In today's rapidly evolving insurance industry, data is the lifeblood that fuels business growth.
Insurers must harness the power of data and analytics to inform decision-making, drive innovation and maximize competitiveness. Embarking on the journey toward data modernization is therefore essential to staying ahead -- or just keeping up -- and a well-defined data and analytics strategy is a critical first step.
See also: 6 Steps for Cultivating a Data Culture
Building the Foundation: Strategic Alignment
The strength of any data and analytics strategy depends on alignment with the overarching business strategy. Without such alignment, it can be challenging to secure funding and assemble the right resources to drive data modernization. Insurance companies must clearly demonstrate how prioritizing and investing data and analytics capabilities translates into tangible benefits for the organization and serves several key purposes:
- Drives profitability: The insurance industry is inherently data-centric, and optimizing data's potential to achieve business goals can provide a competitive edge. By ensuring a clear path to desired outcomes, insurers can identify areas that require investment, engendering confidence in the decision-making process. Furthermore, emerging trends can be identified early, enabling the company to stay ahead of the curve.
- Maximizes efficiencies: Often, insurance companies focus too much on accessing data and building data infrastructure and not enough on developing insights. Shifting to a results-oriented focus on actionable insights can dramatically improve efficiency by identifying resource-intensive processes, removing bottlenecks and embracing a source-of-truth philosophy to ensure data accuracy.
- Manages costs: The adoption of cloud infrastructure can help reduce run times, improve technology management and consistency and lead to substantial cost savings. Real-time cost monitoring provides transparency and allows for dynamic adjustments to resource allocation, enabling cost-efficient utilization.
- Increases innovation: By leveraging cutting-edge data and analytics techniques, insurers can develop new processes, enhance their capabilities and use novel data assets. Innovation should not be pursued for innovation's sake, but always directed toward the organization's business strategy and long-term goals.
Moving Forward: Next-Generation Data and Analytics
After establishing organizational alignment around strategy and securing corresponding investments into platforms, processes and people, insurers are equipped to modernize their data and analytics programs. This modernization includes three primary elements:
- Shorter analytics platform lifecycles: The modern data environment demands a shift in expectations regarding platform lifecycles. Instead of expecting systems to last for decades, organizations should plan and invest for three to five years to allow for agility and quick adaptation to emerging technologies. This requires developing clear definitions around the purpose and interaction points of these platforms, as well as driving business requirements for enhancements.
- Third-party data integration: The integration of third-party data is becoming increasingly critical. It is essential to create an environment for managing external datasets and connecting them with internal ecosystems. This includes addressing legal considerations and ensuring that data acquisition strategies benefit multiple functions within the organization.
- End-to-end business process integration: Above all, a data and analytics program needs to work, so ensuring interoperability between systems is vital. This involves driving platform uniformity, addressing delays in data flow, enhancing security and clarifying data ownership models across the entire data lifecycle.
See also: Why Becoming Data-Driven Is Crucial
Progressing Prudently: Data Management
Data management underpins the effectiveness of any data and analytics program. While embracing a new era of data-enabled products and processes, insurers must ensure they proceed prudently, giving due attention to data governance, ownership and ethics:
- Data governance: Effective governance is the cornerstone of data management and ensures compliance with internal, regulatory and contractual requirements. Using systems that enable data usage and lifecycle management is crucial for maintaining data quality and integrity and compliance.
- Data ownership: This should be approached strategically, starting with user access management and documentation of roles, responsibilities and data flows. In applying the enterprise governance and ownership model locally, mid-level managers play a vital role in translating enterprise expectations to make them more consumable for local users and in line with regulatory and contractual requirements.
- Model ethics: Ethical considerations should be foundational to any model. As data sources continue to expand and analytics become more advanced, it is paramount for insurers to develop and adhere to enterprise model ethics guidelines that are aligned with their values and goals.
Conclusion
For today's insurer, data modernization is no longer an option but a necessity. It starts with a well-crafted data and analytics strategy -- rooted in business objectives, driven by updated processes and systems and supported by sound data management. By constructing a well-defined strategy around these pillars, organizations can navigate the complex terrain of modernization and position themselves at the forefront of the industry's data revolution.
