From cost center to competitive asset
Transforming insurance data strategy
For decades, insurance companies have viewed data primarily as a compliance necessity—something to collect, store, and produce when regulators require it. This mindset has resulted in fragmented systems, siloed information, and missed opportunities. Today, leading insurers are fundamentally reimagining their approach to data, recognizing it as a strategic asset that can drive innovation, enhance customer experience, and create competitive differentiation.
A recent KPMG LLP survey of insurance industry executives reveals where insurers are focusing their data strategy efforts. Data governance leads at 65%, followed by data security and privacy (55%) and risk management and compliance (48%). Nearly half (45%) indicated that they’re focusing on support for AI or machine learning initiatives, while 43% prioritize data quality and integrity.
These priorities simultaneously reflect the challenges insurers face and the opportunities they’re pursuing. Data governance provides the foundation; without clear ownership, standards, and processes, data remains trapped in silos. Security and compliance remain table stakes in a heavily regulated industry. But the focus on AI support signals ambition beyond compliance. Insurers want data that powers innovation.
Insurers still view data as a cost center. They need to shift toward long-term strategic thinking for data and integrate [their] information
Manish Madhavani
US Financial Services Leader, KPMG LLP
The fragmentation challenge
The most significant barrier to effective data strategy is fragmentation. Underwriting systems, claims platforms, customer experience tools, and regulatory reporting systems often exist as separate, disconnected islands.
System and data fragmentation is more than a technical problem; it’s also a strategic issue. When data sits in silos, insurers can’t get a complete view of their customers, risks, or operations. An underwriter making a pricing decision lacks access to recent claims experience. A claims adjuster lacks visibility into a customer’s full relationship. Marketing can’t effectively target customers because data is incomplete or outdated.
Fragmentation also makes it difficult to deploy advanced analytics and AI. Machine learning models require large volumes of integrated data to train effectively. If that data exists in disconnected systems with incompatible formats, the effort required to prepare it for analysis can be prohibitive.
Finding the ‘happy medium’
Overcoming fragmentation requires a strategic framework that treats data as an enterprise asset rather than a departmental resource.
A “happy medium” approach often means starting with specific business domains where data integration can deliver clear value. Claims represents one such opportunity.
It’s where customers most directly experience the company’s value proposition, and where data from multiple sources must come together to process claims efficiently and accurately.
The strategic framework also requires rethinking data architecture. Cloud platforms offer a way to integrate information, consume large volumes of data, and deploy advanced analytics at scale. Data lakes and common data platforms can break down silos while maintaining appropriate security and governance. At the same time, organizations need clear data ownership, quality standards, and governance processes that ensure data remains reliable and trustworthy.
There's a happy medium between doing a big bang for the entire enterprise, which is unrealistic, versus doing something so small that it’s not impactful.
Raj Konduru
Consulting Sector Leader, Insurance, KPMG LLP
Where data strategy is delivering results
Despite the challenges, insurers are finding success by focusing data strategy efforts on specific high-value applications. Claims processing represents one of the most promising areas. When claims data is properly integrated and accessible, AI can quickly generate claim estimates, accelerating settlement and improving customer satisfaction.
Companies are succeeding in using data for claims—applying AI to quickly generate a claim estimate and settle that claim quickly. In addition to improving efficiency, this approach contributes to higher customer satisfaction. Companies are also using AI to detect fraud and stop fraudulent claim payments.
Regulatory compliance represents another area where data strategy delivers tangible value. When data is properly governed and integrated, insurers can meet obligations relating to HIPAA, GDPR, and other regulatory mandates more efficiently and with greater confidence.
Underwriting is also benefiting from improved data strategies. Insurers are using integrated data and advanced analytics to better price risks, especially around catastrophe and climate risks. By combining internal historical data with external sources and sophisticated modeling, insurers can select markets more strategically and set appropriate limits.
The customer-centric approach
Perhaps the most important shift in data strategy thinking is toward customer-centricity. Rather than organizing data around internal systems and processes, leading insurers identify customer touchpoints across the lifecycle and the data needed to serve customers effectively at each.
This outside-in perspective reveals opportunities that inside-out thinking misses. When you map the customer journey—from initial quote through policy changes to claims and renewal—you can identify where data gaps or integration failures create friction. By starting with customer experience and working backward to the required data, insurers can prioritize integration efforts based on business impact rather than technical convenience.
Transforming insurance data strategy
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From cost center to competitive asset: Transforming insurance data strategy
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