Data governance matters in every industry because it helps to establish data accuracy, reliability, integrity, and security – but it is especially important in the insurance world, since insurance providers are privy to vast amounts of personally identifiable or otherwise sensitive data.

A data breach lets hackers misuse stolen insurance data for criminal purposes such as identity theft. Moreover, such events result in business interruptions and can damage the reputation of the victim insurance company.

Data governance is critical in the insurance sector for these and many other reasons. In this article, we’ll explore the issue in detail.

What Is Data Governance?

Data governance means setting standards, policies, and procedures to ensure that data is handled properly while it is gathered, stored, processed, accessed, and deleted. Governance includes complying with internal and external standards to maintain data integrity, accuracy, and auditability.

The key purposes of data governance are:

  • Establish the infrastructure and technology to protect data assets;
  • Set up and maintain processes, procedures, and policies to ensure the accuracy and proper handling of data;
  • Identify the individuals who have the responsibility for handling enterprise data and ensuring data privacy.

Simply put, data governance is a systematic approach to manage data throughout its lifecycle, from acquisition and access to processing, use, storage, and disposal.

The Need for Data Governance in the Insurance Industry

The insurance industry (has developed a serious cybersecurity problem in recent years. Here are some alarming findings from the Cyber Insurance Risk in 2022 report:

  • 82 percent of the largest insurance providers are vulnerable to phishing attacks;
  • 18 percent of the analyzed insurance companies are susceptible to ransomware attacks
  • Software supply chain attacks tripled in 2021.

And why is the insurance sector such a focus for cyber attacks? Data.

The industry generates a vast amount of data, and doesn’t have strong governance systems in place to protect it all. Malicious cyber groups take advantage of these weaknesses to perpetrate phishing scams, launch malware and ransomware attacks, and steal data.

Ransomware is a particularly lucrative crime in the industry, with an average ransom payout of $130,000. The largest ransom paid to date by an insurance company exceeds $40 million.

To protect their data, insurers must lower their vulnerability to cyberattacks and data breaches. This is where data governance plays a role. But apart from data security, insurers also need a robust, enterprise-wide data governance strategy to ensure that their data is accurate, consistent, and compliant.

Data governance is crucial in insurance because it:

  • Ensures that data is reliable and trustworthy;
  • Improves data quality, which drives customer experiences and more personalized insurance products;
  • Provides a single source of the truth for more effective business decision-making.
  • Helps with risk management for insurance companies.

Effective data governance also removes data silos in insurance ecosystems, and increases confidence in data. It ensures that accurate and updated data is always available to support the operational requirements of actuarial professionals.

Data governance helps standardize data management procedures for more consistent data generation, handling, and protection. This then helps with regulatory, legal, and industry compliance and data audits. Finally, insurers with auditable data governance and compliance procedures are more likely to gain the trust of customers and other stakeholders.

Challenges of Data Governance in Insurance

As critical as data governance might be, it also has challenges. These include (but are not limited to) the following.

Personnel Issues

People are vital to a data governance framework, but they also present the biggest challenge to safeguarding and maintaining quality data. Effective data governance requires identifying “data stewards” and making them accountable to protect the organization’s data. This can be difficult to do.

The corporate culture needs to emphasize that data must be handled properly and effectively safeguarded. Users that create, change, and use insurance data must understand their role in maintaining data integrity. It’s not easy to create this cultural change or increase a sense of accountability.

Rapid Proliferation of Data

As the amount of data grows, governing it becomes more difficult. It creates a need for new procedures and systems, which means higher costs for insurance companies. Moreover, lots of new data is unstructured, making it difficult to govern with existing systems designed for structured data.

Data Silos

In insurance, a lot of information resides in separate silos, preventing departments from sharing data effectively. Silos also result in data duplication and errors. Removing these silos can be difficult when various data systems are not integrated. Additionally, governance requires cross-functional collaboration, which also can be a challenge for insurers.

How Insurance Companies Can Implement Robust Data Governance Strategies and Frameworks

Creating a data governance plan and data management strategies can overwhelm many insurers. The below strategies and best practices can reduce the overwhelm.

Review the Current State of Data Governance

Start by understanding the state and performance of your current data governance efforts – warts and all. Determine what your weaknesses are and which parts of governance need help, whether that’s data stewardship, data quality, data management, master data management, or something else. This information will guide the overall data governance strategy.

Create a Data Governance Framework

A data governance framework sets the foundation for data management and maintains the value of data. Both systems and people are part of this framework.

Data governance policies, tools, and procedures are essential to manage data storage, handling, and security. At the same time, people must ensure that these tools, policies, and procedures are in place and working as expected to handle and safeguard data sets.

The other crucial components of a data governance framework are:

  • The model describes the data flows, including its inputs, outputs, and storage parameters;
  • Standards describe how the organization will manage and govern data;
  • Metrics track strategy execution and assess the success of the governance program;
  • Organizational structure spells out the roles and responsibilities of accountable persons.

Assign Accountability

Identify who will have responsibility for the data and the governance program. Proper roles and responsibilities should be assigned, and senior leadership should establish a system to ensure accountability and transparency in the system.

Appoint a Governance Sponsor and Champion

A senior leader should sponsor the governance project and champion the data strategy. This person will also communicate the strategy to stakeholders and enforce accountability for effective data governance.

Create Procedures, Documentation, and Metrics

Strong governance procedures and controls ensure that the data is properly governed and protected. Policies and procedures must be established, communicated, and reinforced through training. Documentation describes all processes and provides a baseline for future improvements.

Metrics provide insights into the performance of governance systems and whether the organization is meeting its governance objectives. The sponsor and their team should identify a handful of manageable metrics that are most likely to yield meaningful business intelligence.

Leverage Technology

The right tools help the organization collect, manage, and secure data as per its governance objectives. Automation is useful to strengthen the data governance program and ensure its ongoing effectiveness.

Technology and software are also useful for:

  • Information stewardship: Profile data, execute governance initiatives, enforce high-quality standards, and assess the performance of data quality processes;
  • Information lifecycle management: Automate information archiving, retention, and destruction to manage risk and control data volumes;
  • Data management and integration: Augment data quality with artificial intelligence and machine learning and get insights from data analytics to assist with decision-making;
  • Metadata management: Manage the “information of information,” such as type, tags, source, and dates to aid with effective data management, categorization, and protection.

Improve Data Governance with ZenGRC

The ZenGRC platform will help you manage your data governance program by providing enhanced visibility into the risks to your data and compliance requirements. Through an integrated system, you can collect critical risk data across the organization and see where risk is changing.

With ZenGRC, Risk Observation, Assessment, and Remediation, you get a single source of truth to build a unified and trusted foundation for your data governance program. You can also automate critical tasks, simplify audits, regulatory compliance, and act quickly to minimize loss events.

Schedule a demo to experience ZenGRC for yourself.