Insurers have more data than ever, but deriving maximum value from all of the information in their systems remains a challenge for insurance organizations. The best way to effectively become a data-driven organization is to develop an enterprise-wide data strategy.
But there are numerous challenges standing in the way of a successful data strategy. Carriers have to contend with legacy platforms and disparate systems that create data silos. They may not have the right talent on their team — or the time needed to upskill existing resources on developments in their field. And, perhaps the biggest hurdle of all, it can be difficult to cultivate the right culture and mindset around data if data initiatives are being led without any business leadership buy-in.
Successful data strategies were a key discussion topic at the recent Datos Insights roundtable I facilitated with Eric Weisburg at a Datos Insights CIO Roundtable during ITC Vegas. Insurance CIOs know they have to create a culture of data literacy and align their data initiatives with strategic business priorities to succeed. But figuring out how to tackle these large-scale projects is not a simple undertaking.
Succeeding With Insurance Data Strategies
When data is an afterthought, it can be difficult to get teams, especially those less familiar with the power of analytics, to change their processes and help drive business outcomes through modern data practices. Getting all of the right factors in place to become a truly data-led organization is difficult, but it is not impossible.
Developing a data-driven culture starts at the top. Internal teams may not see a need to change their processes or adopt new governance practices — after all, this is the way they have always done things, and they are working just fine. It is a leader’s job to showcase ways that adopting strong data practices can benefit their role and the organization overall.
Here are three best practices that insurer CIOs at the roundtable shared to help other insurance leaders prepare for the next phase of their data journey.
1. Ensure business buy-in at all levels of the organization.
The conversation around gaining business buy-in for IT initiatives often focuses on the essential nature of ensuring top-down leadership. There’s no doubt that buy-in from the top is critical when it comes to enabling data practices and setting organization-wide goals. But buy-in from users at all levels is vital, too. These are the people responsible for successfully adopting updated data practices and driving business value.
For instance, a specialty carrier whose underwriters are posting great results may assume there is no reason to change what it’s doing, but a property carrier showing poor underwriting results may assume a rate increase will fix the problem. Without examining the data, neither insurer will know for certain if they’re performing their best.
It’s important to seek out data champions across the enterprise. Show stakeholders at all levels how effectively leveraging data will complement their workflow and their role within the organization. Give them the room to experiment with different hypotheses that can be backed up by data.
Innovation cannot be driven solely from the top down; enabling teams to understand where data can help them do their jobs better to drive the business goals ensures that new ideas develop organically.
2. Invest in the right resources.
Nearly every organization is dealing with limited resources, but insurers need to invest in areas that can drive business value and create business process improvements. When it comes to data strategy, this means providing guard rails for teams to ensure the ideas taken into production are going to drive business value.
Some areas of investment are obvious, like modernization, self-service capabilities, and visualization tools. But areas such as data governance may not be evident. Having a data strategy plays a key role in identifying areas of investment and ensuring the right resources are allocated for it.
While it may feel urgent to invest in emerging technologies like generative AI, there should be clear analysis on how any technology will impact the business before it is adopted. A few years ago, a leading carrier had a few dozen data scientists devoted to exploring use cases for AI. But without any established goals or outcomes for this initiative, these data scientists were unable to produce any results.
In addition, having the right teams and roles in place to support data is vital. Business teams need a Chief Data Officer; data stewards, scientists, and analysts; data governance professionals; and reporting and visualization teams. IT teams need to include data architects, modelers, and engineers; report engineers; data management teams who ensure data quality and maintain the data catalog; and data operations teams.
3. Establish enterprise goals for your data practices — and measure them.
Without enterprise goals and KPIs for data literacy, adoption, and access, the business will have difficulty understanding what they need to do to leverage the data they have. Setting these organizational goals and methods of measurement will empower business teams to have the language they need to articulate what it is they are looking for, to understand how to adopt existing capabilities, and to access the data they want when and how they want.
Data governance is often perceived as an administrative task, but failing to meet regulatory compliance has serious business consequences. Helping the business understand the direct correlation between data governance and business outcomes enables business and IT teams to drive regulatory and compliance outcomes with confidence.
Additionally, demonstrating proof points across the organization is a good way to show how data governance improves the business. For instance, one insurer was running nine months behind when it came to its stat reporting. This delay meant that it couldn’t trust its own data, and the insurer was missing certifications and audits. Employing better governance measures can ensure that carriers are meeting compliance requirements and the data available to internal teams is accurate and reliable.
Having a forward-looking data strategy that is reviewed annually can help ensure that data roadmaps evolve with business needs while building the capabilities that will be needed in three to five years. Reviewing all elements of the data strategy regularly helps organizations know they are meeting their goals and measuring their success effectively.
Scaling Success With Data
Establishing roles and responsibilities for both the business and IT for data initiatives as well as upskilling talent to understand modern data platforms can help insurers’ teams develop effective data strategies that grow with their business. In addition, focus on the fundamentals of data: Without taking the time to prioritize data quality, operations, and governance, no data initiative can succeed.
Maturing a data and analytics organization to operate at scale requires collaboration, effective goal setting and measuring, and investing in the people and tools that can take an organization where it wants to go. Insurance leaders who are striving for a data-driven culture should employ the three best practices above to take their enterprise to the next level.
To learn more about how to drive business outcomes with successful data and analytics strategies, read our whitepaper Driving Business Value With Insurance Data Analytics.