Sveta Hardak-Nissan, chief customer officer (CCO), Global P&C and Reinsurance Division, Sapiens examines the dark side of data.

There is a tsunami of data in today’s insurance and financial services landscape. More data is a good thing – more insights, more decision-making power, more possibilities.

But the dark side is that this data is often decentralised and disorganised.

As data scales and proliferates, it winds up scattered across a slew of disparate locations, stretching across legacy core systems, CRM platforms, partner feeds, and digital channels. The resultant silos of this decentralized data reality ultimately limit growth, slow decision-making, and weaken the customer experience. Without trusted data, agility becomes aspiration, AI becomes partial experimentation, and customer focus becomes fragmented.

The Right Approach

That’s why organsations are turning to a “one source of truth (OSoT)” approach, adopting platforms that enable a governed, trusted, and continuously updated data foundation. With its ability to unify data intentions and definitions even in wide-reaching environments, OSoT is becoming a business imperative that enables faster, better decision-making, meaningful AI adoption, regulatory confidence, and true customer-centricity.

The organisations that will lead the next decade of insurance are those that leverage OSoT to turn data into value.

The AI Imperative

Bain estimates that generative AI in insurance could unlock $100 billion in value in P&C claims, reducing loss-adjusting expenses by 20-25%, and leakage by up to 50%. But the single greatest barrier is not technology – it’s fragmented, inconsistent, and poorly governed data.

Disconnected data leads to conflicting business reports, misaligned decision-making, slow time-to-market releases, weak fraud detection and underwriting precision, and limited compliance transparency, all of which impede full AI  optimization.

An effective OSoT mitigates exactly this issue.

By ensuring clarity, consistency, and confidence across enterprise data, OSoT bolsters the most critical prerequisites for scalable automation and AI growth.

From Silos to Synergy

Many digital transformation programs stall not because of limited ambition, but because they rely on legacy data architecture. McKinsey cites outdated IT stacks as one of the primary barriers to financial service transformation. In insurance, this often appears as “spaghetti architecture” – rather than overhauling operations with up-to-date systems, fragmented platforms are layered on top of each other over decades, patching specific pain-points, but never bringing the whole system up to speed.

Overcoming this hinderance starts with boosting visibility:

Map all data sources – policy, claims, billing, telematics, partner ecosystems, and digital interactions.

Adopt flexible architecture like lakehouses, data fabric, or virtualization layers that were built for modern operations, enabling integration, without massive core rewrites.

Implement cloud-native Master Data Management to create real-time “golden records” for customers, policies, and assets.

The global enterprises from any industry that are most successfully embedding AI at scale have one thing in common: a unified, governed data foundation that is housed in modern data architecture.

Turning Data into Intelligence

According to AWS, nearly 70% of U.S. insurers already run gen AI in their operations, primarily aimed at bolstering employee productivity. This requires AI models with vast data-driven intelligence. But if the data used to give the AI its intelligence in the first place is unreliable, the “insights” won’t be insightful.

Intelligence without governance just creates risk.

With OSoT, insurers can ensure that the data training their AI tools can be trusted to power better underwriting decisions, faster and more accurate claims handling, stronger fraud detection, regulatory readiness, and personalized customer engagement.

Measurable Business Impact

The real value of OSoT appears when embedded solutions can turn insight into action in real time. With trusted analytic tools at their disposal, underwriters can assess portfolio exposure and risks in real time, claims leaders can detect loss trends early and trigger automated workflows, and marketing teams can more deeply personalize their customer profiles and communications.

Data that is centralized and governed with OSoT will also ensure that compliance functions meet regulatory standards across GDPR, Solvency II, and IFRS 17 – a critical attribute at a time when regulatory scrutiny is growing.

When this self-sustaining value loop is set in motion, it looks like this: Data → Insight → Action → Measurable Outcome.

Speed increases, automation expands, and competitive differentiation becomes sustainable.

Start Small, Scale with Purpose

OSoT is not built in a day, nor achieved with a single new platform. It must be grown deliberately.

While modern cloud data platforms such as Snowflake, Azure Synapse, and BigQuery can be trusted to provide the technical foundation for this transformation, success ultimately depends on business ability to prioritize the steps of their OSoT rollout and execute strategically. Here are four best practices:

1. Start with high-impact use cases, such as claims triage, underwriting scoring, or personalization
2. Prove value through ROI-driven pilots
3. Scale with clear governance and maturity models
4. Measure outcomes with clear-cut KPIs, like speed, accuracy, cost, and customer satisfaction

The key to consistent scalability is to prove value-add every step of the way.

One Source: The Secret Sauce

One source of truth will soon prove to be the strategic backbone of modern, AI-driven insurance. When data is centralized and governed by consistent guiding principles, it builds a trustworthy foundation that allows insurers to get the most out of their data and do right by their customers in turn.

Insurers can’t afford to cut corners when it comes to risk assessments and underwriting in the real world – why should they treat their data any different?