Every click, customer interaction, and cloud upload adds to the flood of data most organizations manage daily. Information scattered across systems, SaaS applications, and cloud environments makes maintaining consistency and compliance a challenge.

That’s where a strong data governance framework comes in. These structured standards keep data accurate and secure, while ensuring compliance with regulatory requirements.

In this article, we discuss what data governance is and why it’s important. We also offer strategies for building a data governance framework.

What’s data governance, and why is it important?

Data governance refers to the internal standards an organization uses to manage its data. These standards usually take the form of a data governance framework: a set of ground rules that defines how an organization collects, stores, and uses its data. By laying out these processes, the framework ensures data is accurate, consistent, secure, and responsibly used. 

For example, the healthcare sector requires that patient records are standardized, limited to authorized staff, and comply with the Health Insurance Portability and Accountability Act of 1996 (HIPAA). With these requirements in mind, healthcare companies can build data governance structures that help them stay compliant in these areas.

While it’s a complex topic, you can distill data governance down into these three needs:

• Knowing data: A company needs to maintain a full inventory of all data and its context.
• Accessing data: The appropriate parties in an organization need access to fresh, relevant data.
• Protecting data: The misuse or unwanted exposure of data needs to be eliminated, including by parties inside the business.

Why data governance strategies matter

According to Gartner, 80% of data and analytics (D&A) governance initiatives will fail by 2027. Most organizations’ data governance systems don’t serve their needs because they’re treated as one-size-fits-all solutions that don’t effectively manage data in all scenarios. 

Here’s why a robust data governance framework is necessary.

Regulatory compliance

Data management frameworks ensure compliance with data privacy laws and other industry-specific regulations, helping companies avoid potential lawsuits and large fines. They also protect sensitive data from theft, reducing the risk of reputational damage.

Decision quality

A diverse dataset from multiple sources, paired with fewer errors and inconsistencies, means your leaders can make data-driven decisions with confidence. Following the rules included in a data governance framework can improve data quality, allowing organizations to better understand their customers’ journeys and even discover new growth opportunities. 

Risk reduction

A strong data governance framework improves data quality and decreases risks associated with the origin and sensitivity of data. It ensures that only the right people access data and that definitions and policies are standard across the board.

Avoiding data silos

If you want your organization to have a data-centric culture, then you need to make sure data doesn’t exist in a vacuum. Data governance frameworks foster collaboration by establishing which departments can store and share data with others in data lakes, marts, or warehouses. 

Data governance pillars

To establish whether a data governance methodology is strong enough, evaluate it against these four pillars:

1. Processes and workflows: Above all, data governance frameworks must establish how data is managed across the organization. If a framework doesn’t lay out the core processes and workflows for dealing with data, it isn’t fit for purpose.
2. People and roles: Frameworks must clearly establish who has responsibility over what assets. Without clear ownership and accountability, people are more likely to lose or mismanage data.
3. Policy and standards: Data must be secure and handled according to relevant regulations and policies. Establish logs, regulatory maps, and sensitivity-based data categories to simplify the process. 
4. Data lineage, metadata, and architecture: Data lineage tracks your data flow over time, metadata provides contextual information about each source, and data architecture lays out the structure that allows you to manage both lineage and metadata. A strong framework will establish processes for all of these components.

How to build and implement a data governance framework

Mapping out all the ways your organization deals with data can be tricky. We’ve broken down how to implement a data governance framework into actionable and easy-to-follow steps to help get you started.

Step 1. Define goals, objectives, and business drivers

The way companies manage the governance of data is constantly evolving, so it's important to establish your strategy’s primary goals, objectives, and business drivers to give your teams a sense of direction and guidance. 

Try starting with your organization’s current challenges. For example, are you having data quality issues? Are upcoming industry regulations set to upset your status quo? Start small and build a list of actions from there.

Step 2. Establish a governance council

Securing leadership buy-in early means they can prepare their teams for any ways a new data governance framework might disrupt current workflows. Establish a governance council with cross-department representation to keep initiatives aligned, funded, and prioritized across the organization.

Step 3. Identify and assign roles and responsibilities

Not every member of your team is qualified to run every task, and overlapping responsibilities can lead to data being mismanaged. Establish who needs access to what data, then identify and tag owners and stewards for each task and domain.

Step 4. Create an inventory of data assets

Map out all your data sources, including cloud, on-premises, and SaaS systems, to identify gaps and inconsistencies. A comprehensive inventory reveals duplication, inconsistencies, and risks that hinder your team’s performance. 

Step 5. Define policies, standards, and processes

Clear data access, retention, and protection policies keep both you and your customers safe. These standards ensure consistency across departments and guide the creation, sharing, and use of data. The final result is a structure that keeps your data ecosystem compliant and trusted.

Step 6. Select supporting tools and technologies

Data governance tools can automate cataloging, lineage tracking, and compliance monitoring to speed up the entire data management process. 

While not a dedicated data governance tool, Fivetran can still support your governance strategy through its security and compliance, end-to-end encryption, data blocking and hashing, role-based access control, and metadata logging features.

Data governance best practices

Here are a few best practices to follow when building your framework.

Start small, then expand

Many organizations struggle to manage their framework because they start too big. Instead of immediately trying to establish rules for multiple data lakes or warehousing processes, begin with a single high-impact area like finance or customer data. Once processes mature, you’ll be ready to scale governance across more domains, using the lessons learned from your initial implementation to guide you.

Use a federated governance model

A federated governance model combines central oversight with domain-level ownership. This means a company-wide set of rules governs data, while domain experts define how to apply them locally. Federated models are a great way of keeping a company agile.

Embed automation

Automating data cataloging, quality checks, and compliance reporting frees up your team and eliminates human error. The right tools also give you more visibility across diverse datasets, allowing you to locate and classify information easily.

Ensure stakeholder alignment

Keep every stakeholder — from executives to analysts — in the loop with transparent communication and regular updates. Consider scheduling regular training sessions to ensure data governance becomes part of the company culture, not just a checklist.

Common data governance challenges and pitfalls

Establishing new processes is always going to be challenging. Here are a few common issues to look out for.

Lack of clarity in roles, ownership, and accountability

When you don’t clearly define roles and responsibilities, data governance components quickly become ineffective and lose direction. Confusion over who owns what data or who needs to make a key decision will lead to stalled progress. 

Overcomplexity and trying to govern everything at once

Many organizations launch overly ambitious frameworks that cover too much, too soon. This complexity overwhelms teams and prevents the governance model from gaining traction where it matters most.

Low-quality data and missing lineage or metadata 

Without reliable data, even the best governance framework can fail. Missing metadata or unclear lineage makes it nearly impossible to track where data comes from or how it’s used. If there’s an error, it will be even harder to trace it back to the root cause.

Data silos and ineffective scaling

As frameworks expand to encompass multiple data platforms, maintaining consistent governance becomes harder. Data silos form as disconnected teams create gaps in compliance, visibility, and control.

Use cases and examples

Here are a few examples that show how strong governance frameworks deliver value in the real world.

Regulated industries

Many sectors, such as healthcare or finance, are subject to robust regulatory requirements. In these industries, a strong data governance framework ensures data privacy, compliance, and audit readiness. It helps organizations meet standards like HIPAA or the Fair Credit Reporting Act (FCRA) while maintaining trust with customers and regulators.

AI and machine learning teams

For AI and machine learning (ML) initiatives, clear oversight helps teams trace model inputs, reduce bias, and build reliable, ethical models. Data governance frameworks ensure data lineage, quality, and fairness when inputting data into AI and ML models. 

Merger and acquisition integration

When businesses merge, they must combine data across multiple domains into one system. A data governance framework streamlines these harmonization efforts and keeps everything consistent while reducing redundancy and accelerating post-merger integration.

Self-service analytics programs

Self-service analytics programs allow non-technical users to access and analyze data. Governance frameworks ensure everyone is working from a trusted, standardized hymn sheet. This consistency empowers faster insights without compromising accuracy or compliance.

How Fivetran supports and simplifies data governance

With over 700 fully managed, production-ready connectors and auditable data pipelines, Fivetran simplifies governance while making data more visible, no matter its source. For data engineers and analysts, this means a better understanding of upstream and downstream schemas. It also empowers teams to manage high-volume data pipelines with confidence and control.

Get started for free or book a live demo to see how Fivetran can help streamline your data governance process. 

FAQs

What is a framework for data governance?

A data governance framework is a structured model that defines how organizations manage data quality, security, privacy, and compliance across all systems and departments. 

What are the different data governance frameworks?

The main data governance frameworks include DAMA-DMBOK, COBIT, DCAM, and the EDM Association council framework. These structured systems guide businesses in standardizing data management, improving data compliance, and ensuring data integrity.

What’s a database governance model?

A data governance model is a framework that manages the storage and access of data secured in databases. The idea is to ensure compliance, accuracy, and consistent data use across the organization.

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