Apache Atlas demoApache Atlas demo

What Is Data Governance: Definition, Importance, and Components

March 21st, 2022

header image for What Is Data Governance: Definition, Importance, and Components

What is data governance?

Data governance is a set of policies, processes, and standards to collect, manage, and store data for better decision-making. It helps organizations ensure the relevance, integrity, availability, security, and usability of their data.

There’s no one formula for strong data governance. Each organization will have to craft a data governance strategy to suit their work and data. For example, an organization with individuals’ banking information will have to adjust its data access and retention policies as per financial regulations.

However, data governance isn't just about compliance, nor should it just be a headache for your IT teams. It’s much more — it defines how your organization uses data. Finding the right balance between data democratization and data governance can help create a strong, sustainable data culture in your organization.

[Download ebook] → A Guide to Building a Business Case for a Data Catalog

Why does data governance matter?

Organizations today have vast amounts of data. In a rapidly changing regulatory environment, a strong data governance system is no longer "nice to have". It's crucial to keep your data safe, accessible, and reliable.

However, Gartner predicts that “through 2022, only 20% of organizations investing in information governance will succeed in scaling governance for digital business”.

Data governance matters because it helps companies answer critical questions like:

  • How do you store data assets?
  • Which assets are more valuable?
  • What are the relationships between various assets?
  • What is the monetary value of assets?
  • How can we protect and reuse assets?
  • Who has access to which asset?

An enterprise-level data governance program can break data silos and build trust. It defines a unified way to govern data assets, such as building a business glossary, creating metadata, setting up data lineage systems , smart data catalogs. These help an organization derive maximum value from their data.

The benefits of data governance

There are four ways that data governance will benefit your organization:

  • Comply with data protection laws and regulations
  • Protect the integrity and relevance of your data
  • Democratize your data
  • Build effective collaboration between teams

Comply with data protection laws and regulations

Regulations such as GDPR and CCPA define how organizations should store, manage, and monitor sensitive customer data. The rules are strict, and the fines for violating these rules are draconian. A strong data governance system can help ensure that your organization is in line with important regulations. With data governance all data practitioners have automated guidance to established processes for structured creation, usage and deletion of data.

Protect the integrity and relevance of your data

When you implement better data governance, you get a bird's eye view of your organization’s data activities. You can manage who has access to data and monitor who has modified data to reduce potential security breaches. In case of breaches it always helps to have an alert system in place for notification to concerned authorities plus visibility of how data has changed through lineage impact analysis for quick resolution.

Democratize your data

Data democratization empowers everyone in your organization to access the data they need to do their lives' best work. With data governance, you can safely democratize your data, prevent data silos, and create a self-service analytics ecosystem where everyone can easily explore and use data. For e.g. everyone should have visibility of what data is available within an organization, but they do not necessarily need to understand that data in detail till they have authorization, with a proper data governance strategy in place you can ensure to give people visibility of data + access controls, so they can ask for what they need, and to the right person.

Build effective collaboration between teams

Collaboration is the key to winning with data. Smooth collaboration between business and IT teams helps everyone accomplish data tasks as they evolve along with changing business requirements. Data governance makes experimentation and collaboration easier while ensuring that each person only has access to the data they need.

Data governance enables effective collaboration

Data democratization is a reality with effective data governance

Related read: Data governance benefits — 4 ways it helps build great data teams

[Download ebook] → Rethinking Data Governance for the Modern Data Stack

Data Governance best practices

Governance shouldn’t be a complicated, siloed initiative that only a few people in your organization are responsible for.

We live in a world where data governance needs to be fundamentally rethought. Think of it like Google Docs, which made it easier for teams to collaborate on files. This helped people stop sharing and downloading files, which improved security and governance.

Great governance makes data safer and easier for everyone in an organization. It’s a team sport, so it should make life easier for every stakeholder involved.

Looking to build a strong governance system in your organization? Here are five essential best practices that will help you lay a strong foundation:

Track and manage data assets with the help of a modern data catalog

A watertight data governance strategy cannot be implemented till you catalog all information you hold. So almost always step 1 is tracking, profiling, managing and auditing data assets with the help of a modern data catalog.

  • Bring distributed data across databases, warehouses, etc., to one place.
  • Enrich assets with metadata.
  • Quickly locate frequently used data and people responsible for it.
  • Auto-generate data quality profiles.

Create business glossaries

Creating business glossaries is a super important step towards data governance. There’s stuff like PII data and GDPR rules that are standard across organizations. Each organization also has different ways of serving data to the right user, all of that can be controlled by a central metadata repository governed by business glossaries.

  • Create business glossaries
  • Classify and mask sensitive data.
    • Set clear classification and access policies to comply with regulations like GDPR and CCPA.
    • Automatic discovery of PII data assets will make classification easier.

Set up data lineage and impact analysis

Modern data catalogs are built on principles of diverse metadata collection and management and can easily help with automated data lineage and impact analysis. Lineage visibility is available to the column level to help easily identify the source, movement, behavior, and impact of data changes over time.

  • Set up data lineage and impact analysis
    • Quickly identify the source of data and how it moves over time.
    • Check the data assets impacted before making any changes.
    • View the transformational logic at each step.

Grant and manage data access control policies

Data governance also ensures you can control access to data at a granular level thus also ensuring that you have the ability to democratize data across your organization.

  • Grant and manage data access control policies
    • Create granular data access policies
    • Ensure that each user can only access the data they need.
    • These policies can even be propagated through lineage.

Related read:8 data governance best practices from data leaders


Data governance helps organizations maintain data security and privacy without slowing down data agility. Good governance can contribute to the creation of a data culture that is a win for every stakeholder, rather than just the IT team.

Ready to take our human-centric data governance ecosystem for a test drive?

Data Catalog Primer - Everything You Need to Know About Data Catalogs.

Adopting a data catalog is the first step towards data discovery. In this guide, we explore the evolution of the data management ecosystem, the challenges created by traditional data catalog solutions, and what an ideal, modern-day data catalog should look like. Download now!