Successful Insurtech innovation depends on having access to all the data necessary for making and executing better decisions.
Take fire loss- this is not just a UK, European or US matter. Global data feeds give a global picture and when the time axis is added both trend analysis and potentially predictive analytics. Place/location underlies all this data and this is where Location Intelligence Partners will make a big difference.
Then there is being able to access, analyse and visualise all the data in the organisation including:-
- emails
- letters
- Freeform text in webforms
- Metadata in images
- Speech
Today only 10% to 30% of this internal data is actually accessible!
Near 100% of data access is vital for product planners, risk management and fraud teams- all need tools to investigate hunches, intuition and new ideas. Tools such as 360Retrieve are already making a big difference to insurance companies.
Over and above that you need purpose built analytics embedded in the enterprise apps to let all staff have the information to make and execute better decisions. Not just visual insights but the embedded so that users can see and act. This means write-back is an essential function.
BI software is traditionally poor at write-back however- see why.
And secure and manageable self-service analytics so that operational people can combine human intuition with corporate BI
Big Data, Bigger Data- really important but you also still need to master the detail of data management before extracting and exploiting the value.
Digitalisation and new technologies mean that far greater volumes of data are becoming available for evaluation within a much shorter time frame. Data analysis can be used to examine client portfolios to reveal trends, improve processes, optimise holdings, and provide targeted support to sales. The more global and comprehensive the data basis, the more valuable the data will be. The new dimensions of data and their analysis require some competences that not all insurance companies have. New competitors may be able to analyse data sets more quickly and apply the results in new applications – thus placing traditional insurers under pressure.
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