Data Analytics: The “Buy vs. Build” Question
Insurers have long been familiar with the question whether it is better for them to “buy or build” their information technology; that is, whether it is better for them to develop their own proprietary in-house automation or utilize systems developed by outside vendors, with modifications. In the age of “Big Data,” the question of “buy vs. build” has moved into the realm of data analytics.
Where data was once relatively scarce and difficult to gather, the world is now flooded with private and public data, along with previously unimaginable power to organize and analyze that data. In light of the abundance of data and analytical resources, how important is it for insurance carriers to develop and maintain those resources in-house? To what extent can carriers effectively rely on third-party providers?
The answer will differ from carrier to carrier, of course, and probably among product lines within a carrier. To explore this topic of “buy vs. build” in carrier data analytics, we spoke with Risa Ryan, Head of Strategy and Analysis in the U.S. Reinsurance Division at Munich Re.
In the age of “Big Data,” how valuable is traditional insurance premium and loss data? To what extent can new forms of structured and unstructured be used in its place?
Risa Ryan: Premium and loss data are the underpinnings and fundamental to the insurance business, its strategy, pricing, and profitability. It’s everything we do and can’t be replaced; it can only be enhanced. Traditional data, the characteristics of policies beyond loss data and premium, are important as well. Traditional data, such as policy-level detail and demographic information, can be used for determining profitability and driving growth.
New forms of data, both internal and external to an organization, as well as both structured and unstructured, are important and can help enhance or possibly replace traditional data. Structured data sets may include census, wage, CPI, weather, and crime data. Unstructured data, such as claims file documentation and client visit write-ups may be a little more difficult to analyze and draw conclusions from, but are equally important.
What are the biggest advantages to developing and maintaining a storehouse of exclusive, proprietary data? What are the biggest advantages to having in-house data analysts and applications?
Risa Ryan: An organization that develops and maintains proprietary data can evaluate that data to optimize its risk appetite and risk portfolio. Developing and maintaining a storehouse of exclusive data can also play a role in helping the company identify any gaps either as organic growth in the current portfolio or in new products, and, thus, create new business opportunities. In-house data analysts and applications offer companies several strategic advantages. They can unlock insights about the business strategy and help steer the business to better enhance its market position. At Munich Re, we use our in-house resources for even more tailored solutions that better meet the needs of our clients, and positions us to create customized structures and products based on our experience with a particular client.
What are the biggest advantages to having in-house data analysts and applications?
- Flexible resource allocation – Analysts can be moved from one project to another depending on needs both internally and externally.
- Easy to manage maintenance, updates and next generation development – A strong connection between the analytics team and the business and its needs combined with familiarly with the data and systems makes updates and next generation development is easier.
What are the alternatives to making such an investment? Apart from the fixed costs, is there any danger that in-house data will become superfluous or irrelevant?
Risa Ryan: One size doesn’t fit all companies. So while building a data analytics capability can be the way to go for many companies, there is still a case to be made for using outside firms and tools to supplement internal capabilities when there is a need to get to the market quickly. In addition, external resources can also offer much a much needed fresh perspective and exposure to new data sets. Finally, firms need to consider the cost and time it takes to train people and get them up to speed to build and execute a data analytics capability to meet internal deadlines—when you purchase that capability externally, it’s up to vendor to deliver within your timeframe.
How much value does your organization place on its proprietary data? Is it a defining feature of your franchise, something you seek for its own sake, or a byproduct of your marketing and underwriting strategy?
Risa Ryan: Our data is our competitive advantage and the key to unlocking future growth and product development for our company and our clients. It factors in to how we price our business and the products we develop. It allows us to deliver the next generation of risk solutions to our clients.