Life insurance is a complicated business. Large insurers offer a wide variety of products and services designed to lower risk. These one-stop providers offer tailored solutions or traditional options such as life insurance, annuities, 401(k) plans and other employee benefits.
For better customer service, insurers provide customers access to all account information, investment portfolio as well as the ability to pay premiums at any time. However, relying on these self-service tools requires accurate and reliable data quality powered by data governance which helps insurers to consume and process large amounts of third-party information from various sources like custodial banks, asset managers and data vendors.
Insurers must verify the quality and accuracy of all external data sources to ensure customers can update their own profiles, file claims and manage their policies. With vast amounts of complex structured, semi-structured and unstructured data coming in from third-party sources every day, an integrated approach to data quality and data governance is a necessity. This is especially critical when most of these newer applications reside both on premises and in the cloud.
Many insurers depend on manual processes to establish enterprise-wide data quality. However, these processes and systems are susceptible to human error and drain productivity. When errors occur, they take a long time to fix, resulting in annoyed customers and a negative user experience.
Given the speed and scope of data today, significant data integrity challenges can strain manual processes. Insurers are making an effort to analyze information pulled from external sources. Yet, the same users can mistakenly pull data from the wrong source, miss critical data or duplicate data in error. Data quality issues compound when data inconsistencies exist in multiple places across different systems, even cloud applications.
Customer insurance portals require insurers to refresh data regularly and in real time. Manual processes not only jeopardize results for a single-use case but negatively impact the customer experience, revenue and long-term ROI. When data errors occur, insurers need a consistent process for manipulating and resolving errors to achieve the desired outcome.
Instead, insurers need a robust data-quality powered data governance strategy that scales across the enterprise and utilizes automated tools to eliminate manual processes.
A modern data intelligence platform includes business-friendly capabilities and enables a self-service approach for business and IT. With a simple, visual interface, a scalable data intelligence platform allows users to score and measure data integrity while monitoring data transformations and aggregations. Additionally, the platform should include centralized, collaborative interfaces and workflows to provide unprecedented visibility into data quality and data assets.
The great news for insurers is that they can realize several benefits of data quality powered data governance and bring clarity into their data landscape with an integrated platform that can help insurers:
For insurers to remain competitive, they must provide the highest level of customer service. Automating processes with a data intelligence platform helps easily leverage data assets for analytical insights. Subsequently, insurers can meet revenue, customer experience and regulatory goals, all while building confidence in their data quality.
Are you ready to take advantage of data quality powered data governance in the insurance industry? Download our case study, Optimizing Insurance Business Processes with Quality-Powered Data Governance.
Are you looking for additional information about the benefits of integrating data quality and data governance? Read this article from Emily Washington, our EVP of product management, Cultural Change Through Integrated Data Governance and Data Quality.