Global insurance organizations manage massive volumes of data across diverse markets, but leveraging this data effectively remains a significant challenge. Insurers rely on data to inform key business decisions throughout the insurance value chain — whether improving operations, enhancing products and customer experiences, or expanding their portfolios. However, data access and data governance remain major hurdles for insurers due to several factors, such as data quality and consistency, integration challenges, and even team training.
These challenges, in fact, likely point to why most Chief Data Officers at insurers feel their organization is still in the early to mid-stages of data analytics maturity.
Despite these challenges, when governed and leveraged correctly, data can be a powerful asset that drives business goals across the insurance value chain. From improving operational efficiency to personalizing products and enhancing customer experiences, effective data governance can help insurers unlock the full value of their data.
Data Analytics Challenges Insurers Face
Insurance companies face several obstacles when it comes to being able to effectively use the data they collect. For one, data is dispersed across multiple, disconnected systems, making it difficult to get a unified view of the business. Without proper governance, it’s difficult to guarantee data is accessible, usable, and trustworthy for decision-making.
On top of this, carriers are dealing with data in varied formats — structured, unstructured, and semi-structured — leading to complexities in data management and analysis. According to MIT Sloan, 80 to 90 percent of data is unstructured information.
Missing data, data silos, and legacy systems prevent teams from accessing crucial insights when they need them. In a survey by WBR Insight, an increase in data silos was identified as one of the top four technology challenges faced by the insurance industry today. And poor data quality can cost organizations as much as 15 to 25 percent of their total revenue.
Many insurers lack the analytical tools needed to address these issues, and even if they have the tools, teams may not have the skills to use them effectively. In fact, a survey of 50 insurance analytics leaders revealed that only about half felt their organizations had the talent required to fully capitalize on their data.
To simplify their data landscape and ensure governance at scale, insurers must build a compliant, interoperable data infrastructure that supports their global operations.
7 Steps to Building Your Data Governance Strategy
Here are seven key steps insurers should take to enact a scalable data governance strategy that enables them to harness their data’s full potential:
1. Build a Culture of IT-Business Collaboration
The first step to building a strong data governance strategy is enabling data as a strategic priority across the organization. This means setting up enterprise goals for data literacy, data access, and adopting data-driven practices.
Taking the right steps to build a data-driven culture helps teams across the organization understand why strong data practices are important and how to adopt them. Getting the whole enterprise on board helps enable grassroots innovation from within the whole company rather than relying solely on data analysts to drive growth in this area.
2. Establish the Right Policies and Procedures for Governance
Getting the organization on board with the importance of data is a necessary first step, but it is just as important to then ensure that the enterprise has well-defined rules and procedures around the quality, accessibility, and certification of data. This includes laying out data security rules from the very beginning of a data governance strategy.
Effective data governance starts with having rules in place about which roles can access which data as well as defining data certification rules for various regions of the data infrastructure. Business users need to be able to easily understand which level of data they are accessing. Aside from simply enabling access by role, department, and user level, organizations need to establish what data gets encrypted and whether that encryption needs to happen in motion or at rest.
3. Establish Data Stewardship
To ensure these policies and best practices are continuously followed, it’s important to assign a business owner to particular subsets of data. These data stewards should be responsible for the governing policies associated with their particular data category.
For instance, the data steward for customer data should be able to ensure that the necessary data is being captured for customers for any line of business. If the same customer has policies for multiple lines of business, it is the steward’s responsibility to ensure that all of that data is associated with the same individual policyholder.
4. Ensure Regulatory and Statutory Compliance
Insurance is a highly regulated industry, which means that data governance strategies must account for all of the applicable regulatory and statutory compliance requirements their policies need to adhere to.
For instance, if a carrier is required to report on its claim reserves on a quarterly basis, policies should be in place around how that claim reserve data is accessed, how it is structured for submission, what the submission process looks like, and so on. Building a process around this level of detail can ensure that the correct data is being pulled, processed, and analyzed in the right way every time.
5. Identify the Sponsors and Stakeholders
While having the right policies, stewards, and compliance considerations in place is crucial for data governance, it is just as important to have the right level of leadership, engagement, and sponsorship. A top-down approach to governance helps people in all roles across the organization understand the need for governance.
In addition, making it a part of organizational goals helps improve adherence to governance practices. Executive-level sponsorship improves stakeholder buy-in at all levels, which ensures that the organization receives critical feedback on how data is being used across departments. This feedback is vital for ensuring that data is complete, fresh, and relevant. While governance sponsorship can come from IT, stakeholder feedback should come from all over the business.
6. Build the Data Catalog
After establishing processes, stewards, and sponsorship for data governance, it’s time to create the data catalog. This means identifying and cataloging all of the data attributes that exist across the organization. Effective data catalogs require collaboration between IT and the business — IT cannot successfully achieve this alone.
On top of that, a data catalog is not a one-and-done activity; it is an iterative process. As the business evolves, it’s important to continue taking stock of the data involved to ensure there are enterprise-level definitions for data being collected and measured. If new lines of business are launched or new metrics for return on investment are adopted, the data catalog should reflect that.
7. Identify the Right Tools and Automation Frameworks for Governance Implementation
Once the plan is in place, the processes are established, and the people driving the strategy are identified and engaged, it’s time to select the right tool or tools to achieve the necessary capabilities for governance.
Different organizations have varying frameworks and tools in place to manage their data governance strategies. There are numerous automation frameworks insurers can leverage or build to improve their speed to market initiatives, and there are tools to help with data quality, data movement, and data governance. Selecting the right tools for the organization’s specific needs can help ensure governance processes run smoothly.
A Reliable Data Strategy
Effective data governance is crucial for global insurance organizations looking to harness the power of their data. By following these seven steps, insurers can simplify their data governance strategies and ensure their data is an asset — helping them stay compliant, improve operational efficiency, and make better business decisions.
Interested in learning more about data analytics in insurance? Watch our webinar Unlocking Opportunities – Data & Analytics in Insurance to explore the implementation and practical applications of data analytics.