Defined benefit (DB) pension schemes should make sure their data is up to date and fit for purpose, Hymans Robertson warned, as insurers may refuse to quote or provide pricing for buy-in and buyout if it is not.

Hymans Robertson recommended a five-stage approach that schemes should follow to ensure their data is fit for purpose, including data audit, cleansing, and ongoing management.

The consultancy said that having a high-quality data standard in place as early as possible can benefit administrative processes, reduce risk, enhance member experience, and ensure alignment with compliance frameworks from bodies like the Pensions Standards Administration Association (PASA).

In addition to this, Hymans Roberston suggested that this is “key” for the upcoming pension dashboards that will impose new levels of expectations on scheme data quality and currency.

“Every DB pension scheme will have its own journey but taking a holistic approach to data improvement and working towards an ‘accurate all the time’ data set will benefit the scheme’s progression towards their chosen endgame,” Hymans Robertson head of digital strategy, Scott Finnie said.

“This will allow for a smoother transition and increased flexibility as all end game options can be explored with the knowledge that the data is accurate and correct.

“Our five-stage approach, as outlined in our paper is key to getting data into this position and provides insights to ensure that data monitoring is seen to be an ongoing task for DB schemes as they continue their journey to endgame – which, for most, will include compliance with the pensions dashboards requirements along the way.”

In particular, the five-stage approach starts with a clear definition of the link between data and benefits, while stage two looks at auditing the data at a regular interval.

The third stage is a data improvement plan which aims to ensure that executing the plan, which is stage four, will be straightforward.

Hymans stated that it was “important” that regular updates are provided to stakeholders and progress is maintained during the fourth stage, ensuring that the data project momentum continues.

The final stage the consultancy set out in the paper was to maintain ongoing monitoring and see data as a key integral part of a scheme rather than a one-off exercise.

Finne added: “Ensuring your data is good quality might sound simple, but we know that the reality isn’t always as so.

“Having correct data benefits DB schemes in several important ways.

“It cuts administrative processes, reduces risk and enhances the member experience – all of which are invaluable to the smoothness of a scheme’s endgame journey.”

He explained that for those on a buyout journey, however, data quality was an increasingly “pivotal” consideration for insurers when considering whether to quote for a transaction and the pricing they offer.

“Investing in a considered, holistic data improvement plan will make the scheme more attractive to market – as well as offering reduced risk, dashboards compliance, and improved member experience along the way,” he concluded.

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