The Data Dilemma
Invaluable intelligence lies trapped in the vast reams of data that every major risk carrier holds. Deploying those insights day to day, and across the company, would prove enormously valuable to their business, but getting it out in useful form is the challenge.
To mine this precious ore—a catalyst to enhanced profitability—they’ve typically adopted ever-more-complex data capabilities. These have arrived in the form of systems and processes. But awareness of data’s value is increasing across the organisation, which multiplies the demands on its intelligence-extraction systems and process, placing them under increasing pressure from multiple directions.
As they seek to mine their information reserves, re/insurers face questions which should be easy to answer, but often are not. Many of the questions are about the data itself, because the more you have, the more difficult it is to exploit:
- What data do we have, and where is it?
- How do I combine disparate data sets?
- When is data sufficient? When is it consistent?
- How can I validate and compare our data with industry information?
- How can my data be formatted to suit underwriting, actuarial, compliance, and everyone else?
Other, equally difficult questions reflect the evolving risk-trading environment:
- How can we profit from new e-trading platforms?
- Why can’t we meet new reporting obligations with our existing data capabilities?
- How can I plan effectively, given the rapid change in trading-partners’ IT systems and expectations?
- What can I do to minimise the complexity of multiple clouds?
Data volumes and complexity have multiplied. The technological environment is rapidly changing. The need to gain competitive advantage through processes is more pressing than ever. This has often created fragile systems landscapes within individual risk-carrying companies.
It is likely that good internal data flows exist. However, that’s a long way from mining the data to improve daily decisions across the piece. Very often the relationship between data sets is poor, which makes it very difficult to extract all the data relevant to a single issue.
Meanwhile external data, which could dramatically enhance the intelligence garnered, is often inaccessible. As with any new data set, connecting it to others is a difficult, sometimes intractable problem. Even when that’s achieved, it may be impossible to discriminate between good data and bad, since data in the system all looks equally valid, even when it isn’t.
System Challenges & Expenses
On the systems side, most re/insurers have already invested in good architecture and application integration initiatives. But that simply uncovers other challenges. For example, in many cases multiple initiatives are not adequately aligned, creating internal barriers down the road.
Systems often work well for some parts of the business, but simply can’t cope with other situations. For example, even the snappiest underwriting data tool may be completely inadequate for modelling or exposure management, let alone actuarial or compliance. To compound the challenges, the processes that evolve around systems are often expensive in terms of people and time, requiring highly skilled programmers or armies of entry clerks to deliver anything new.
Given all that, workflows and customised dataflows have often evolved into a rigid, fragile structure which is not only unfit for its multiple purposes, but also frustrating. The data is there, but transforming it daily, in any part of the business, from a mass of inaccessible fields into useful insights—the refined ore which can be used to make business gold—may seem an insurmountable task.
Insurance Data Infrastructure Deep-Dive
In our blog series “Creating Value Through Insurance Data Infrastructure” we will discuss how to extract the value from data to benefit your business. You won’t have to replace your existing systems, spend thousands of hours coding or rekeying data, or commit millions to a new data architecture. Please get in touch if you can’t wait and would like to learn more now, at email@example.com.