Why Data Governance is Essential for the Insurance Industry

The foundation of insurance is data and analytics. Actuaries and their mathematical models enable insurers to calculate risk to determine premiums. Today, the rise of digital insurance companies and the changing risk landscape together drive the industry’s digital transformation.

As the volume, veracity, variety, and volume of data expands, insurance companies need a stable framework to govern data and democratize access. Further, compliance regulations like the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) demand that organizations maintain data security and compliance. At the same time, there’s a growing opportunity to learn from customer data to deliver superior products and services. For these reasons, insurers are adopting data governance solutions for a range of use cases.

Understanding data governance and its crucial role in the insurance industry

Insurance companies collect large amounts of data every day. Auto insurers, for example, track everything from how many miles a customer travels to what type of car they drive. Insurers use this data to help determine whether a policyholder qualifies for discounts, offers, and coverage.

Data governance for insurers ensures that the data their company collects, stores, analyzes, and uses remains accurate and complete. This allows insurers to make better decisions about customer needs, products, and pricing.

With a clear data governance strategy in place, insurance companies can rely on the data underlying their models for enhanced decision-making and reduced risk.

The regulatory landscape for data governance in insurance

Insurance companies operate in an increasingly complex regulatory environment, with frameworks like GDPR, CCPA, and industry-specific regulations demanding robust data governance practices. Insurance organizations must also navigate requirements for data protection, retention policies, and reporting obligations while maintaining compliance with state-specific insurance laws. These regulations mandate strict controls over personal information, financial data, and claims history, making comprehensive data governance not just beneficial but essential for operational continuity and risk mitigation.

Understanding and adapting to prevailing regulations is essential for insurers, who must establish robust data governance policies that support both regulatory requirements and industry best practices, ultimately fostering greater trust with clients and stakeholders.

Key data governance challenges facing the insurance industry

The insurance sector grapples with unique data governance challenges, including the management of vast amounts of sensitive personal and health information across disparate legacy systems. Insurers must reconcile data from multiple sources, ensure consistency across different departments, and maintain data accuracy while processing millions of transactions. The challenge of maintaining data lineage, establishing clear ownership responsibilities, and implementing standardized data quality protocols is amplified by the industry's rapid digital transformation and the integration of third-party data sources.

The role of effective data management in enhancing insurance operations

Insurers with a robust data governance strategy can better understand their customers’ needs. A sharper understanding of customers can increase not only revenue, but customer satisfaction and lifelong loyalty. Also, by cataloging sensitive data protected by regulations, insurers reduce compliance risk. Finally, with data governance, insurance leaders can increase efficiencies across the business, saving time and money.

From a business perspective, insurers’ data management ensures data completeness and accuracy. For example, insurers must collect accurate demographic information, such as zip code, age, gender, marital status, and income level. In addition, insurers need to have fresh data, such as historical rates or claims experience. Accurate, fresh data ensure that insurers make informed decisions around pricing and new products.

In addition, the insurance industry also must comply with various data privacy and security regulations. To do this, they must know what personally identifiable information (PII) or electronic Protected Health Information (ePHI) they collect to implement the appropriate safeguards, such as flags that warn internal data users if data is private. Data governance enables them to do this more efficiently, streamlining processes and increasing employee productivity.

The consequences of poor data governance in the insurance sector

A weak or absent data governance strategy can manifest in a number of ways. For example, a company may have collected, stored, and analyzed data… but may lack a cohesive plan for assigning ownership, knowing where data resides, or understanding the data’s quality. Your business may be suffering from a lack of data governance if:

• Data is siloed across multiple departments

• You lack universally agreed-upon definitions for basic terms like “customer”

• You don’t have visibility into what data is stale, deprecated, or subject to compliance regulations

• PII data is in clear text and available to a significant portion of the organization

For insurers, this could mean that auto, homeowners, and healthcare lines collect the same data from the same people but never share it. Without a data governance strategy, organizations may have outdated information from policyholders who kept their auto and homeowners policies with them while moving to a different healthcare insurer. This disconnect leads to outdated and inaccurate data.

A weak data governance strategy creates three primary risks:

• Inaccurate analytics: Outdated or inaccurate data undermines the analytics’ quality.

• Data breaches: Sensitive data distributed across silos makes security more difficult and gives malicious actors more opportunities to infiltrate your system.

• Fines or penalties: Noncompliance with regulations, like the GDPR and the Health Insurance Portability and Accountability Act (HIPAA), can lead to fines and penalties if the insurer can’t delete data or prevent unauthorized access – often totalling in the hundreds of millions of dollars.

The importance of data quality in insurance data governance

It is important to stress that high-quality data is fundamental to effective insurance operations, from accurate underwriting to efficient claims processing. A robust data governance framework ensures data quality through standardized processes for data validation, cleansing, and enrichment. Insurance companies must maintain strict data quality standards to support accurate risk assessment, regulatory reporting, and customer service. Poor data quality can lead to incorrect pricing decisions, regulatory non-compliance, and diminished customer satisfaction.

Data governance challenges in the insurance industry

Many insurance companies struggle with implementing a data governance strategy because they lack the necessary technology, people, and processes. Insurers have been collecting data for years, often relying on internal data sources rather than public databases. Consolidating this historical data into a single location becomes overwhelming without the right support in place.

The insurance industry faces significant data governance challenges, including:

• Difficulty integrating large data sets with new technologies

• Responding to the continuously evolving regulatory landscape

• Collaboration across product lines and internal departments

• Budget constraints that limit the ability to invest in tools

How data governance improves risk management in insurance

Effective data governance strengthens risk management capabilities by providing insurers with accurate, reliable data for underwriting decisions and claims processing. By implementing robust data governance frameworks, insurance companies can better assess risk patterns, detect fraud, and make more informed decisions about policy pricing and coverage terms. This systematic approach to data management enables insurers to maintain comprehensive audit trails, ensure data accuracy for actuarial calculations, and respond more effectively to emerging risks in the market.

Using data governance to enhance customer trust in insurance

Data governance plays a crucial role in building and maintaining customer trust by ensuring the secure and ethical handling of sensitive information. Insurance companies that demonstrate strong data governance practices show their commitment to protecting customer privacy, maintaining data accuracy, and using personal information responsibly. This transparency in data handling practices, combined with clear communication about data usage policies, helps insurers build lasting relationships with policyholders and differentiate themselves in a competitive market.

Best practices for data privacy in insurance

Leading insurers implement comprehensive data privacy practices that go beyond basic regulatory compliance. This includes implementing role-based access controls, encryption of sensitive data both at rest and in transit, and regular privacy impact assessments. Organizations should establish clear data classification schemes, maintain detailed data inventory records, and implement robust data masking and anonymization techniques. Regular employee training on data privacy protocols and incident response procedures ensures consistent application of privacy standards across the organization.

Building an effective data governance strategy for insurance companies

Insurers should build a modern data governance strategy to remain competitive and compliant. Here are some of the key considerations for insurers to keep in mind when building this out:

1. Review existing data assets

The first step to building a data governance strategy is understanding the data; this means identifying and categorizing it.

For example, to comply with privacy laws, all PII should be identified, including:

• Names

• Dates of birth

• Addresses

• Telephone numbers

• Email addresses

• Social security numbers

• Driver’s license numbers

• Medical record numbers

• Bank account or credit card information

• Health insurance information

• Biometric data

In addition, it should be determined where different departments store data. Some examples of departments to review include:

• Sales

• Marketing

• Finance

• Actuarial

• Claims

Once the data needed has been uncovered, along with where it resides, duplicated or outdated information can be removed.

2. Determine needs and business goals

By aligning business needs and goals with the organization’s data governance strategy, leaders can optimize data use for data teams and increase efficiencies.

Business goals tend to fall into three categories:

• Risk mitigation: Safeguarding data security and privacy by implementing controls has dual purposes: it protects sensitive data from external bad actors and ensures compliant usage, protecting against the risk of legal fines.

• Business insights: By supporting a deeper understanding of customers and prospects, leaders can make smarter, data-driven decisions about products, marketing, renewals, and claims management. They can develop more thoughtful communications and insurance plans that better address unique customer needs across different categories.

• Operational efficiency: Data governance helps organizations reduce costs while providing better customer experiences across policy administration, agency and agency management, and expense management.

How do you know what to prioritize? Some considerations include:

• Executive team strategies

• Risk trends

• Competitive analysis

• Product pricing

• Losses arising from claims

• Privacy and security compliance requirements

3. Assign roles and responsibilities

The “governance” in data governance means accountability and oversight. An effective strategy identifies responsible parties, outlines their roles, and empowers them to collaborate as they align on shared goals for the program.

Insurers should create a cross-departmental team that includes representatives from:

• IT

• Claims

• Underwriting

• Actuarial

• Customer service

• Payment processing

Within this, it may be necessary to think more granularly. For example, within underwriting and claims, organizations may want to consider who should be responsible for data sets that enable different product lines like:

• General liability

• Homeowner’s

• Auto

• Construction

• Product liability

• Professional liability

• Healthcare

Although there may be overlaps between the different data groups, identifying an individual as the “owner” will help to maintain accountability and oversight.

4. Implement data policies and procedures

Data policies establish a decision-making framework that defines how data is collected, used, and managed. Data policies establish and enable:

• Common definitions for data processes

• Compliance requirements

• Internal and external stakeholder communication expectations

Meanwhile, data procedures implement these policies, documenting the tasks needed to comply with the internal controls. They enable teams to:

• Ensure tasks comply with policies

• Establish consistent and efficient operations

• Determine how and when the steps within the core data functions should be completed

• Define job duties and staff interdependencies

It is important to be prepared to provide access to an individual’s personal information and the ability to correct, delete, and transfer their personal information. This is critical to be in compliance with regulations such as CCPA and GDPR.

When data policies and procedures are implemented, organizations ensure that everyone handling or managing data has a consistent knowledge base and set of expectations.

5. Leverage a data catalog to help streamline governance

A data catalog can automate many manual processes, including data identification, tagging, and classification. It creates a single source of reference for all data across a company while giving everyone the access they need to use the data with confidence.

A modern data catalog enhances a data governance strategy by:

• Automating dataset discovery

• Identifying and describing inventory

• Evaluating datasets to determine suitability for an analysis

• Setting access controls that comply with data security and privacy regulations

• Enabling self-service through search

The data catalog acts as the central hub for the data governance strategy, ensuring that people have the data and access they need with appropriate “governance guardrails” to guide compliant behavior and maintain accountability and oversight.

How to measure the success of data governance programs in insurance

Successful data governance programs in insurance can be measured through key performance indicators (KPIs) that track data quality, regulatory compliance, operational efficiency, and business value creation. Essential metrics include data accuracy rates, compliance violation incidents, time spent on data-related tasks, and the cost savings achieved through improved data management. 

Insurance companies should also monitor user satisfaction with data accessibility, the effectiveness of data security measures, and the impact of data governance on business outcomes such as improved underwriting accuracy and customer satisfaction scores.

The role of data catalogs in insurance data governance

Data catalogs serve as the foundation of effective data governance in insurance by providing a centralized repository for metadata management and data discovery. These tools enable insurance companies to maintain a comprehensive inventory of their data assets, track data lineage, and ensure regulatory compliance. Data catalogs facilitate collaboration between business and technical teams, streamline data access processes, and help maintain data quality standards across the organization's entire data ecosystem.

The future of data governance in the insurance industry

The future of data governance in insurance will be shaped by emerging technologies, evolving regulatory requirements, and changing customer expectations. Insurance companies will need to adapt their data governance frameworks to accommodate real-time data processing, IoT device integration, and advanced analytics capabilities. The industry is moving toward more automated and intelligent data governance solutions that can scale with the growing volume and variety of data while maintaining compliance and security standards.

How AI and machine learning enhance data governance in insurance

AI and machine learning technologies are revolutionizing data governance in insurance by automating data quality checks, identifying potential compliance issues, and streamlining metadata management. These technologies enable insurers to automatically classify sensitive data, monitor data usage patterns, and detect anomalies that could indicate security breaches or compliance violations. Machine learning algorithms can also help predict potential data quality issues and recommend corrective actions, making data governance more proactive and efficient.

Real-world use cases for insurance companies

Alation has been a catalyst for insurance companies in propelling customer success forward. We’ve already established that a data catalog can support insurance in maintaining compliance, but the competitive benefits of governance have historically gotten short shrift.

For example, Texas Mutual Insurance leverages Alation with Snowflake to streamline data usage, leverage data as an asset, and promote data literacy. On the platform, Texas Mutual built a consolidated view of the full ‘life-cycle’ with definitions, including, quotes, written, earned, billed, and net premiums. This allows every business function to report on key areas consistently and make decisions using the same, foundational understanding.

This, in turn, has led to increased trust in the data and faster delivery. With Alation, Texas Mutual has reduced the delivery time for key business dashboards by 80%. Executives now have daily access to trusted dashboards, which empower them to make in-the-moment decisions about where to deploy critical capital each day.

CNA insurance, one of the largest insurers in America, uses Alation to govern data across a hybrid data landscape. CNA has centralized metadata, lineage, stewardship, and business rules on-premises, while also creating a framework for governance, compliance and security in the cloud.

Alation has also supported American Family Insurance (AmFam) in its mission to transition from a “no legacy systems; no legacy mindset” to a more agile approach that leverages Alation Analytics Stewardship Application. This has helped the Fortune 500 insurance company drive more value from self-service analytics while ensuring accurate and compliant data use.

How insurance organizations can establish a data governance plan

Alation’s data catalog enables insurers to create a people-first, active data governance approach so that decision-makers can trust the data underlying their analyses while still meeting increasingly strict compliance requirements. With Alation’s Data Catalog, teams can adopt a data culture by breaking down silos, accelerating data governance, and empowering data governance leaders.

The Alation data catalog uses machine learning to drive pattern recognition so that insurers gain insight into how people use data, including popularity rankings, user breakdowns, and usage recommendations. To implement data policies and procedures, insurers can use our automated business glossary that aligns business and technical terminologies to establish common definitions across all stakeholders. Finally, our natural language search capabilities reinforce policies and procedures so that everyone can use the best and most relevant data, faster.

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FAQs

What is data governance in the insurance industry? Why is it important?

Insurance companies collect large amounts of data every day. Auto insurers, for example, track everything from how many miles a customer travels to what type of car they drive. Insurers use this data to help determine whether a policyholder qualifies for discounts, offers, and coverage.

How is data management useful in insurance?

Insurers with a robust data governance strategy can better understand their customers’ needs. A sharper understanding of customers can increase not only revenue, but customer satisfaction and lifelong loyalty.

What is the difference between data governance and data management in insurance?

Data governance establishes the framework, policies, and standards that define how data should be handled in an insurance organization, including roles, responsibilities, and compliance requirements. Data management is the practical execution of these policies through day-to-day operations like data handling, storage, and usage in activities such as claims processing and underwriting.

How can insurance companies justify the cost of implementing a data governance program?

The ROI of data governance in insurance can be measured through reduced operational costs from streamlined data processes, fewer regulatory fines, and improved decision-making accuracy in underwriting and claims. Insurance companies typically see returns through better risk assessment, reduced data errors, and enhanced customer satisfaction from improved data quality and protection.

What are the key roles needed on an insurance company's data governance team?

The core data governance team typically includes a Data Governance Officer (DGO) or Chief Data Officer (CDO) for strategic direction, Data Stewards from different business units for maintaining data quality, and Privacy and Security Officers for ensuring regulatory compliance. Data Owners and Data Quality Analysts round out the team by managing specific data sets and maintaining data standards across the organization.