We See More - Infographic - The Risks of Poor Quality Data

Infographic - The Risks of Poor Quality Data

16 August 2018

We believe you can achieve amazing things with data. If you look at it differently and see what others can’t.

You can divine insight, spot dangers and make the most of opportunities. But you have to know what you’re doing. As the consequences of poor quality data can have a detrimental effect on a scheme’s ability to deliver accurate and efficient services to its members.

The key to unlocking the success of technology-driven initiatives is found in the quality of the underlying data. With the vast majority of data existing for several decades, it has undoubtedly ventured through different mediums, often evolving through periods of varying administration practices and being migrated through numerous systems with fundamental different designs. The operational risk of incorrect data can result in a significant strain on day-to-day administration functions, lead to errors and inconsistencies. 

Cost risk and reputational risk are also cause for concern. In fact, the root cause of most problems encountered by pension scheme administration departments can be traced back to poor data. 

Though the impact of these problems on pension schemes will vary from manageable localised issues to more expensive, higher profile ones. Take a look at our latest infographic to see the possible consequences of poor quality data may hold for your scheme: 



These issues and impacts are consequences of poor quality data. The existence of these problems will generate an immediate need to identify and correct the data issue, inevitably resulting in stretched resources and tight deadlines. Operational costs will increase and productivity will suffer. This is not a position any pension scheme should be left in.

Fraud Protection

In addition to the responsibility and importance Trustees have of maintaining good quality data for their members, it is also their responsibility to detect and mitigate fraud. Pensioner existence fraud is one of the most common types of fraud experienced by schemes, where benefits continue to be paid to relatives of deceased pensioners. Identity fraud is one of the faster-growing types of fraud in the UK and affects all aspects of an individual’s personal wealth. To reduce this risk, some due diligence checks against established third-party data sources can help quickly identify any suspicious records for more detailed investigation.

The Pensions Regulator (TPR)

TPR now has an eagle eye over data quality, as with regulations around Guaranteed Minimum Pension (GMP) and General Data Protection Regulation (GDPR), it is imperative schemes maintain their data and take it seriously. TPR said, “It’s disappointing that we are not seeing more schemes taking their duty to keep proper records more seriously. We’ve made clear what our expectations are and many schemes, across all scheme types, are not meeting them. By adding record-keeping measures to the scheme return, we will be able to target our interventions more specifically at those failing in their duties.”


Time is running out for schemes to take their data quality more seriously, and to put in place checks and measures to provide concrete proof of this. More importantly, an action plan must be produced with a clear achievable timescale for implementing any data-cleansing activities. The rewards will far exceed the effort invested.

*Information from this article was sourced in conjunction with the PLSA


If you would like more clarification on any of these points listed or guidance on how to begin your data cleanse then please speak to our team of experts today using the form below. We’d love to hear from you!