and accrued benefits consistent with actuarial data (DB schemes); contribution credits accurate (DC schemes)    |
| **KFH registrations**    | All four KFH roles registered with the PA; notification dates on file; no lapsed or unconfirmed appointments                |
| **Scheme returns**       | Annual return submitted on time; data consistent with internal scheme records                                               |
| **Policy currency**      | Policy dates accurate in the scheme record — the PA can cross-reference adoption dates with the policy documents themselves |

***

## Data Quality Dashboard

PensionsPortal.ie includes a built-in **Data Quality Dashboard** that validates your scheme data continuously and surfaces exceptions for remediation.

Navigate to **Data Quality** from the main dashboard.

<Steps>
  <Step title="Review validation results">
    The dashboard shows validation results across all data categories: member records, contribution data, KFH registrations, and policy currency. Each category shows a pass/fail count and a list of specific exceptions.
  </Step>

  <Step title="Prioritise by severity">
    Exceptions are rated by severity:

    * **Critical** — data that will fail PA submission validation or create member detriment (e.g., missing PPS numbers, unreconciled contributions)
    * **High** — data that will generate PA queries (e.g., outdated addresses for a significant proportion of members)
    * **Warning** — data that should be corrected but is not immediately submission-blocking
  </Step>

  <Step title="Assign and track remediation">
    For each exception, assign it to a responsible person (administrator, adviser, or KFH) with a target resolution date. The dashboard tracks open exceptions and flags overdue items.
  </Step>

  <Step title="Export a validation report">
    Generate a validation report for board or audit review. The report shows the overall data quality score, open exceptions by category and severity, and remediation status. This report is evidence of active data governance for supervisory purposes.
  </Step>
</Steps>

***

## Preparing for the PA's 2026 Platform

<CardGroup cols={2}>
  <Card title="Clear Critical Exceptions Now" icon="triangle-exclamation">
    Address all Critical-severity data exceptions before the PA's new platform goes live. Missing PPS numbers, unreconciled contributions, and incomplete KFH registrations are the most common sources of submission failures.
  </Card>

  <Card title="Reconcile Contribution Records" icon="check-circle">
    Ensure all contribution periods are reconciled and closed. Outstanding payment exceptions and unmatched contributions should be resolved and cleared from the data quality dashboard.
  </Card>

  <Card title="Update Member Records" icon="person">
    Verify that active and deferred member records are complete and current. Identify and update outdated addresses, and confirm PPS numbers are validated against scheme records.
  </Card>

  <Card title="Confirm KFH Registrations" icon="users">
    Verify that all KFH appointments are notified to and confirmed by the Pensions Authority. Check that the notification dates in PensionsPortal.ie match the PA's records.
  </Card>
</CardGroup>

***

## Regular Data Review

Data quality is not a one-time exercise — it requires ongoing attention. We recommend:

| Frequency                      | Review                                                                                                   |
| ------------------------------ | -------------------------------------------------------------------------------------------------------- |
| **Quarterly**                  | Review the Data Quality Dashboard for new exceptions; assign remediation for all Critical and High items |
| **At each board meeting**      | Include a data quality status update as a standing agenda item; minute the discussion                    |
| **On each contribution cycle** | Reconcile contributions before closing the period                                                        |
| **Annually**                   | Export a full validation report as part of the scheme's compliance record                                |
| **Before any PA submission**   | Run a full validation check and resolve all Critical exceptions before submission                        |

<Tip>
  Add a quarterly data quality review to your **Compliance Calendar** so it doesn't get crowded out by other governance obligations. Navigate to **Compliance → Calendar** and add a recurring quarterly task.
</Tip>

***

## Data Quality and SRP Readiness

Data quality contributes directly to two SRP pillars:

* **Operations:** The PA assesses data accuracy and contribution reconciliation as part of its operational governance review
* **Communications:** Benefit statement accuracy depends on the quality of underlying member and contribution data

A scheme that cannot produce accurate, reconciled data on demand is not operationally ready for supervisory review.

***

## Further Reading

* [Supervisory Review Process — Operations Pillar →](/trustee-guide/supervisory-review-process)
* [Member Management →](/trustee-guide/your-obligations)
* [GDPR and Data Protection →](/compliance/compliance-overview)