By Angus Black, Director at BarnOwl Data Solutions
The insurance industry is undergoing a profound transformation. With the rise of artificial intelligence, automation, and advanced analytics, insurers now have unprecedented access to powerful tools and vast volumes of data. But despite these advancements, one critical issue continues to hinder progress, the quality of the data itself.
No matter how sophisticated the technology, if the underlying data is fragmented, inconsistent, or inaccurate, the results will be unreliable and potentially damaging.
Despite the rise of advanced analytics and accessible AI tools, the core issue remains, technology alone cannot solve data quality challenges. In fact, poor-quality data, when fed into sophisticated tools, can amplify errors, distort insights, and lead to misguided decisions. This is particularly dangerous in a sector like insurance, where data-driven decisions impact underwriting, claims processing, reinsurance calculations, and compliance.
Why data quality is still a challenge – Most insurers still operate on fragmented systems, with data spread across various insurance management platforms, each with its own structure, standards, and inconsistencies. Add to this the human element of inconsistent data capture processes across teams, lack of standardisation, and differing interpretations of key metrics like premium calculations or policy values. It’s a perfect recipe for chaos if not managed carefully.
At BarnOwl, we tackle this challenge head-on by helping insurers centralise, conform, and clean their data, creating what we call “AI-ready data.” Our approach is designed not just to store data, but to identify and remediate issues through repeatable, governed processes. It’s not glamorous but it’s absolutely critical.
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Building a robust data pipeline – So where should insurers begin? The first step is to build a repeatable, reliable process to acquire and integrate data. That means ensuring daily and monthly data flows are automated, validated, and conformed into a standardised format.
From there, consistent data quality checks and remediation processes need to be embedded into the data pipeline. This includes creating rules for what data should look like, checking for anomalies, and, importantly, closing the feedback loop when issues are detected. These are not processes that can be fully automated, human insight is still crucial when identifying the root causes of data problems and resolving them.
Governance and the role of people – One of the most persistent challenges we see is the lack of standardised data definitions across business units. When different departments ask for the same data in different ways, it leads to conflicting results and internal confusion. A key solution is to establish a common language around data, with clearly defined roles and responsibilities.
Often, data governance is seen as an IT problem, but in truth, it must be a business-wide priority. Everyone, from underwriters to brokers to finance, has a stake in ensuring data is clean, consistent, and trustworthy. At BarnOwl, we’ve developed a standard data format and clear documentation that removes ambiguity and ensures every stakeholder understands what’s expected.
Making it accessible for smaller insurers and UMAs – We’re often asked whether smaller insurers and UMAs, with limited internal data teams or budgets, can realistically tackle these challenges. The answer is yes. Our solution is highly scalable and cost-effective, thanks to cloud-based infrastructure and a flexible service model. We’ve built integrations into over 30 insurance management systems and provide a “starter pack” of predefined data models and rules based on years of experience.
This approach allows smaller players to leapfrog traditional barriers, avoid hiring large in-house teams, and benefit from enterprise-grade data management from day one. Importantly, our partnership model ensures the knowledge and practices we implement begin to rub off internally, upskilling staff and embedding a data-first mindset across the business.
Avoiding the risks of automated poor decisions – Automation is a powerful tool, but only when used on clean, governed data. Otherwise, it’s the equivalent of driving blindfolded. Embedding data quality and business rules directly into your data pipelines ensures any downstream automation, whether for claims processing, financial reporting, or sales insights, operates on trusted, validated information.
This is crucial not only for operational excellence but also for regulatory compliance. With laws like POPIA in play, insurers must be confident their data is being used ethically and within legal boundaries. AI can help here too, by monitoring usage patterns and flagging anomalies or unauthorised access, providing both efficiency and peace of mind.
The road ahead: Innovation with guardrails
Looking ahead, I see a powerful convergence of AI, regulatory compliance, and ethical data usage. With the right investments in data infrastructure and policy, insurers can create defined datasets that are safe, auditable, and AI-ready.
This will unlock next-level analytics, real-time decision-making, and innovative product development, while ensuring everything stays within the ethical and legal “guardrails” of modern data governance.
At BarnOwl, it’s been rewarding to watch our clients mature in their data practices. Seeing them advocate internally for better data governance and quality, often using the very language and processes we helped implement, is a sign that meaningful, sustainable change is possible.
As the industry continues to digitise and innovate, one thing remains clear, the foundation of every smart, scalable, and compliant insurance system is high-quality data. And now, we have the tools, and the know-how, to make that a reality for every insurer, big or small.