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Data Analytics in Insurance

Data Analytics in Insurance
August 18, 2015

The insurance world is witnessing a significant change in the way business is being done. Amidst increasing globalized competition, strict risk and compliance requirements and the ever-changing regulatory landscape, insurance players also face the  daunting task of digitally connecting the customer to the products that are driven by their choices, needs and convenience.

Considering the customer to be the core driver of this change, insurance players are hindered by their inability to extract transparent customer information from old legacy infrastructures and this in turn has led to a growing realization that old solutions will not solve new business realities.   

Insurers need to realize that certain technologies like predictive analytics can help them drive their future state technology roadmap in a way that makes best use of available IT spend while driving early business benefit realization. Competitive advantage can only come from substantial insights, created by deploying technological transformations that  tackle the real-world threats. For this reason, investment in data and analytics is being considered increasingly indispensable to transform strategy into business value.

Financial Data Analytics for Insurance

The traditional process (as described in the picture above) of extracting value from data and analytics is fraught with many significant challenges. The leading roadblock in the initial stages is capturing reliable data. The presence of unstructured data across antiquated systems makes it difficult for insurers to create a single data view. It may be possible that by the time insurers integrate their applications, their own landscape will have changed leading to an ongoing and very expensive program of work to maintain a viable EDW. This step is the most time consuming and may prove to be the biggest barrier to derive value from this technology investment.

The key to a successful data strategy is not constructing coherent data systems, but having an understanding how the data strategy can be made operational effectively in the business. A strategy that can provide the business with the information it needs to improve and then monitor that improvement, can be delivered without the need for the perfect data base being delivered first. This evolutionary alternative approach can be adopted to leapfrog the challenges of traditional data consolidation.

In effect, a transformational data strategy can run on two separate agendas with short term analytics using existing data to improve the business while a strategic enterprise data warehouse is developed and deployed.

The real question therefore should be, what tools can be deployed to make the best use of existing information. We have proposed this approach along with a detailed solution in a whitepaper.