What is enterprise data governance?

By now, it’s common knowledge that data is a vital competitive asset for almost every company. There is, however, a flip side to this undeniable truth: data can also become a liability.

Gathering, storing, and using data isn’t limited to experts on data or technical teams. It flows through the entire organization. There are countless touchpoints where people in sales, product, marketing, and other diverse teams make decisions about and take action on the information your company collects.

Without cohesive structure and guidance for handling data, stakeholders may unintentionally corrupt the quality or security of your company’s data. That can spark a cascade of problems that lead to lasting damage.

You can employ a data governance framework to provide clear and consistent policies and standards to ensure the integrity, security, accessibility, and overall quality of your data assets. In this article, we’ll cover what that looks like in an enterprise organization.

What is Enterprise Data Governance?

The Data Governance Institute defines data governance as a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models, which describe who can take what actions with what information, and when, under what circumstances, and using what methods.

That’s quite a mouthful. Exactly what this looks like will vary from org to org, depending on your specific product and priorities. However, any data governance program has the same three goals:

• Establish and align rules for the entire company around how data is collected, stored, and used (and also when and how it is to be discarded)
• Monitoring the global data estate to make sure governance standards and policies are being respected
• Resolving any issues or conflicts affecting data in your company while supporting your data end users

These are good tenets to follow for businesses of any size. But they’re critical for enterprise organizations. Smaller orgs and startups may be able to muddle through with informal data governance practices, addressing data-related cross-functional activities in meetings and emails. The larger the company, the more complex and distributed the data assets. This is where the four pillars of enterprise data governance come in.

The four pillars of enterprise data governance

Data governance plays a crucial role in ensuring the effective management, quality, and security of your data assets. To achieve this, an effective data governance program needs a solid foundation based on the four pillars of data governance: data quality, data stewardship, data protection and compliance, and data management.

Data quality

Ensuring the accuracy, completeness, and consistency of all data assets across your entire organization, no matter how large or distributed your company may be.

Data stewardship

Quality doesn’t happen by itself. Another cornerstone of data governance is creating and assigning clear and dedicated roles and/or responsibilities for the people in charge of managing, monitoring, and ensuring data within your organization — i.e., data stewards.

Data stewards serve as the front line for a governance program because they are collectively in charge of defining and documenting your company’s data assets, ensuring data quality, and promoting effective data sharing and usage by every shareholder across the organization. They also act as liaisons between different teams to resolve any data issues that arise and are responsible for ensuring that governance policies and procedures are implemented correctly.

Data protection and compliance

The third pillar of data governance focuses on protecting data from unauthorized access, ensuring compliance with privacy laws and regulations, and managing user access and authentication. There are three main components here:

• Security measures that prevent unauthorized access to your company’s data overall.
• Privacy measures to protect the privacy of sensitive and personal data within your company.
• Processes and policies to ensure compliance with all applicable regulations and contractual requirements.

Data management

This pillar encompasses the processes and procedures for storing, accessing, and manipulating data effectively. It includes metadata management, data lifecycle management, and data integration (i.e., how data is structured, stored, and linked across different systems and databases in your organization).

When does a company need enterprise data governance?

Enterprise data governance becomes necessary when your organization becomes large enough that casual, arms-length management can no longer be counted on for controlling data-related activities across the entire company.

As your company grows, so does the number and complexity of your data systems. Without some sort of formal data governance framework, your data estate quickly becomes siloed as teams naturally diverge in their priorities and choices made around data. It quickly becomes impossible to gain informed, higher-level horizontal visibility around the masses of data pooled and handled around your organization.

Likewise, many (if not most) enterprise-level companies eventually become liable to regulatory compliance and/or contractual requirements that call for a formal data program. For example, 6.3 billion people—or 79.3% of the world's population—is covered by one or more national data privacy regulations like the EU’s GDPR or China’s PIPL. Certain heavily regulated industries, like finance and healthcare, are subject to even more laws like FINRA (the Financial Industry Regulatory Authority) and HIPAA (the Health Insurance Portability and Accountability Act).

Enterprise data governance frameworks

Enterprise data governance frameworks are platforms designed to streamline and automate the complex and multifaceted process of managing, organizing, and protecting data across a large, and even globally distributed, enterprise organization.

It’s certainly possible to draw up your own data governance framework from scratch. There are many governance program templates out there, both open source and for-profit, ranging from the nonprofit Data Governance Institute to elite consulting companies like McKinsey and Gartner.

The “why build when you can buy?” rule absolutely applies to data governance frameworks. Starting with a pre-built framework based on tried-and-true principles that you can adjust to your organization cuts down the time required to design and roll out a new program.

Data governance tools can also cut down the time required to implement a data governance framework. With tools like dbt Cloud, you can create a data control plane to manage your data uniformly in a standardized framework. That means everyone in the organization—whether a data engineer, business analyst, or executive leader—can move fast with trusted data in a scalable, cost-effective way.

An enterprise-quality framework standardizes your teams on the terminology and concepts most important to your company while it builds collaborative data bridges across the full organization. Business, technical, and compliance stakeholders can now communicate easily with each other, exchanging data-driven information and ideas.

In short, an enterprise data governance framework empowers every person in your company to extract value from your data assets while managing cost and complexity.

Choosing the right enterprise data governance framework

There are many data governance framework tools out there. Critical components of a mature, enterprise-ready platform include data stewardship, quality control, cataloging, lineage tracking, security, compliance, and data visualization.

Not every solution, however, offers all of these in a single platform that can help you understand your operations, improve your performance, and achieve your goals. Here’s what to look for in a scalable and enterprise-ready platform:

• It manages data complexity in a way that’s modular, scalable, repeatable, and governed — directly inside of your data platform instead of scattered across all the different business intelligence and technical platforms used by all the different teams and departments within your organization.
• It’s vendor-agnostic. It can integrate with the major data cloud platforms and data tools that are important to you. It makes it straightforward to connect your data directly to Snowflake, Databricks, BigQuery, and all other leading data cloud platforms.
• It utilizes centralized, reusable models, fosters collaboration, reduces duplication, and ensures consistent data definitions across teams.
• It has robust audit logging and access control features to help safeguard data integrity.
• It allows your data teams to build, test, and deploy analytics code using software development best practices (like portability, CI/CD, observability, documentation, etc.) to create production-grade analytics pipelines that scale along with your workloads.
• It delivers accessible and easy-to-understand data models that can be delivered into your BI tools, LLMs, and APIs so that stakeholders have the accurate data they need and when and where they need it.

The dbt framework is a scalable and enterprise-ready platform that fulfills all of these requirements plus many more. dbt can help your company standardize data transformation processes, increase data quality and transparency through lineage tracking, and automate documentation while providing horizontal visibility across all your company’s data assets.

The right data governance framework makes sure that you can be confident in your data—and everyone in your company, no matter what their role, can use it for making accurate and informed decisions. Your enterprise gets enhanced data quality and consistency, reduced data management costs, and accelerated insights from trusted data.

When your enterprise data governance framework automates making your data flows traceable and your data-related processes transparent, you can focus on optimizing your operations, improving performance, and achieving your goals—while simultaneously minimizing any downside data security and privacy risks.