This RMS article was previously published in Property Casualty 360
The effective use of data is so important to every insurance business — especially as big data and analytics are seen as a “silver bullet” for transformation. But to get on this transformative journey, your approach to data in your business may have to change. The traditional view of data focuses mainly on data collection and storage: how to collect, store, access and arrange the data, with rules and procedures to achieve this.
There is a tendency to separate data from analytics. If you think of data analytics, the image may be of the hard-pressed team of analysts and IT specialists, working to tight deadlines, “mining the data” to deliver the core reports that the business needs.
If any of the above rings true, you may need to change your mindset. First, for data collection and storage, the cloud has revolutionized the way data is stored, accessed and managed, offering high capacity and high availability, all typically on a pay-as-you-use basis. Historically, this is where much of the investment in this area went. But with the cloud, the burden has lifted as businesses now do not need to become experts in data storage or to plan, build and manage data centers, which were seen as critical in-house infrastructure in the past.
Moving on From Data Storage
The cloud also offers the gateway to a world of data analytics applications. This lies at the heart of the transformational change, the need to have impactful business insights. But for many, little investment or attention is given to using this data to drive insights. As a result, organizations suffer from DRIP, that is, they are data rich, but insight poor. Insight still mainly lies on your analysts’ desks, but to make the desired impact with analytics, manual processes and “special analyses” need to be removed. Insights need to be delivered automatically to the right employees at the right time, providing actionable insight so they can make better decisions.
Admittedly, many businesses have not had an easy data journey. Similar to data storage, there are war stories that pervade every insurance business about the challenges in managing the data, how the transfer and processing of data was an arduous process, the failure of long data runs, and slow and intermittent data links. For insurance businesses, sometimes the approach to data management and analytics can get so bogged down in process that it lacks focus and a central purpose.
Learning From the Health Care Industry
Consider the health care sector. The data systems they use are designed around a central purpose, which is to deliver relevant information to each professional involved in caring for a patient, whether it is a surgeon, consultant, nurse or an administrator. To achieve their aim, data is shared with everyone who needs it, and the level of data and insight is appropriate for each professional. Typically using a single platform, everyone contributes in real-time to avoid the use of siloed or offline systems.
What is also different with health care is the lack of a data “back office”, which is common in the insurance sector. Analytics rules are predefined, and data processing is automated straight through from back to front office. This is built into the system design, delivering critical information to frontline staff, and the output understands and anticipates what they need.
Four Things to Do With Data Systems
What can the insurance industry learn from health care? Here are four things the insurance industry can do with its own data.
1. Use systems designed with a central purpose to deliver the information your teams need.
2. Build around a single platform.
3. Define analytics rules that anticipate the information needed for each role.
4. Automate and deploy in the cloud.
Analysts still have a big role to play, but not for routine analytics, which should be automated and customized to frontline roles. Analysts should be free to do what they are best at: advanced analytics for the overall business.
Can the insurance industry move on from legacy worries about storage and shift the emphasis for data analytics away from analyst teams to automation? By embracing new enterprise platforms that combine the flexibility of the cloud with scalable storage and computational power and that orchestrate data to deliver analytics at the point of impact — the frontline — data moves from being a burden to being a business asset.