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 GDPR and 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.
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.
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.
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. 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.
What are the Risks of a Weak Data Governance Strategy?
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 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 General Data Protection Regulation (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.
What are Data Governance Challenges for 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 Insurers Can Build a Strategy for Data Governance
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:
- Dates of birth
- 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:
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:
- 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
- Product liability
- Professional liability
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 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.
Alation Use Cases for Insurance Organizations
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 Alation’s Data Catalog Helps Insurance Organizations 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.
Interested to learn more? Book a demo with us today.