The 3 Principles of Data Governance: Pillars of a Modern Data Culture

There are three important data governance principles that every data leader should know in 2023: #

1. Data governance should pivot to “data and analytics” governance
2. Rather than a centralized approach, data governance benefits from a decentralized, community-led approach.
3. Data governance should be a part of daily workflows, not an afterthought.

These principles are important because data governance is sometimes seen as a restrictive process; a set of rules that constrain higher-value activities. In reality, proper governance is essential for a functioning data culture. A useful analogy is the invisible hand in Adam Smith’s “The Wealth of Nations,” where some level of governance (such as an independent court system) is necessary to allow for beneficial emergent outcomes.

Governance is not only necessary; it’s increasingly a major limiting factor for data democratization. Tristan Handy of dbt explains in The Modern Data Stack: Past, Present, and Future that the modern data stack underwent a Cambrian Explosion from 2012-16, catalyzed by the release of Amazon Redshift in October 2012, and we’re on the brink of a second Cambrian explosion. One that requires data governance to step up — and mature to be an enabler of data consumption and usage; be more about context than control.

What are the key modern data governance principles? #

1. Pivoting from data governance to “data and analytics” governance #

Big data frequently draws comparisons with the California Gold Rush. Thousands of companies are springing up to help organizations gain value from data. “Data is an asset that has value to the enterprise and is managed accordingly,” according to the TOGAF Standard Architecture Principles. TOGAF, an enterprise architecture methodology used by 60% of Fortune 500 companies, further notes that data needs to be shareable and easily accessible for users to carry out their projects.

Data governance has always treated data as an asset – governance itself is key to extracting value from data while complying with privacy regulations. What’s changing is data governance is taking a greater role across the lifecycle of a data asset. The focus is broadening to include all the infrastructure and processes used to derive insights from data — rather than just concentrating on the data assets themselves. For example, governance has an important role in machine learning/artificial intelligence programs, which require effective governance to be successful.

This shift means that silos between governance, BI/analytics, and data management teams are dissolving as each team is able to share and consume information about data. “People are recognizing that BI and Analytics are not separate from Data Governance — they are just in another place on the continuum,” says Kelle O’Neal, Founder & CEO of First San Francisco Partners, in a DATAVERSITY interview.

2. Rather than a centralized approach, take a decentralized, community-led approach #

One of the most exciting things about modern data governance is that it creates a foundation for great teams by making it easier to collaborate on projects.

Our team at Atlan, for example, began by using data to tackle massive and complex problems facing the government of India (among other organizations). Internally, we had a diverse roster of individuals during our initial presentations, including social scientists, political scientists, data engineers, and geospatial scientists.

Every day our Slack channel was filled with questions about what a column represents, or requests for access to data. We realized as we grew in scale that fundamentally our biggest challenge was collaboration, not technology or infrastructure.

Our efforts to address these issues led us to build an internal tooling that served as a collaborative work space for data consumers — like what GitHub is for an engineering team. We went on to build a centralized data platform for the government of India in record time. Today, it is used by 100,000 government officials, MPs, and MLAs as a backbone for data-driven decision making.

“If you’re able to create a way for these diverse people to collaborate really effectively, to be a dream data team, where they trust each other, and they can collaborate effectively, then magic can happen.” - Prukalpa Sankar, Co-Founder of Atlan

3. Move from an afterthought to a part of daily workflows #

Data governance has traditionally been seen as an afterthought, but data governance now is evolving into a set of practices that embed into daily workflows and enable deep and secure collaboration between data teams.

Part of making governance a regular practice is to ensure everyone involved with a data asset — from the person who curates it to the person who eventually accesses and uses it for decision making — understands the aspects of data governance that are relevant to their work.

It’s important to have the right tools in place that automate data governance as much as possible. Modern data platforms offer a number of features that make democratized governance a reality, including:

• Customizable access policies based on user roles
• Easy integrations with platforms like Slack, Microsoft Teams, and Jira to make metadata accessible in native workflows and tools.
• Automated data lineage that allows you to track the source and evolution of a data asset
• A data catalog that allows data teams and consumers to manage distributed data in one place

The Pillars of Data Governance and How They Support a Diverse Data Culture #

For too long, data governance has been seen as a hindrance rather than a boon to data teams. For governance to evolve into a function that actively works to make data usable and shareable in real-time, organizations need an approach to governance that includes the three principles discussed above: pivoting to data and analytics governance, focusing on collaboration, and bringing governance into daily workflows.

A simple way to accomplish this is by implementing a data governance platform that is built to suit these goals.

To help understand Atlan’s approach to data governance and how it helps foster collaboration among diverse data consumers read our guide, The Third-Generation Data Catalog Primer.

Data governance principles: Next Steps #

Implementing data governance programs is a monumental undertaking. That’s why a solid plan, impactful goals, relevant and real-time metrics, and an emphasis on constant communication and collaboration are essential data governance best practices to embrace.

Ready to make data governance effortless?

Try Atlan — Auto-construct data lineage and deploy best-in-class data access governance without compromising on data democratization.

Data governance principles: Related reads #

• Data governance and its importance in the modern data stack
• 6 commonly referenced data governance frameworks in 2023
• 8 best practices for a robust data governance program
• Objectives and goals of data governance: Data democratization & data security
• Data governance policy: Examples, templates & how to write one
• Data Catalog: Does Your Business Really Need One?