The upcoming transition to the new pension system requires ensuring the quality of your data. Key account
manager Wilco de Vries provides 5 practical tips to enhance data quality and guarantee a seamless transition.
As the transition to the new pension system on January 1, 2028, approaches, pension funds and administrators are facing significant challenges. It’s concerning that pension fund administrators currently have low confidence in successfully navigating this transition, with one of their biggest challenges being data quality.
While big pension funds currently face data quality as a significant challenge, there's a silver lining. By adopting a proactive approach and addressing these issues, they can potentially avoid delays, additional expenses, and errors in pension payments. Recognizing these concerns, Wilco de Vries, key account manager at Human Inference, has outlined 5 practical tips to proactively address data quality issues well in advance of the new system's implementation.
Significant pension funds frequently encounter challenges with data quality, leading to errors and inconsistencies. Some funds rely on Excel sheets to manage their data, which exacerbates the problem. Besides Excel, other factors can affect pension data, potentially leading to inaccurate calculations and consequences.
Some small funds often rely on pension providers to handle specific processes. This involves working together and sharing data, which can pose additional risks to data quality. Therefore, pension providers need to conduct data inventory, standardize processes, and invest in technology. These measures require small funds depending on the administrator to guarantee data quality.
The data landscape is becoming increasingly complex, bringing privacy and security issues. Coupled with the resistance and costs within organizations, this creates additional complications for pension funds and administrators. With the new system’s introduction, solving data quality issues becomes more crucial than ever, as even one data problem is one too many.
Data quality is crucial for organizations that rely on data. Accurate and reliable data is valuable because it enables informed decision-making.
Take advantage of our free checklist to evaluate whether your organization fulfills the 6 critical data quality dimensions.
To address data quality challenges, pension funds and administrators can take specific actions to enhance their data quality and minimize additional challenges. Here are 5 practical tips to consider:
Start by inventorying all relevant data, such as participant information, financial records, investment data, and any deviations from the norm, such as sick leave and disability years. Then, critically evaluate the quality of this data to detect any errors and inconsistencies.
Agree on how to manage data. Standardize formats, terminology, and validation rules to ensure consistency and accuracy. This contributes to the quality of your data and complies with stricter data security legislation.
Clean and organize your data by correcting errors, removing duplicates, and filling in missing information. Also, enhance your data with information from external sources to improve accuracy and completeness.
Invest in modern systems and tools for working with your data, like data lakes, data governance platforms, and automated data quality checks. This will help you manage complex retirement data effectively and consistently monitor its quality.
Ensuring data quality is an ongoing process, not a one-time task. Therefore, closely monitor your data quality, conduct special checks, and adjust processes if necessary.
Investing proactively in data quality will ensure a smoother transition to the new pension system. With reliable data as the foundation, you can be confident that you will soon be able to pay out the correct pensions to your participants.
To assist pension funds and administrators in this endeavor, we provide tailored advice and the tools to organize and maintain their data. Contact us to explore how we can help you improve your data quality for a smooth transition to the new pension system.
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Poor data quality costs organizations hundreds of thousands of euros per year. Unreliable data leads to incorrect decisions and inefficient processes.
Fortunately, our data quality checklist allows you to assess in just 5 minutes whether your data meets the 6 data quality dimensions. Please leave your details to download the document instantly.