Traditionally, insurance carriers and brokers have relied on their market position to help corporate clients make better insurance product decisions. This position is under existential threat from re-insurers and outside players (e.g., Big Tech). At the same time, new data sources like behavioral analytics, IoT, and non-traditional external datasets create opportunities and threats for traditional carriers, brokers, and re-insurers alike. There is no doubt that the insurance data ecosystem is changing rapidly. Carriers need a new market perspective and a plan to leverage emerging data strategies to monetize their data.

Data Monetization Strategy for Insurers

Traditional insurance carriers are sitting on a mountain of data. To extract maximum value from this data, they need to embrace innovative data monetization strategies. Insurers can advance their data monetization strategies using a 3-pronged approach: a data platform strategy to ensure that the right foundational data capabilities are in place, a product strategy to determine feasible product and service offerings, and finally, a carefully aligned go-to-market strategy.

Data Monetization: The Next Frontier for Insurance Firms

1. Data Platform Strategy

A data monetization strategy must start by examining the company’s data architecture. The data architecture governs data collection, management, consumption, and distribution throughout the organization. A paradigm shift in data management is underway, driven by knowledge graphs that enable thriving distributed data assets within an organization. Knowledge graph-centered architecture is becoming a central focus due to its advanced ability to make organizations GenAI-ready. These datasets must be safely combined and analyzed to maximize the monetization potential of data assets. This data architecture is foundational to artificial intelligence (AI) applications.

Another architectural consideration is the growing use of hyperscalers. These cloud-based solution providers offer evolving analytics and tooling, as well as scalable computing power that keeps up with ever-increasing data growth. Cloud technology allows companies to share real-time data at scale and further facilitates monetization through new models like subscription-based information-sharing platforms. Companies should ensure that their data architecture can address data quality with new data flow requirements across data platforms (cloud or non-cloud, hyperscaler, bespoke, etc.) or analytics partners. They should also address how the data foundations and knowledge graphs can help leverage machine learning, Gen AI, and analytics to enrich data assets.

2. Products Strategy

Companies should start by identifying which data assets can be leveraged for new products or services (with or without support from the ecosystem). To begin this strategy, think “new vs. better”. Can the new product or service improve existing offerings, is it a new product launch? For example, the new InsurTech ecosystem could provide a service option – exposing some data to junior partners through API layers for insights. These new entrants are experts in building behavioral analytics models and using new tech to drive value and opportunity. Partnerships with InsurTechs could also open new possibilities with generative AI or advanced ML modeling.

Insurers should consider the product evolution journey. Does the product have the potential to grow over time? How feasible is it, and what are the constraints? For example, an IoT data product can begin as a predictive maintenance solution for logistics companies. It can later evolve into an ecosystem-enriched vehicle uptime and customer assurance solution drawing on cross-party data from manufacturers, service providers, and consumers.

3. Go-to-Market Strategy

There is no such thing as a one-size-fits-all approach to monetization. The highest-performing companies begin with internal data monetization before selling data to third parties. When developing a market strategy, consider whether your organization will allow data analysis within your platform or provide third-party view-only access to your insights (for example, offering insights-as-a-service or product). Adopting the best go-to-market approach for data monetization depends on whether you sell a product or service, the in-depth expertise needed, and how to deliver data assets.

Such a wealth of service offerings from multiple players poses another decision for insurers: What will service delivery look like (data feed, advice, report, subscription, license, or one-time fees)?Insurers undertaking data monetization efforts should consider possible disruptions to core broker/client/carrier relationships. Large insurance brokers, for example, are setting up advisory consulting businesses to support existing clients, and some are providing crucial cybersecurity services to organizations. Some carriers and re-insurers are setting up AI modeling factories for internal and external data monetization, and ‘risk solutions’ lines of business are emerging. Partners may be able to tolerate some competition around data monetization, but at times, such competition may change the nature of the partnership. Given how the product/service portfolios of different constituents (brokers, insurers) are evolving, existing ways of working are no longer the same. Conflicts of interest, cannibalization, and other evolving dynamics may destabilize the current market relationship.

A Real-World Example

One of our global insurance brokering clients operating in 130+ countries wanted to re-imagine its data products. The company was experiencing market saturation, declining projections in existing product lines, and threats from new entrants experimenting with innovative data products. To meet changing client demands, the firm needed to re-position. How could the insurance giant re-imagine and monetize its data products portfolio to unlock new revenue lines for the business?

The three-pronged approach outlined above enabled the company to create a successful data monetization journey. Through AI-powered mapping solutions and graph technologies, the company was able to accelerate the linking of various independent data and intelligence products and rapidly unlock new monetization opportunities. The strategy helped the company identify approximately $100M in monetizable data product lines. The firm added direct-to-consumer selling channels and innovative commercial models in service line businesses.

New Data Strategies to Avoid Disruption

Carriers, brokers, and reinsurers have a significant and largely untapped market opportunity to create value through data monetization. The ability of any firm to realize this potential will depend on its capacity to understand client expectations (including the latent market for data assets), ensure that the foundational building blocks are in place, and define a clear execution path. For most insurance providers, this is as much an exercise in changing the market dynamics as it is an internal change management exercise.

Companies can successfully navigate new data monetization opportunities by addressing the three levers mentioned in the Data Monetization Strategy for Insurers section above. This strategy will lead to tailored, personalized experiences, services, and customer-centric products (including targeted marketing programs). Over time, this will enable companies to create a competitive edge and avoid forfeiting revenue and market positioning to traditional and new players.

About the Authors

Ajay Mangilal Jain

Vice President, Data, Analytics, and Intelligence
Ajay is an accomplished leader, passionate about helping enterprises achieve their digital transformation goals through cutting-edge data, analytics, and AI technologies. He provides strategic guidance to executives and manages client engagements in various industry sectors, including banking, financial services, insurance, manufacturing, and energy. Ajay is a thought leader with visionary ideas for organizations looking to leverage their data assets to optimize business operations, identify new revenue streams, and drive innovation.

Pankaj Gupta

Global Consulting Head, Capital Markets and Insurance
Pankaj brings three decades of experience across business transformation, corporate finance, and boardroom advisory primarily within BFSI. He has a proven track record of working with clients to shape and deliver transformation programs to enhance corporate topline, deliver cost and operating efficiencies, manage risk and compliance themes, and set up sustainable innovation models.

Tanusree Saha

Global Strategic Advisory Head, Data, Analytics, and Intelligence
Tanusree enables her clients to unlock value from data and intelligence and drive new business lines. She guides her clients through data monetization and experience shift transformation initiatives from vision to execution. Tanusree brings more than 20 years of cross-industry experience in consumer and retail, travel and transportation, and BFSI. She thrives at the intersection of art and science, creating the next new.