Data Governance Enablement: Concept, Challenges, Principles, and Best Practices

Last updated on: June 15th, 2023, Published on: June 15th, 2023
Data Governance Enablement

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It’s one thing to have a data governance plan. It’s another to put it into practice. In this article, we’ll discuss what makes data governance enablement challenging and how to craft an actionable strategy that takes it from idea to reality.

Table of contents #

  1. What is data governance enablement?
  2. The challenge with data governance enablement
  3. The principles of data governance enablement
  4. How to implement data governance enablement
  5. Conclusion
  6. Data Governance Enablement: Related Reads

What is data governance enablement? #

Data governance enablement implements governance through procedures and processes that mirror how your teams actually work.

A successful data governance enablement initiative isn’t a one-size-fits-all solution. Instead, it’s a dynamic, flexible strategy supported via tooling and automation.

The challenge with data governance enablement #

Unfortunately, in some quarters, “data governance” has earned a bad reputation. When many people hear the term, they think of a top-down, rigid set of rules and regulations to which everyone must conform.

This is how too many data governance programs are run, even today. Rules and guidelines are set by a select few, without buy-in from an organization’s diverse pool of data producers and consumers. Organizations provide little tooling to assist, instead relying on the implementation of manual processes throughout the company.

The result is an approach to data governance that doesn’t scale to meet the needs of modern business. People perceive governance as a box they have to tick.

Many data-related projects don’t get off the ground in this environment. Those that do often operate as “shadow IT”, creating loosely managed data silos throughout the company.

At best, this approach makes data hard to find. At worst, it results in a compliance violation that can lead to hefty fines.

The principles of data governance enablement #

A better approach is adaptive data governance. Adaptive data governance abandons this command-and-control approach in favor of a distributed, flexible approach that uses multiple styles and is responsive to context. An adaptive approach drives data governance enablement, rather than hindering it.

Some hallmarks of adaptive governance and how they drive data governance enablement are:

  • Decentralization
  • Scalability
  • Flexibility
  • Security
  • Collaboration

Let’s explore each principle further.

Decentralization #


Rather than leaving decisions to a select few, a successful data governance enablement program distributes responsibility. It uses a hybrid approach to data governance, providing tools for data owners to oversee their own domains while ensuring overall compliance with industry standards and state regulations.

Scalability #


By 2025, we humans will be generating 180 zettabytes of data every year. In the face of such numbers, a successful data governance enablement program can’t be a manual effort. Automated tooling and decentralization are indispensable to any enablement program in the modern information age.

Flexibility #


A good data governance enablement program recognizes that the data needs of (for example) an engineering team and of a sales team are distinct, each with their own unique requirements.

Enablement also means achieving compliance while leaving room to meet the demands of individual use cases. It establishes different rules for data based on a diverse set of criteria, including but not limited to:

  • Job personas
  • The purpose of the data (its use cases)
  • Compliance scope (e.g., establishing different rules for Personally Identifiable Information, i.e., PII)
  • Project-based rules (data types, business workflows, etc.)

Security #


A bottom-up approach to data governance won’t work if everything is free-for-all. Successful organizations use trust-based security to drive decisions around who should have access to what data. It provides easy-to-use features for classifying data, and granular governance and access controls for data stewards, producers, and consumers alike.

Collaboration #


A data governance enablement program can’t scale if important functions remain in the hands of a select few. Collaborative tooling enables people to work together on maintaining data sets, metadata, and business glossaries while also giving data owners oversight over the final product.

How to implement data governance enablement #

So how do you get from a top-down, rigid data governance regime to true data governance enablement?

Here are the four key steps you need to take:

  1. Define a data governance framework
  2. Implement a data catalog
  3. Drive data catalog adoption
  4. Measure and make adjustments

Let’s break down each step further.

Define a data governance framework #


A data governance framework is a set of standards, guidelines, protocols, processes, and rules that enable organizations to effectively manage their data.

It takes some care to define a data governance framework that drives enablement rather than diminishes it. A good data governance framework should:

  • Encompass all data assets — all data sources, models, dashboards, analytics, etc.
  • Be practitioner-led — give data owners control over their domain while centrally enforcing definitions, standards, and processes to drive compliance
  • Be embedded in daily workflows — if a data governance framework is an afterthought, no one will do it. Rather, it should become an indispensable part of every data-related activity.

There are several data governance frameworks you can use as your starting point. Choose the one that works for your business and begin mapping out how it will fit into your existing data workflows.

Implement a data catalog #


A data catalog is a single source of truth that catalogs, classifies, and enables the discovery of all of the data in your organization. It is an indispensable piece in the scalable data governance enablement puzzle.

To understand the importance of a data catalog, take data classification as an example. Minimal compliance with laws such as the General Data Protection Regulation (GDPR) requires classifying data according to its sensitivity levels (i.e., confidential, highly confidential, personal).

Without a data catalog, you have to ask every team to find a way to tag their data and hope for the best. Furthermore, fulfilling a regulatory request (e.g., a GDPR right to erasure request) is a Herculean task, as you may not even know where all your company’s data is.

With a data catalog as your single source of truth, you can verify that all onboarded data sets are appropriately tagged for compliance purposes. You can generate alerts and action items for any teams that fall out of compliance. Finally, you can track the movement of data throughout your data estate and flag any movements that might put you at risk for violation.

That’s why selecting the right data catalog is an important decision. Look for one that supports the key features of a modern data catalog, such as classification, data lineage, collaboration, and granular governance controls.

Drive data catalog adoption #


With all stakeholders on board and your data catalog in place, the next step is to take an active approach and drive data catalog adoption through your organization.

Driving adoption involves:

  • Actively onboarding new teams to the data catalog and eliminating data silos
  • Creating adequate documentation and training
  • Encouraging knowledge sharing across the organization

Measure and make adjustments #


How do you know that you’re actually achieving data governance enablement? The only way to be sure is to measure.

So, before embarking on your data governance enablement journey, define the key metrics you’ll use to measure success. These can include:

  • The number of datasets onboarded to your data catalog
  • Data quality (measurable using data catalog tools such as Atlan)
  • The percentage of employees trained on data governance strategies and data catalog usage
  • The percentage of employees using the data catalog and associated data governance tools over time

If you’re not hitting your targets, gather more information to discover where you can improve. For example, if awareness of the data catalog is low, this may mean you need to invest more in internal evangelism and training.

Conclusion #

In this article, we covered the importance of bottom-up, collaborative, and flexible data governance for data governance enablement.

We also discussed the principle tasks for driving data governance enablement, including defining a data governance framework, adopting a data catalog, driving adoption, and measuring the results.

Data governance doesn’t have to be rigid and bureaucratic. A bottom-up, decentralized approach to data governance enablement can help employees get the most out of their data while also ensuring cross-company compliance.

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