Pensions Dashboards – Data Quality



One the key success criteria for the pensions dashboard initiative, is the ability of individuals to find their pensions. Without this being an effective and controlled process, there is a risk that individuals will not see all of their pension holdings and be left unaware of what they may be entitled to in retirement.

Each time a user attempts to find their pensions, all schemes or their agents (in the form of an integrated service provider (ISP)) will receive a structured request from the central digital architecture1 (as provided by Pensions Dashboards Programme (PDP)).

This request will contain specific information relating to the user, some of which has been verified against third party sources, such as credit registers, and some which has been provided directly by the individual themselves.

On receipt of request from the central digital architecture a scheme, or their agent, must search their member records to confirm whether they match the individual making the request. How matching is undertaken is covered in a separate note.

The ability to match an individual to their pension record is contingent upon the accuracy and comprehensiveness of the information stored by the scheme. Without prioritising the completeness and quality of data, we cannot guarantee the success of any matching criteria.

Finding ways to engage members and encourage them to provide updated data both on a one-off basis or as an ongoing principle has always been a challenge. People move, get married or have any number of life events, but remembering to tell their pension company is often a low priority.

Pension Find Request

As set out in our first briefing paper on Matching Criteria (DBBN2), schemes will be required to match data held on members to structured data provided in the Pension Find Request.

It should be remembered that every find request will be sent to all schemes, therefore there will be many that do not match against a scheme member.

The ability of the scheme to match the data against their data set will be reliant on the quality of the personal information held by the scheme or their administrator.

Data Quality

The success of any matching criteria is directly dependent on the quality of the personal information held for individuals by the scheme (or their administrators). There are many reasons why information may be incomplete or inaccurate for example:

  • Information was not provided when the member joined the scheme
  • The member has moved and not notified a change of address
  • The member has got married, divorced or changed their name for any other reason and not notified the scheme

It is vitally important therefore that all schemes review the data that they hold and undertake remedial steps to ensure that data is accurate.

This review should focus on two things:

  1. Is the data complete? – is information contained within all the data fields required for a match, and does it conform to the correct format.
  2. Is the data accurate? – after verifying the completeness of the information, schemes should ensure that the data is current and relevant.

When considering data completeness, the scheme needs to consider a number of elements:

  1. What format should the data be in?
  2. What is the impact if data is present but incomplete versus it not being there at all?
  3. Who provided the information in the first place?

When looking to complete data, the scheme should be mindful of taking shortcuts that will compromise accuracy.

It has long been known that where information is not known, the fields may be populated with any data just to enable the record to be saved. As such there is a risk that it is assumed that the fields are correct, when they have in fact been made up.

There are classic examples that follow a pattern simply to ensure that all data fields are complete and when reviewing both completeness and quality, should be taken into consideration. These include:

  • Use of a dummy date of birth such as 01/01/1900
  • Dates of birth in the future or too young to be valid
  • Dates of birth too old to be realistic e.g member would be >110
  • Use of dummy national insurance numbers such as (or variations of):
    o AB123456C
    o TN100556M
    o XX999999X
    o ‘unknown’
    o ‘N/A’
  • Addresses being:
    o Blank
    o ‘N/A’
    o Company or employer addresses
    o c/o addresses
  • Post Codes as:
    o ‘AB1 23B’
    o ‘XX1 1XX’
    o Blank
    o ‘N/A’
  • First names abbreviated to initial or blank or a ‘known as’ is used

While it is easy to recognise the use of these shortcuts, finding the correct data to undertake correction is far more challenging. The fact that the information was not available at inception means that it could be difficult to obtain now. The risk being that any member with an incomplete record may not be reachable with the limited information available to enable the completion of data.

We know that HMRC offers limited capability to validate national insurance numbers when someone commences employment, but that does not support later requests.
It is therefore challenging to complete a missing NINo unless it can be provided by the member themselves.

Completing addresses can be more successful with the use of data sources like voters roll or credit agencies but it is still incumbent on the scheme to ensure that they have the correct detail – it could be easy to get two John Smiths confused, for example.

The reality is that the majority of missing information needs to be gained directly from the member, and efforts should be made into finding and validating it with them through whatever tools are available. This will be discussed further in the section ‘Broadstone’s View’ below.

It should also be remembered that the Pensions Regulator places an obligation on trustees to regularly review scheme data and to provide a view of completeness in their annual report2. These obligations cover many of the data fields required for matching under pension find principles. Therefore, the need for data completeness has long been established. A scheme’s most recent Record Keeping review is a good place to start when considering data completeness (and, by extension, what matching criteria might be appropriate for that scheme).

Data accuracy

Of equal or greater importance than the completeness of data fields is ensuring that the information is accurate. Ensuring data accuracy is a far harder exercise to complete, given that all fields may be complete and meet the appropriate data structure, but remain inaccurate because the information is wrong.

There are some things that can be reviewed to provide a first simple view of data accuracy. These are covered in the section on data completeness, and are largely a sense check on the integrity of the fields:

  1. Dates of birth that do not make sense –
    a. 01/01/1900 is often used as a filler
    b. Dates of birth where the age is unrealistic e.g >110
  2. Addresses that show as n/a or not known
  3. National insurance numbers captured as AB123456C or using any of the HMRC forbidden characters

Beyond this there are a number of challenges that will impact data accuracy that would mean matching criteria will not be effective:

  1. Change of address – the member may, in fact, have moved several times since the address on record. Members often do not think of contacting their pension provider, particularly if they are not receiving regular communications.
  2. Change of name – members could change name for a number of reasons:
    a. Marriage
    b. Divorce
    c. Change via deed poll
  3. Variable spellings – some names can be spelled in different ways. This impacts both first names and surnames:
    First names
    a. John or Jon
    b. Mohammed or Muhammed
    c. Rosie or Rosy
    a. Smith or Smythe,
    b. O’Leary or OLeary,
    c. McDonald or Macdonald
    How the name is captured will have an impact on the way in which the matching process will work.
  4. Hyphens and Apostrophes
    a. Some names, particularly surnames, are hyphenated, however not all systems allow for correct or consistent hyphenation e.g Mary-Anne vs Maryanne or spaces before and after the hyphen.
    b. There are some names that include apostrophes in them (e.g. De’ath, Le’Roy, Le’Anne) – it is important that systems can allow for the capture of apostrophes.

While we can use some intelligence in the matching process to allow for some of the variances, it is still best to ensure that data is accurate and consistent. By being consistent, we have a greater chance of creating rules that can help matching e.g. always remove an apostrophe or always remove spaces before and after a hyphen.


Utilising third party agencies that can validate data will go a long way to help schemes improve the quality of their data. However, many of these services can simply highlight what is incorrect and not necessarily provide a proposal for correct data. Where that occurs, the scheme can contact the member to request the updated information. If it is the address that is incorrect, however, the scheme may not be in a position to contact the member and the issue is magnified.

As previously mentioned, finding ways to make sure that members notify schemes of changes has always been a challenge. There are potential routes that will help with the update of data:

  1. Provide online tools that engage the member.
  2. Collecting preferred communication methods from members.
  3. Utilise a service that can provide recommended data correction.

Online tools

Broadstone currently provide their Broadstone Engage solutions to service scheme activity. Alongside this, schemes can offer members access to our member facing version of the tool which provides members with access to their individual pension record. By encouraging members to engage with this service, it is more likely that they will remember to provide information at the time that changes occur. If supported by an appropriate communications exercise, prompting members to access Engage will also encourage them to provide the information needed and to
continually update their personal data as it changes.

Communication with members

Simply communicating with scheme members, active or deferred, using their preferred contact method, may have the effect of highlighting where we have some differences. People may not change personal emails as often as they change address or they may keep mobile phone numbers after other changes in their personal circumstances.

By using a considered communications approach, schemes may reach more members than if they relied on postal mail alone. If members are reached, they can be encouraged to confirm their personal details.

Third party services – data enrichment

As part of the service provided by Broadstone, we already use third party solutions to check some elements of personal information, but this service supports normal operations and not the need for a point in time data exercise.

Broadstone are engaging external partners that will enable schemes to choose an enhanced data matching service. These services will use data extrapolated across various data sources to potentially present alternative values for some of the personal elements. This does not remove the need to ask members to confirm their information but will provide more chance that the member can be contacted.

Broadstone’s View

The quality of member data has never been more important. Providing accurate client information to the matching process is imperative to best support individuals locate their pensions through pensions dashboards. If the personal data is inaccurate the ability to match is negated. Data accuracy will also be important for schemes when they get to the point of buy-out with an insurance company.

The Pensions Regulator is encouraging schemes to start focussing on data quality now and to not wait until nearer staging. Broadstone supports this view but recognises that for some schemes staging may not take place until late 2026. Consequently, any activity that is undertaken now, must also support an ongoing process to ensure accuracy is maintained.

Broadstone will provide a view of personal data for each scheme ahead of the date at which they will be required to connect to the ecosystem. The evidence from this view will help schemes determine the extent to which data cleansing is required. If they wish to do more than the core data review, Broadstone can assist by writing out to members (using preferred communication channels), providing an online portal for members to update data themselves, or provide access to third party solutions for data enrichment, and will advise the scheme on the cost of doing so.

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