Data sits at the core of every retirement system. Service credit records, salary histories, beneficiary designations, contribution totals, contact details. These are the key data points that form the foundation of benefit calculations, member communications, and fiduciary accountability.
When participant data is accurate and up to date, plans can have confidence that benefit estimates are reliable, and the statements members receive reflect an accurate state of their pensions. It doesn’t stop there either. Retiree payroll can function without manual intervention. In short, effective pension plan administration revolves around data.
When data fails, the consequences show up everywhere: in call volumes, in processing delays, in overpayments that are costly to recover, and in member trust that is difficult to rebuild.
The importance of accurate data cannot be overstated, and the industry conversation is catching up. Data integrity has become a recurring theme at NCPERS, PRISM, and other major public pension forums, moving from a back-office topic to a board-level agenda item. The reason is straightforward: as plans face pressure to modernize operations, demonstrate accountability, and improve member outcomes, the quality of the data underneath all of those objectives is becoming harder to ignore.
Data problems in pension administration are rarely dramatic. They accumulate quietly over time, and the financial consequences are very real.
One data management firm estimates that more than a billion dollars is paid annually to deceased pension participants across US plans – and through our own research, we found that 2.33% of plan members of a pensionable age are dead, but their deaths have not been reported to pension plans in a timely way.
Benefit calculation errors from incomplete service credit records or misreported salary data can lead to recoupment situations that are difficult for members and expensive for plans to resolve. The Department of Defense identified more than 23,000 employees receiving incorrect pension contributions due to calculation errors in their records.
Beyond direct costs, poor data quality creates indirect pressure. Staff spend time investigating discrepancies rather than serving members. Actuarial valuations are only as sound as the underlying records. Audits become more complex when data cannot be readily verified.
Historically, many plans approach data quality as a remediation exercise. A cleanup project is funded, records are scrubbed, and the immediate issues are resolved. But without changes to how data enters and moves through the system, the same problems reappear within a few years. A cleanup project addresses a backlog. An operating discipline prevents one from forming.
Participant data degrades naturally over time – Lexis Nexis estimates by as much as 17% annually. Members move and don't update their addresses. Beneficiary designations go unchanged after major life events. Employers submit payroll data with inconsistencies that aren't caught at the point of entry. Deferred members who left public service years ago become effectively invisible until they approach retirement and their records need to be reconstructed.
But the plans treating data integrity as an ongoing operating condition are the ones seeing the benefits downstream. They validate data at the point of entry rather than correcting it later. They run regular exception reporting to catch anomalies before they compound. They set clear expectations for employer data submissions, with automated checks that flag errors before they enter the administration system. And they treat member self-service not just as a convenience feature but as a data quality tool, encouraging members to verify and update their own records. We explore member self-service in more depth in this article.
There is a direct line between data quality and member engagement. Every communication a retirement system sends depends on accurate underlying data. An annual member statement that shows incorrect service credit doesn't just create a support call. It undermines the member's confidence in the plan.
Personalized communications amplify both the benefits and the risks of data quality. When the data is right, personalized content helps members understand what they have earned and what their options are. When the data is wrong, personalized content makes the error more visible and more damaging. Plans that want to improve member engagement need to invest in data integrity first. The communication layer is only as credible as the data beneath it, as we explored in our recent article on annual member statement engagement.
For plan administrators and boards, data integrity is ultimately about accountability. Benefit calculations that rely on inaccurate records create financial risk. Communications that reflect incorrect information create reputational risk. An inability to verify data during an audit creates compliance risk.
Federal oversight agencies have increasingly emphasized the responsibility of fiduciaries to maintain accurate and current participant records. The Department of Labor's guidance on missing participants makes clear that administrators are expected to take proactive steps to locate nonresponsive members rather than simply noting the gap.
The plans managing this well are not necessarily the ones with the largest budgets. They are the ones that have recognized data quality as a continuous operating discipline rather than an occasional remediation effort. That shift in perspective is where the real progress begins.