Solvency II Data Quality

Solvency II is just one of many regulatory programmes that require organisations to demonstrate that they have data quality management processes in place along their end to end data supply chain.

The horse meat in beef burgers scandal in 2013 provides an excellent analogy. To provide “trust” to food consumers, all organisations in the food industry (including supermarket chains) must know and manage their food supply chain. Similarly, to provide “trust” to data consumers (e.g. to regulators, senior Management, board members and shareholders), all organisations must know and manage their data supply chain.

After many years of preparation, Solvency II finally “became applicable” on Jan 1st 2016. The first reporting by companies will take place in April 2016.

Solvency II Timeline

Solvency Timeline – published by EIOPA

On 12th Feb 2016, the UK Prudential Regulatory Authority (PRA) published a “Data Review” of how 50 Insurance firms assess the quality of data that underpins the validity and integrity of one of their most important business processes, their Solvency II Internal Model”.

Regardless of the industry you are in, I recommend that you read this “Data Review“. It contains examples of good practice, but also many “war stories” showing where firms got it wrong. One recurring theme is the failure of firms to realise the dependency between upstream and downstream business processes, and the data flow between them. In other words, they don’t understand their data supply chain.

Would you like to better manage your data supply chain?  

Contact Me

Here are some of my posts on Solvency II Data Governance (Data Supply Chain Management) that you may find useful:

Solvency II Standards for Data Quality – common sense standards for all businesses

What does complete appropriate and accurate mean?

Solvency II mandates Data Governance

How do you collect your data?

Know your data

FSA SII progress review findings – More Data Governance required

Data Governance – Did you drop something?

What is your undertaking-wide common understanding of data quality?

Rgds Ken

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