How Data Quality in Insurance Impacts Business Outcomes in 2025

Updated January 31st, 2025

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Ensuring data quality in insurance helps with better decision-making, reduces risks, improves customer experiences, and supports regulatory compliance.
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In this article, we’ll look into the importance of data quality in insurance, its benefits, challenges, and actionable steps to improve it.


Table of contents #

  1. What is data quality in insurance? 6 core pillars to consider
  2. Data quality in insurance: Business benefits across the value chain
  3. Five key considerations for ensuring data quality in insurance
  4. Improving data quality in insurance with five practical steps
  5. Summing up: The competitive edge of quality data
  6. Data quality in insurance: Related reads

What is data quality in insurance? 6 core pillars to consider #

Data quality in insurance refers to the accuracy, consistency, completeness, and reliability of data used for driving essential processes, such as underwriting, claims processing, risk assessment, and compliance.

You can have all of the fancy tools, but if your data quality is not good, you’re nowhere.” - Veda Bawo, Director of Data Governance at Raymond James

Consider an insurer processing a health insurance claim. If policyholder data is outdated or inaccurate—such as incorrect contact details, mismatched claim histories, or missing policy updates—claims processing can be delayed, leading to operational inefficiencies and frustrated customers.

Also, read → Data quality in modern platforms explained

To avoid such pitfalls, insurers must focus on six core pillars of data quality:

  1. Accuracy: Insurance data is free from errors, as even minor inaccuracies can cascade into significant financial liabilities. For example, In property insurance, accurate information about a building’s age, materials, and location ensures proper risk assessment, leading to fair pricing and faster claims processing.
  2. Completeness: Missing data can create blind spots in risk assessment and claims handling. In auto insurance, a thorough vehicle profile—covering mileage, accident history, and maintenance records—ensures more accurate risk evaluation, preventing underpricing and potential losses due to overlooked details.
  3. Consistency: Data consistency (format, context, ownership, etc.) across systems is crucial to avoid contradictions and inefficiencies. If a customer’s address differs across internal databases, miscommunications could delay policy renewals or claims processing.
  4. Timeliness: Insurance data must be up-to-date to support real-time decision-making and risk management. For example, in health insurance, outdated medical records can lead to incorrect premium calculations or claim rejections, frustrating customers and increasing operational inefficiencies.
  5. Uniqueness: Data uniqueness eliminates redundancies. For instance, a life insurance company with multiple records for the same policyholder may experience billing errors and redundant administrative efforts.
  6. Usability: Insurance data should be easy to interpret and accessible for those who need it. Claims adjusters, underwriters, and data analysts must be able to extract insights without relying on IT teams for constant support. This bridges the gap between information and action, empowering insurance companies to extract maximum value from their data assets.

Also, read → Data quality measures to know in 2025

Next, let’s see how high-quality data in insurance can drive efficiency, compliance, and customer trust across the value chain.


Data quality in insurance: Business benefits across the value chain #

Maintaining high-quality data in insurance provides several advantages across the insurance value chain, including:

  • Accelerated time-to-market for new products
  • Cost optimization through operational efficiency
  • Enhanced fraud detection and risk mitigation
  • Streamlined claims processing using automation and AI
  • Greater regulatory compliance
  • Improved customer engagement and retention

Accelerated time-to-market for new products #


High-quality, actionable data helps insurers to identify market trends, reduce product development cycles, and develop innovative products quickly. Munich Re, for instance, uses high-quality weather data to refine its catastrophe risk models, offering innovative insurance products tailored for extreme weather scenarios.

Cost optimization through operational efficiency #


Operational inefficiencies caused by data errors—such as duplicate records or manual corrections—can cost insurers millions annually. According to a Gartner report, poor data quality costs organizations an average of $12.9 million per year.

An insurer processing health claims might struggle with duplicate policyholder records due to inconsistent data entry across systems. If the same individual is listed under multiple records, claims processing teams may spend unnecessary time verifying details, leading to delays in reimbursements and customer dissatisfaction.

Standardizing data eliminates redundancies, speeds up reimbursements, and reduces manual work.

Enhanced fraud detection and risk mitigation #


According to the Coalition Against Insurance Fraud, fraudulent claims cost the US insurance industry approximately over $308 billion annually. While AI can help, leveraging AI models requires high-quality data. Clean, well-structured data enables AI to identify suspicious patterns more accurately, reducing fraud losses and strengthening risk management.

Also, read → 4 fundamental factors to consider for data readiness in AI

Streamlined claims processing using automation and AI #


According to a McKinsey article on reimagining insurance productivity, advances in technology—mostly involving AI—will automate nearly half the work done by claims workers. This will boost productivity, enhance customer experience, and improve loss frequency and severity by increasing claim accuracy.

Lemonade Insurance already uses AI bots to process claims in as little as two seconds, showcasing the potential of AI in minimizing errors and inefficiencies. Meanwhile, a Nordic insurance company automated claims processing by leveraging AI to handle unstructured data, resulting in increased operational efficiency and improved customer experience.

More effective and streamlined regulatory compliance #


The insurance industry’s regulatory environment, governed by frameworks like Solvency II, GDPR, and IFRS 17, demands transparency and precision.

For instance, IFRS 17 is an international financial reporting standard for insurance contracts. Compliance requires insurers to analyze data from actuarial systems, trading systems, claims administration, and accounting systems, to name a few.

Ensuring the availability of high-quality data across all of these systems is essential for effective IFRS 17 compliance. Inaccurate contract data or misclassified liabilities can distort profit calculations, leading to regulatory scrutiny and financial restatements

Improved customer engagement and retention #


Accurate and complete customer data ensures seamless claims processing, reducing frustrations that can drive policyholders away. A customer denied a claim due to administrative errors is unlikely to renew their policy or recommend the insurer.

By maintaining high-quality data, insurers can also proactively communicate with customers about policy renewals, discounts, and personalized offers, fostering loyalty and long-term relationships.

Using data from wearables, John Hancock’s “Vitality” program rewards policyholders for healthy behaviors, fostering deeper customer connections and improving retention rates.


Five key considerations for ensuring data quality in insurance #

To ensure effective data quality in insurance, you should:

  1. Integrate your data estate with proper data modeling: Insurers handle vast datasets and without a structured approach, inconsistencies emerge, making insights unreliable. Data modeling acts as a foundation, creating relationships between disparate datasets, enabling insurers to generate actionable insights. For instance, aligning CRM data with telematics enables more accurate underwriting and risk assessment.
  2. Ensure active metadata management for contextual insight: Active metadata transforms static documentation into dynamic, real-time insights about data usage, ownership, and lineage. In insurance, this offers clarity on each data asset, minimizing delays in decision-making and improving transparency.
  3. Formalize data contracts across teams: Data contracts ensure alignment on quality standards, timeliness, and schema integrity between producers and consumers. This reduces errors and improves accountability in downstream processes, like underwriting or claims processing.
  4. Automate data quality checks for scalability: The growing volume and complexity of data makes scaling data quality checks a challenge. Automated tools can help with data quality checks, catching errors before they impact critical processes.
  5. Maintain data consistency for multi-regional compliance: Insurers operating across regions must adhere to numerous frameworks like GDPR, HIPAA, and Solvency II. Standardizing data quality practices across jurisdictions ensures compliance, minimizes audit risks, and supports seamless regulatory reporting.

Improving data quality in insurance with five practical steps #

To improve the overall data quality of your data estate, you should:

  1. Define clear standards for all data assets using an insurance business glossary that documents the connections between data, definitions, and domains in your organization
  2. Set up a single source of truth – a one-stop shop for people to consolidate their data knowledge and create a living breathing repository of information
  3. Enable personalized access controls governing access of sensitive information, defined based on user roles, personas, projects, data domains, and more
  4. Track data flow across your data estate and perform root cause analysis with active, cross-system, column-level data lineage mapping
  5. Automate data validation checks in all workflows to eliminate manual errors, ensuring accurate decision-making
  6. Automate compliance management – audit trails, versioning, risk assessments, regulatory reporting
  7. Set up real-time alerts for flagging data anomalies and policy breaches as they happen

Let’s see these capabilities in action at CSE Insurance and Porto.

How CSE Insurance enhanced data quality and governance #


CSE Insurance, a California-based property and casualty insurance provider, faced significant challenges in managing and utilizing its data effectively, limiting their ability to ensure data quality and foster collaboration.

These included:

  • Data silos: CSE Insurance’s data was fragmented across various departments, making it difficult to create a unified view and access critical data in real time.
  • Mismatched metrics: Basic definitions and metrics were warped, creating a lot of ambiguity between different user groups.
  • Migrating documentation: Since CSE Insurance had numerous tagged workbooks, separated user sites, permissions, and updated descriptions, migrating data meant reconfiguring several aspects.

CSE Insurance adopted Atlan as their modern data workspace to address these issues and establish a scalable, efficient, and collaborative data ecosystem.

With Atlan, CSE Insurance set up a single source of truth for many different data users in half the time they expected, and without losing months of documentation. As a result, CSE Insurance witnessed the following business outcomes:

  • Improved data quality: Atlan’s Google-like interface empowered teams to discover shareable, comprehensive data profiles. This helped every user get access to high-quality, trustworthy data within minutes.
  • Reliable data governance: All data assets were migrated from Tableau to Atlan with appropriate tags, permissions, and context. The business glossary gave comprehensive definitions, descriptions, and calculations required for data users to do their jobs.
  • Increased trust in data: Data users could trace lineage to understand data flow, assess data quality, and make sense of all of their data with accuracy.

Aside from the fact that [Atlan] allows us to do our data management properly, with our definitions and glossary, it’s actually a really nice way to collaborate.” - Larisa Gorokhova, Director of Business Intelligence

How Porto transformed its approach to data quality #


Porto, a leading Brazilian insurance provider, wanted to improve data quality and governance across its data ecosystem. Data quality issues impacted decision-making and posed compliance risks with LGPD, Brazil’s data protection law.

The challenges faced included:

  • Fragmented data systems: Porto has roughly 14,000 employees, and has been in operation since 1945, leading to siloed infrastructure and knowledge. Data scientists had to scour the organization looking for subject matter experts who could answer their questions.
  • Manual documentation: Manual processes are inefficient, error-prone, and tough to scale. Porto’s data estate had over 1 million data assets and defining asset owners, enriching data assets, or securing sensitive data manually was daunting.
  • Compliance risks: Non-compliance with Brazil’s LGPD, a 65-article regulation on data privacy, could expose Porto to lawsuits and consumer backlash. Conducting stringent internal audits to ensure that their data is identified, properly tagged, secured, and masked was essential for regulatory compliance.

To address these challenges, Porto wanted something that went beyond a catalog. They selected Atlan to democratize knowledge across all business units in their organization.

[We] envision a hub of business knowledge where any employee can ask any question they might have about Porto’s business. Every detail an employee needs about concepts from Churn Rate to Lifetime Customer Value would be explorable and explained plainly, without the need to ask a single question of a colleague.” - Danrlei Alves, Senior Data Governance Analyst at Porto

With Atlan, Porto witnessed the following business outcomes:

  • DIY data discovery for better decisions: Porto’s data consumers can locate all available data, and the context around it, in a simple search. This data is accurate, relevant, and continuously updated, thereby supporting and speeding up decision-making.
  • Automated metadata management, ownership, documentation for better governance: Atlan Playbooks streamlined processes like data ownership assignments and metadata documentation, significantly improving the consistency and reliability of data. The overall efficiency gain because of automation is 40%.
  • Automated compliance for better data security, privacy, and integrity: Automatically tagging PII data has saved Porto tons of hours. Moreover, sensitive information such as names, email addresses, and account numbers have strict access controls in Atlan, and are properly masked in upstream systems.
  • End-to-end lineage for greater cost optimization: Atlan has driven remarkable visibility across their data estate – data warehouses and lakes, all upstream and downstream systems. Using lineage, Porto’s team can find unused and redundant data assets. Deprecating unused assets and pipelines has helped in improving data quality while cutting costs.

Summing up: The competitive edge of quality data #

Data quality is a strategic differentiator in insurance, driving efficiency, compliance, and customer satisfaction. By leveraging modern solutions like Atlan and adopting strong governance practices, insurers can improve operational efficiency, cut costs, ensure seamless regulatory compliance, and improve customer engagement.

Start by auditing existing data practices, closing gaps, and implementing an AI-powered data platform for improved data quality in insurance.



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