Is there another industry that cares about data and metrics more. Perhaps Formula One?
The above was said by the Operations Director of a London Market Insurer at a recent Insurance Technology Forum breakfast briefing. And most of the room agreed. Data and information is critical to the operations of an insurance company.
But there are common pitfalls that insurance companies often fall foul of when trying to manage their data.
Here are our top 5 data management pitfalls in insurance:
- The British Empire Approach: Trying to govern the whole world – Trying to make ALL data streamlined and governed is a common banana skin. If you get information on a current flood area you need the capability to do some quick analysis on it. But there isn’t the time (or any reason) to get it into a tightly governed warehouse. Make sure you have something in your platform for quick and dirty analysis.
- Less is not always more – On the other end of the spectrum; not bringing enough of the correct data through to your data platform is a problem. Then the burden of data management passes onto the business when producing information for core processes such as reserving meetings and regulatory reporting. Data required for core business processes should sit within an automated and governed platform.
- What’s in a name? – Are there really 37 different Brokers called Willis? Or 24 different Aons? If you integrate data without going to the effort of cleansing that data then you’re helping no one. Senior Management may even miss the big picture. Include cleansing rules in your transforms.
- Keep calm and recycle – It’s great if you have your data warehouse supplying the actuaries with trusted governed data but once your IBNR, UPR etc. has been calculated it should be fed back into your data platform. If you’re not then you’re missing out on the true value of your platform . . . and probably relying too much on Excel.
- It only rolls downhill – The higher up the data chain you can fix a problem the more positive the impact on the decision maker. Problems in source are fixed in the data platform. Problems in the data platform are fixed in the semantic layer. Problems in the semantic layer are fixed in the reports. Data capture practices are generally poor in underwriting systems but fixing something at source rather than further down the chain saves a lot of unpicking later.
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