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Six steps to data quality

Transparency is key to Solvency II compliance

Six steps to data qualitySolvency II: For the better part of four years, we’ve watched as regulators zigzag closer and closer to finalizing this perplexing regulation. However with continued uncertainty on the implementation date, insurers are looking for immediate internal improvements to help prepare for Solvency II. Transparency is a common breakdown point for many organizations.

A lot of what Solvency II addresses is improving transparency across the firm, between the firm and its regulators, and with the customer. For instance, very often, the business has no continuous, organization-wide view of risk and struggles to clearly and consistently explain the source and reliability of its data. For example, if a business user changes the data, the process of transparently sharing information breaks down no matter how effective the IT control.

There is little to no traceability of data flows to reassure IT and provide automatic documentation. And data quality is not always embedded into data management processes, meaning there is limited monitoring and reporting of data quality. And even with access to reporting, there is no clear ownership of specific data or a process to resolve identified issues. Most organizations have minimal data governance, almost no focus on data quality and very little collaboration between IT and business units. This can reduce the firm’s view of its true exposure. 

Clear path to data quality

There are six data quality process steps that SAS recommends to support Solvency II initiatives, increase transparency and establish reliable data governance and monitoring. Run the steps iteratively (in a full cycle) and often. The steps are broken into three stages – plan, act and monitor.


Step 1 – Define the Solvency II business terms and define the data sources you will use.

Step 2 – Conduct data profiling to discover what your current data contains.


Step 3 – Design business rules for checking your data to ensure it is valid and complete.

Step 4 – Execute your business rules by embedding the services and rules they use into your operational systems and data integration processes. .


Step 5 – Measure and monitor the actual state of your data against what is expected and how it trends over time. Trigger tasks to improve your data as needed.

Step 6 – Make the required updates and improvements to your data, systems and processes to make things better.

There will be many resources at work on this process from both the IT and business side – oftentimes a recipe for stress and friction. To take advantage of the array of skills, knowledge and responsibility of each, understand where your barriers are so that all can work to remove them. This will ensure a seamless handover of responsibility and a more collaborative environment.

You can find more information about how data management impacts your organization and its Solvency II implementation in this free white paper: Download Data Management and Solvency II: A Critical Partnership.

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