How Insurers Can Monetize Data Through Innovative Strategies
Techwave Solutions
Building greater futures through innovation and collective knowledge
Traditionally, insurance carriers and brokers have relied on their dominant market position to guide corporate clients in making better insurance product decisions. However, this position is now under threat from re-insurers and new players, including Big Tech. At the same time, emerging data sources such as behavioral analytics, IoT, and non-traditional external datasets are presenting both opportunities and challenges for traditional carriers, brokers, and re-insurers. The insurance data ecosystem is evolving rapidly, and carriers must adopt a fresh market perspective and develop a plan to leverage emerging data strategies to monetize their data effectively.
Data Monetization Strategy for Insurers
Insurance companies are sitting on a wealth of valuable data. To unlock its full potential, insurers must adopt innovative data monetization strategies. At Techwave Solutions, we recommend a three-pronged approach for advancing data monetization strategies:
Data Platform Strategy
The foundation of any data monetization strategy begins with assessing the company’s data architecture, which governs the collection, management, consumption, and distribution of data. A significant shift in data management is occurring with the rise of knowledge graphs, which allow distributed data assets to thrive across the organization. Knowledge graph-centered architectures are becoming crucial for preparing organizations for next-generation AI applications. To maximize the monetization potential of data, these datasets must be securely combined and analyzed, which is critical for enabling artificial intelligence (AI) applications.
Another important aspect of the data architecture is the growing adoption of hyperscalers—cloud-based platforms that provide scalable computing power and advanced analytics capabilities. Cloud technology enables companies to share real-time data at scale and facilitates monetization through new models like subscription-based information-sharing platforms. Companies should ensure that their data architecture is capable of handling data quality requirements, whether on cloud platforms, hyperscalers, or custom-built solutions. Additionally, the data foundations should be optimized to leverage machine learning, generative AI, and analytics to enhance data assets.
Product Strategy
The next step is identifying which data assets can be transformed into new products or services, either independently or in partnership with the broader ecosystem. Companies need to think about the evolution of these products—are they designed to improve existing offerings, or will they introduce entirely new solutions? For instance, partnering with InsurTech firms could enable insurers to utilize behavioral analytics models and advanced technology, adding value to their offerings.
Insurers should also consider the long-term potential of their products. For example, an IoT-based product might start as a predictive maintenance solution for logistics companies and later evolve into a more comprehensive vehicle uptime and customer assurance platform, enriched by data from multiple ecosystem partners like manufacturers and service providers.
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Go-to-Market Strategy
There is no single approach to data monetization that fits all companies. Leading firms typically start by monetizing data internally before selling it to external third parties. When developing a go-to-market strategy, insurers must decide whether to allow external access to their data analysis platforms or to provide view-only access to their insights, offering insights as a service. The strategy chosen will depend on whether the company is selling a product or service and how deeply they need to integrate expertise and deliver data assets.
Insurers also need to decide how to deliver their services—whether through data feeds, reports, subscription models, licensing, or one-time fees. It’s important to consider how these offerings will impact core broker-client relationships. Some large brokers, for example, are establishing advisory consulting services to provide clients with enhanced value. Others are offering cybersecurity services or setting up AI modeling factories to generate new revenue from both internal and external data.
Real-World Example
One of Techwave Solutions' global insurance clients, operating in over 13 countries, sought to reimagine its data products. Facing market saturation and competitive threats from new entrants experimenting with innovative data strategies, the company needed to reposition itself to meet evolving client demands.
Using the three-pronged approach discussed above, Techwave Solutions helped the company embark on a successful data monetization journey. By leveraging AI-powered mapping solutions and graph technologies, the company was able to link various independent data and intelligence products, rapidly unlocking new monetization opportunities.
New Data Strategies to Avoid Disruption
The insurance industry is at a pivotal moment where the potential for data monetization is vast but largely untapped. For carriers, brokers, and reinsurers, the ability to capitalize on this opportunity will depend on their understanding of client expectations, their readiness to build foundational data structures, and the clarity of their execution strategies.
Successfully navigating data monetization requires careful attention to the three levers mentioned earlier—data platform, product, and go-to-market strategies. This comprehensive approach will enable companies to offer tailored, personalized experiences and customer-centric products, resulting in a competitive edge and the ability to fend off new and traditional competitors.
In summary, data monetization is the next frontier for insurance firms. Techwave Solutions is committed to helping insurance companies unlock new revenue streams by leveraging advanced data strategies and innovative technologies, driving the future of the insurance industry into a more data-driven era.