What Is Data Governance and Why Does It Matter?

Dataiku Product Lauren Anderson

According to Dama, a global organization dedicated to advancing the concepts and practices of information and data management, “data governance is defined as the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets" (Henderson, D., &; Earley, S., 2017). The purpose of data governance is to ensure that data is managed properly, according to policies and best practices.

Obviously, data governance is not a new concept, but as organizations deal with an ever-increasing amount of data from customers, suppliers, and internal tools (by one account, in 2025 there will be 463 exabytes of data generated per day, globally) along with the growth of data privacy legislation and regulations, enforcing best practices and data governance frameworks is more critical than ever.

data governance

Why Does Data Governance Matter?

Proper data governance is essential when it comes to managing the needs of a modern enterprise. The impact of poor data governance practices can be costly. When data is not properly secured or categorized, companies can run afoul of data privacy regulations like CCPA, HIPAA, or GDPR, which can lead to hefty fines and negative reputational impact. Conversely, data quality issues, such as when data definitions are different between tools, can lead to inaccuracy in BI and data science initiatives which can, in turn, cause the business to focus on the wrong strategic projects. 

Ultimately, a good data governance strategy aims to get rid of data silos, giving the organization access to high-quality, relevant data — all in a secure, governed way. Organizations can achieve better customer outcomes and operational efficiencies with a good data governance framework and strategy. 

Data Governance vs. AI Governance

While data and AI Governance both have the same underlying goal of enforcing the right frameworks and best practices across the company, AI Governance also squarely focuses on scaling AI — from technical issues around data quality and ML model maintenance to overall inefficiency, opacity, and risk associated with growing AI initiatives. Ultimately, good data governance leads to better AI Governance and vice versa.

→ Full Ebook: From Data Governance to AI Governance

Data Governance vs. Data Management

Data governance can be considered an important subset of the umbrella topic of data management.  As a part of data management, data governance explicitly deals with concepts like how data is secured, modeled, cleaned, tagged, and categorized. In contrast, data management more broadly deals with topics like ingesting, storing, mining, and archiving data. 

What Are the Best Practices Around Data Governance?

Establishing a strong data governance framework is a journey and it’s worth re-evaluating whether you have clear alignment with your overall goals from time to time. Here are some general best practices we’ve heard from customers and the industry: 

    • Understand how to measure success and involve the business in defining goals: A good data governance strategy should have clear metrics and KPIs to measure progress over time. Business leaders should also be involved in defining goals, both to ensure organizational alignment and policy enforcement. 
    • Define clear roles and accountability teams across the data lifecycle: Data isn’t static — it’s transformed, cleansed, deleted, etc. by different users for different purposes. Because of this, you should have a way to build audit trails and data lineage throughout the entire lifecycle, with all users who interact with data so that the right people are accountable.
    • Don’t overcorrect on data restrictions: Restricting data access to a high level can be tempting, however creating bottlenecks to data access can drastically slow down the business, creating a new type of operational risk — that of project failure and falling behind the competition. Before creating new policy restrictions, try to gather information from the business on how the data is used before making decisions so that you know at which level to restrict access. 

Dataiku and Data Governance

In the world of data governance, there are many tools available at different points of the data lifecycle, from data cataloging to policy management and threat detection. Dataiku is designed to easily integrate with your data governance tech stack so that you can benefit from Dataiku’s unified analytics and AI platform capabilities, from data preparation to model deployment, without disrupting your current strategy. For example, through APIs and plugins, you can easily connect to metadata management and cataloging tools.

Dataiku also has features within the platform to help with data protection, privacy, and compliance. Additionally, to address data quality, Dataiku’s visual flow gives full traceability of data from source to final data product, while visual recipes empower users to quickly cleanse, deduplicate, and transform data. Overall, Dataiku makes it easy for you to enforce data governance best practices with the mix of solutions that you see fit.

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