Emergence of Data Access Governance in the Insurance Industry


While data quality, dependability, integrity, and security hold importance across all companies, their significance becomes even more pronounced within the insurance sector. This heightened importance arises from the fact that insurers possess access to sensitive information.

In the event of a data breach, hackers could exploit compromised insurance records for fraudulent purposes such as identity theft. Furthermore, such incidents result in business disruptions and have the potential to damage the reputation of the targeted insurance company. These factors collectively underscore the necessity of robust data governance within the insurance industry.

Implications of the New Data Role for insurance providers:

Like numerous other sectors, the insurance industry is currently experiencing a digital transformation. The nature of work is evolving due to disruptive technologies such as artificial intelligence, robotics, the Internet of Things (IoT), big data, 3D printing, virtual reality, and cloud computing. This shift towards digitalization sometimes compels companies to establish fresh business models. Concurrently, insurance companies must be cautious about the potential perils of digital disruption. New players are entering the insurance market, bringing with them innovative strategies.

At its core, the insurance business revolves around its policyholders. Consequently, insurers need to contemplate how data might impact the expectations of their customers. Consumers seek insurance policy options that are straightforward and easy to comprehend. They anticipate being able to connect with their insurance providers through any available means, at any time, from any location, and using any device.

Why Is Data Management Helpful In The Insurance Industry?

Strong data governance strategies help insurance companies learn more about their client’s requirements. Revenue, customer happiness, and brand loyalty benefit from a deeper knowledge of the consumer base. Insurers may lessen the likelihood of a regulatory violation by compiling a database of sensitive information. Lastly, with governance, insurance executives may boost efficiency and save costs.

Insurers must, for instance, diligently gather standard personal data like zip code, age, ethnicity, marital status, and income. Insurers also want new information, like rate changes or claims data. Insurers may confidently set prices and develop new products using up-to-date, accurate data.

In addition, the insurance sector must follow several laws governing the privacy and security of personal information. To implement the necessary protections, like flags that notify internal data users whether data is private, they must be aware of the types of PII and ePHI they collect. They can accomplish this with less effort because data governance streamlines operations and boosts productivity.

Challenges in Data Governance within the Insurance Sector

While data governance holds significant importance, it’s not without its challenges. Here are a few examples, although this list is not exhaustive.

  • Questions of Personnel

The human element is the lifeblood of the data governance framework, but it also presents the most substantial risk to data quality. Identifying individuals as “data stewards” and entrusting them with the security of the company’s data is vital for effective data governance. However, achieving this can be a challenging endeavor.

Establishing a culture that emphasizes proper data handling and secure storage should be an integral part of the company’s ethos. Individuals working with insurance data, from its development to utilization, must understand their role in maintaining its quality. Cultivating such a responsible culture is a complex undertaking. 

  • Quicker product introduction

The insurance industry operates within a highly competitive landscape, frequently introducing new offerings. Simultaneously, multiple IT releases might occur to support these offerings. Consequently, testers often struggle to allocate sufficient time to tasks like identifying appropriate test data and configuring test environments.

Indicators of Ineffective or Absent Data Governance

Recognizing signs of ineffective or absent data governance is essential. For instance, a company might possess data that has been collected, stored, and analyzed, yet lacks a cohesive strategy for designating ownership, locating data, or evaluating its quality. If any of the following scenarios apply to your organization, it’s possible that a data governance issue exists:

  • Lack of Data Sharing Across Departments
  • Absence of a Consensual Understanding of Fundamental Concepts, Such as “Client”
  • Inability to Determine Data Currency, Relevance, or Suitability for Regulatory Compliance
  • Storage of Personal Identifiers in Plain Text, Accessible to Nearly Any Employee
  • Potential Scenario of Different Divisions, Such as Auto, Homeowners, and Health Insurance, Collecting Identical Data from the Same Individuals Without Sharing. This could lead to outdated information being retained about customers who switched healthcare providers but maintained their existing auto and home insurance policies.

These signs underscore the necessity of a structured data management framework to prevent such issues. 


The ever-evolving compliance standards present a substantial challenge for insurers. They need to review their existing data management policies to assess their ability to strategically respond to and accommodate the regulatory changes within the industry.

In the face of changing regulations, insurers are confronted with a choice: concentrate on developing a viable product or seize the opportunity to reshape their company and position themselves as industry frontrunners.

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