Improving organizational data practices to leverage AI

Spring has finally arrived in many areas, and those with green thumbs are marking the changing seasons by prepping their gardens for flowers and vegetables. While seemingly unrelated, life insurance organizations can actually learn a lot from common gardening practices, especially when it comes to their data.

Just like good soil is the cornerstone of growing strong, healthy plants, data is the foundation of a life insurance organization. If you want beautiful flowers or fruitful crops, you can't just plant seeds in the ground and hope for the best, you need to actively care for them. That same notion applies to life insurers' data and technology investments. When an organization's data is properly maintained and readied, it has the potential to become a strategic asset that can be leveraged to provide new business insights, streamline processes, and deliver real-time, hyper-personalized digital experiences advisors and customers expect. It also means that organizations can effectively implement the latest technologies, such as artificial intelligence.

Recent research from Equisoft, UCT, and LIMRA shows that 78% of global life insurance carriers believe data readiness is the biggest challenge in getting value from AI, and 46% felt they aren't ready to implement AI given their current state of data readiness. Additionally, research from ACORD predicts that robust integration of AI capabilities can reduce life insurance industry expenses by as much as $300 billion. Given how important AI is expected to be in the industry, it's essential that life insurers remedy their data issues to be able to leverage it effectively.

For life insurers looking to apply gardening concepts to their data practices to make them more effective for setting the technological foundation of their organization, here are a few things to consider.

Focus on data controls

Data controls are systematic processes, policies and technologies designed to ensure data quality, security, integrity and compliance throughout its lifecycle. Examples of common data controls include governance controls, quality controls, access controls and compliance controls.

If an insurer's data controls are not robust or consistently implemented, a number of risks arise—especially given the fact that life insurance data is often personal medical and financial information that needs to be protected. Possible risks include fraud, reputational damage, incorrect risk assessment and underwriting, inaccurate pricing or claims assessment, low customer satisfaction, and poor-quality decision-making.

Life insurers need to evaluate the effectiveness of their data controls to address data quality issues and inconsistencies. One way to do this is through AI-driven data quality automation, which will detect and correct data quality issues more effectively. These technologies can identify patterns and anomalies that manual processes might miss, ensuring higher accuracy and consistency.

Carriers can also consider using data lineage tracking tools that provide end-to-end visibility into the data lifecycle. Data lineage tools track data throughout its lifecycle, documenting its origin, transformations and usage. The tools have the ability to trace data from its origin, through transformations, to its final use. This enables the insurer to trace and validate information used in key business processes.

Another essential part of ensuring data quality and integrity through improved data controls is integrating rich metadata that includes detailed context, ownership and usage rules. With strong metadata, it'll be easier for carriers to successfully locate data and extract value from it.

Manage data governance

Consistently enforcing data governance policies, procedures and accountability is essential to improved data quality. By expanding and enhancing data stewardship programs to include clear roles and responsibilities, data stewards will be equipped with the tools and training they need to manage data assets effectively and enforce governance policies.

One of the biggest practical difficulties insurers have is ensuring consistent implementation and adoption of their governance standards across the enterprise. Establishing and reinforcing governance role responsibilities enables leaders to hold team members accountable for adhering to the data practices in the governance framework.

Another measure for carriers to consider is implementing and integrating automated data governance tools, such as automated policy enforcement, data cataloguing, and compliance monitoring across the broader data management ecosystem to ensure policies are adhered to and being appropriately monitored

Life insurers should also conduct regular audits of data governance practices to identify gaps and areas for improvement, make adjustments based on evolving business needs and regulatory requirements.

Comply with data standards

Data standards are an essential part to maintaining improved data quality. Enforcing strict adherence ensures data consistency and integrity across integrated data sets. These standards will also help identify and address inconsistencies and enable carriers to set a path forward to remedying them.

Carriers should also invest in real-time data infrastructure. This means that data is immediately accessible as soon as it's created or acquired, and it doesn't need to be modified or transformed before being integrated into the system. This ensures that all new data is available and appropriately structured for uses cases.

Foster continuous data improvement

One of the biggest mistakes a life insurance carrier can make when improving their organizational data is making improvements all at once. Instead, they should consider implementing a "Kaizen" data culture, which emphasizes implementing incremental changes that allow for organizations to adapt, evolve and adjust to changes.

Under a Kaizen culture, carriers can work to bolster data literacy, awareness, and accountability for everyone in the organization through organized trainings and workshops. Life insurers can also regularly review and revise data management practices, policies and procedures, remaining vigilant for gaps, biases and unknown factors. By establishing continuous data profiling and monitoring processes that provide real-time insights into data quality metrics, organizations can proactively address potential quality issues and quickly resolve them.

AI is the future so don't let your data hold you back

AI will soon be deeply integrated into all aspects of a life insurance organization. The technology has the potential to be as impactful to an enterprise as the internet and provide implementers with enduring benefits, but the data foundation on which it grows and thrives requires continual upkeep to maintain accuracy and ensure it adapts with an organization's needs.

With strong data controls, effective data governance, strict adherence to data standards, and continuous data improvement, life insurers can ensure their organizations are data-ready for implementing and reaping the benefits of AI and other innovative technologies.