Welcome back to the second part of our three-part blog series, where we dive deeper into the Pension Dashboards Program Data Standards. In this part we’ll be exploring some of the mandatory data elements required for verifying an individual’s identity and matching to multiple pension pots. We’ll highlight some of the common problems which may lead to an increase in partial matches and poor member experience.

Having up-to-date data is a crucial aspect of a successful Pension Dashboards implementation. When the dashboards are live, having accurate and current data will significantly reduce the number of partial matches and in turn improve member experience and reduce operational overheads on administrators.

What is a partial match? A partial match is when a data provider (ie a pension provider or scheme) reviews the data they receive from the pensions dashboards ecosystem and thinks they may have a matching pension on their system without being sure. An example of this could be that the forename, surname and date of birth match but the address provided is different, potentially because the saver hadn’t updated their address details with the provider.

When partial matches occur, administrators will be required to invest time and resources to manually verify and resolve discrepancies. A partial match also increases the risk of matching a member to the wrong pot, which is not acting in the members best interest and will cause compliance challenges for the administrator.

By gaining a deeper understanding of the data elements and their associated definitions, administrators can proactively address data accuracy challenges, ensuring that partial matches are reduced and members receive accurate information when they access their Pension dashboards.

In total there are 87 data elements which are defined under the Data Standards and categorised as mandatory, conditional or optional.

In the following table we have focused on key personal information outlined in the standards and common problems impacting data quality.

Data ElementCommon ProblemsOptionality
Given NameAbbreviations, shortform names and mistakes.Mandatory
SurnameSpelling mistakes at the point of entry.  
Name changes due to marriage/divorce or change of circumstances.
DOBD.O.Bs captured in inconsistent formats.  
Missing or incomplete information.

Address Type (Current or Previous)
Over 10% of the population move address every year.  
Deferred members less likely to maintain accurate records.  
An address when an account was opened is likely to have changed
The same individual may have multiple pots associated to different addresses.  

Address Line 1
On average 20% of address data is incorrect or incomplete  
Addresses are stored in multiple differing formats
Provide all previous addresses over last 30 years.

Data degrades consistently over time due to changes in circumstances like getting married, moving home and changing address. Information at the point of capture will also be entered in an inconsistent format, with miss information or mistakes and incorrect due to human error. Both data degradation and incorrect information will result in increased partial matches when matching members to multiple pots as part of the Pension Dashboards rollout.

In an initial qualitative research study undertaken by PWP, this issue of degradation was highlighted, especially in the case of deferred members:

Some participants find that where there is a direct relationship with the individual, data quality is much higher. Others have challenges with individuals even being aware that they have a pension entitlement in the first place –which is particularly challenging when individual’s circumstances change and the data becomes out of date (e.g. address, surname or name changes, other contact details etc.)

MM. Are working with schemes, administrators and ISP’s to reduce partial matches and improve member experience by:

– Cleaning and standardising member addresses using royal mail PAF
– Confirming if a member is living as stated or gone-away from the given address
– Tracing members to new addresses and providing historical addresses associated with the member
– Identifying error and improving accuracy of member name or D.O.B

Speak to MM. now for a report on the quality, currency and completeness of member data.

Prepare and have confidence in your migration to the Pension Dashboards.