The Pensions Regulator (tPR) recently published the latest in their series of guides around record-keeping for trustees and scheme managers.
“A quick guide to measuring your data” specifically covers the calculation of data quality scores, but also includes advice on how each scheme’s “scheme-specific” data items – formerly referred to as “conditional” data items – should be defined and agreed.
It’s clearly a useful step for the pensions industry, so we asked our Head of Pensions, Garreth Hirons, for his take on the document.
“It’s the perfect time for tPR to lay out its expectations on data quality and give the industry a clear steer on their expected scoring system.
GMP equalisation is coming into view, DB consolidation is increasingly under consideration and there’s the possibility that IORP II’s rules on Annual Benefit Statement provision will bring data to members’ attention on a more regular basis.
My work on data-related projects across the industry has convinced me of the importance of establishing a universal measurement, and this document removes any lingering questions around the Regulator’s preferred approach.
To me, the most significant parts are the guidance on when to measure your data – at least once a year, which should serve to make data quality a more visible issue to trustees and administrators – and how to calculate data scores.
Whilst in the past I have seen schemes measure data quality on number of failures, both in total and per member, and by number of members with failures, it’s now clear that tPR favour the latter method.
In the case example given, separate percentage scores for common and scheme-specific data are given, and any member with even a single failure was counted against the score.
There’s also some welcome advice about the type of checks that should be done – presence, consistency and format, plus contextual checks at specific life and scheme events – and how scheme-specific data should be defined, with a clear focus on accord between trustees and administrators.
I’m pleased that tPR have decisively erased some question marks around these key processes, and it’s gratifying to see just how close this advice comes to Intellica’s recommended approach, and past applications thereof.
Taken in conjunction with the existing guidance on record-keeping and September 2017’s guide to data improvement, they have taken another step towards a clear and complete, cradle-to-grave framework for data quality measurement and improvement.”
