What if your multi-year data roadmap is the problem, not the solution?
Most insurers have invested heavily in data maturity. Actuarial reserving, statistical modeling and portfolio analytics are embedded disciplines across the value chain. Data strategies and roadmaps are in place. Yet one challenge cuts across almost every program: speed to value.
Despite years of investment, many insurers still struggle to turn strategy into accessible, high-quality data that changes how people work day‑to‑day and demonstrably moves the needle on financial outcomes.
At Thoughtworks, we see speed to value as the principal success criteria for data modernization, not just a nice-to-have at the end of a multi-year roadmap. We employ the practices of evolutionary architecture and product thinking to deliver data platforms and products that can change as fast as the business does, without repeatedly starting again.
We've combined the latest advances in AI, our pioneering work on data mesh and years of delivering scaled data modernization programs in insurance and adjacent sectors to attack the speed-to-value problem directly. We now deliver working data products, tested against real data, in days rather than months or years.
In this article, we explore why data modernization often stalls in insurance and how a pragmatic use of AI, coupled with a rigorous approach to modern architecture and delivery, can radically accelerate your data strategy.
Why data modernization so often fails
Enterprises generally invest in data modernization to achieve three outcomes: accelerating access to valuable data, increasing the quality of that data and reducing operating costs. Unfortunately, many organizations fail to achieve these benefits. Producing data takes too long, data quality remains low and costs continue to rise.
Some of the most common reasons for these failures are:
Modernizing technology without modernizing practices
Modern, cloud-based data platforms have become incredibly sophisticated tools for empowering your data engineering teams. But effectively exploiting the benefits of these platforms requires adopting modern engineering practices and establishing products and platform teams to reduce dependencies. Data modernization strategies that do not address critical changes to practices and processes will not achieve the desired outcomes.
Not aligning your data strategy to meaningful outcomes
An effective data strategy must provide a clear architecture and roadmap for modernization, but too often data strategies focus exclusively on architecture and implementation details, without clearly articulating how those investments will align to the business outcomes driving the modernization effort. Failing to map specific activities to accelerating time to value, improving data quality or enabling AI will generally result in those objectives not being met.
Adopting a "build it and they will come" mindset
The "build it and they will come" approach describes the strategy of proactively building enterprise solutions in anticipation of future demand from your user community. This strategy presents multiple risks; from failing to address urgent needs while waiting for the platform to be built, to realizing that user needs were not accurately anticipated. The most successful modernization strategies incrementally deliver value to the business and rely on user feedback to drive the prioritization of assets.
Achieving success through product thinking, agile engineering and adapting for AI
These common challenges can largely be addressed by:
Adopting a product mindset
Over the past six years, the data mesh principle of treating data as a product has become increasingly popular. Identifying the types of data consumers within your organization and developing fit-for-purpose data products enable data engineering teams to better align to business requirements and achieve success.
Apply these same principles when formulating your data modernization strategy to ensure it reflects both the drivers and expected outcomes. Key steps include:
Identifying data consumer personas and understanding their consumer journeys.
Applying FAIR standards to increase usability.
Articulating the expected ROI of data investments.
Embracing modern agile engineering practices
Achieving the greatest return on your investment in a modern, cloud-based platform requires developing new technical capabilities and adopting new engineering practices. A successful data modernization strategy must enable your data engineering team to quickly deliver high quality, reusable data products at scale by:
Employing DevOps and DataOps practices that maximize the benefits of modern, cloud-based platforms.
Optimizing delivery through cross-functional product teams aligned to business domains.
Enabling independent product teams to deliver interoperable, consistent assets through a shared data delivery infrastructure.
Shifting to AI-ready data
Any data modernization effort must take into account the growing demands of AI. AI solutions and agentic architectures increase data governance and management requirements:
Enabling the retrieval and integration of structured and unstructured data.
Expanding metadata requirements to better contextualize data for LLM consumption.
Ensuring that data governance policies remain intact and that AI governance issues are addressed as well.
Harnessing AI to accelerate speed to value
Leveraging the power of generative AI, we've created a set of tools that dramatically accelerate delivery of data products. Our data product workbench enables teams to quickly translate natural language business use cases into product design specifications and specifications into working code. With the data product workbench, we can take you from defining a use case to testing new data products in as little as one week. Our workbench also enables teams to dramatically reduce the effort to migrate existing assets from legacy data platforms.
Working with us on data modernization
Our combination of a product-based approach, agile engineering practices and our data product workbench provides unprecedented speed and automation, representing the definitive path to achieving continuous business value from your data strategy.
Want to see this in action? We invite you to a working session where we'll demonstrate our modernization accelerator, building insurance-specific data products in real-time and explore where you are on your modernization journey.
What you'll gain from the session:
Clear insight into your current roadblocks and organizational readiness.
A live demonstration of the AI accelerator building insurance-specific data products.
A concrete recommendation for your highest-impact first data product.