Build a practical data governance plan that delivers results
Written by Gregory Jaros, Andrea Reyes and Karen Latora, with interview contributions from David Woodley, Rachel Sullivan, and Christopher Amherst
Organizations must be able to trust their data to operate effectively and make sound decisions. Reliable, well-managed data powers stronger business intelligence, more accurate constituent and financial insights, and greater organizational confidence. However, building that trust requires intentional effort and planning. By designing governance models—and supporting them with capabilities such as automated data profiling, de-duplication, and master data management—organizations can ensure their data is accurate, consistent, and delivers long-term value.
Data governance is often misunderstood as a compliance exercise or a documentation burden. But in practice, it is what allows organizations to trust their data, use it confidently, and scale its impact over time.
Treating data as a managed asset reduces risk, improves quality, supports regulatory compliance, and expands its value for reporting and analytics. Effective governance is iterative and flexible, adapting to evolving quality and monitoring needs. A strong Data Governance Plan clarifies ownership, accountability, and usage–building trust in data and enabling faster decision-making, self-service analytics, and more efficient data operations.
When done well, governance doesn’t slow teams down, it frees them up.
To Begin, Assess the Current State
Before undertaking any data governance initiative, organizations must first assess their current state. This step is critical to understanding which problems need to be solved, which opportunities can be leveraged, and who should be involved in the effort. A thoughtful assessment helps break what can feel like a large, complex undertaking into clear, achievable steps.
In conversations with data leaders at Northeastern University, the University of Alaska Foundation, and Purdue for Life Foundation, a consistent theme emerged. Despite differences in size, structure, and data maturity, each organization emphasized the importance of starting with a focused set of practical questions before introducing formal governance structures. Establishing this clarity early permits organizations to prioritize effectively and align stakeholders around shared goals.
A strong current-state assessment begins with answering fundamental questions:
- Who owns the data?
- Who is responsible for its maintenance and management?
- Where do data silos and shadow systems exist?
- Which data assets are most critical to the organization?
- Which data challenges have the greatest organizational impact?
It is equally important to determine whether these challenges stem from process gaps, technology limitations, or training needs. Together, these steps help organizations focus their governance efforts strategically—rather than attempting to govern everything at once. With These insights in hand, organizations can begin building a data governance foundation that is both practical and sustainable.
Data Governance Works Best When It’s Iterative
One of the most common mistakes organizations make is attempting to design and deploy governance all at once. Implementing data governance is a significant undertaking that depends on meaningful culture and process change, as well as the adoption of new behaviors and responsibilities across the organization. Attempting to introduce everything all at once dramatically increases the risk of failure. An iterative approach is far more effective: breaking transformation into small, achievable pieces that allow for rapid testing and adjustment while delivering incremental value. This approach helps demonstrate benefits early, reduces disruption, and prevents governance efforts from being deprioritized. Solutions tailored to an organization’s culture consistently yield the best results.
A practical starting point is improving the current state of data operations, then embedding governance into each iteration of data products and datasets. As new use cases are delivered—often in short, two- to four-week cycles—governance artifacts are created and refined in parallel. Over time, this approach builds a durable foundation that scales with the organization. Rather than attempting to anticipate every requirement upfront, iterative governance allows organizations to focus on top priorities, engage key stakeholders, and deliver high-value use cases quickly. This ensures governance remains relevant and aligned with how teams naturally use data.
What a Strong Data Governance Foundation Includes
A strong data governance foundation starts by focusing on the data that matters most and applying best practices to real-world use cases. Early activities typically center on:
- Identifying key data domains and subject matter experts
- Defining high-impact data elements and metrics
- Establishing clear, business-facing definitions supported by data dictionaries
- Drafting a data catalog and business glossary
- Setting a regular cadence for monitoring, issue resolution, and change management
It is equally important to establish the operating model that sustains governance over time by:
- Defining the purpose and objectives of the data governance program
- Prioritizing one or two critical pain points as initial use cases
- Clearly assigning roles and responsibilities across business and technical teams
Foundational activities establish active and effective governance. By sequencing the rollout across data domains and business unit–often starting with a pilot—and aligning with existing systems and change management efforts, organizations can build a governance foundation that is practical, scalable, and built to last.
Build Upon the Foundation
As data governance matures, organizations expand their foundation by introducing more formal intake processes for data issues, support models, policies, service levels, and clearly defined roles for data operations and maintenance. Experience gained in early phases helps inform the governance roadmap and refine rollout timelines. During this time, successful governance efforts undergo:
- Continual monitoring and adjustment of processes to support consistency and scale
- Cultivation of in-house data champions, supported through targeted training programs
- Assessment, implementation, and roll-out of enabling technologies to reinforce governance practices and reduce manual effort
Both David Woodley, Chief Data Officer at the University of Alaska Foundation, and Rachel Sullivan, Assistant Director of Data Strategy at Northeastern, emphasized the importance of formalizing data ownership early and establishing an executive-level data governance committee to enforce standards across the enterprise. An effective committee:
- Aligns data policies with organizational goals
- Confirms staff are trained in core data tools and technologies
- Provides consistent communication across the organization
- Revisits data and technology decisions as needs evolve
- Establishes quality control measures to manage data updates and change
This structure keeps governance grounded in real-world usage and solidifies its support for analytics, reporting, and operational priorities.
Governance as a Driver of Data Enablement
The real value of data governance shows up in how effectively people can use data to do their jobs. In conversations with data leaders, one theme consistently emerged: assigning value to data is essential. Governance is not about collecting more data–it is about collecting the right data. As Sullivan noted, organizations must avoid the “garbage in, garbage out” trap by being intentional about what data they capture, manage, and trust. Woodley reinforced this view, emphasizing that identifying the most important data sets is far more critical than maximizing volume.
Without governance, data teams are often pulled into reactive work, answering ad hoc questions, resolving confusion caused by overlapping data sources, or fixing downstream issues triggered by upstream changes. These efforts consume time and limit the team’s ability to deliver higher-value analytics and insights.
A governance-led enablement approach addresses these challenges by:
- Documenting data lineage so the impact of changes is understood in advance
- Building curated data dictionaries and catalogs aligned to business use cases
- Delivering self-service dashboards and data products designed around how users actually work
Incorporating usability feedback and change management into every release assures that these assets remain relevant and trusted.
Our recommended approach to data governance focuses on empowering data teams while enabling scalable, self-service capabilities for end users. The result is increased self-sufficiency among analysts and business stakeholders, faster turnaround on analysis, and higher satisfaction and adoption across the organization. By reducing reliance on centralized teams for routine questions, governance frees up time for deeper, higher-impact work. Data teams can operate as true data services functions at scale, delivering reusable data products that ultiply their impact and deliver business value.
How Data Governance Improves Data Operations
When data governance is embedded quickly into data delivery, organizations experience clear operational gains. Teams can scope data changes more quickly and accurately, reduce the time required to build, test, and release new data products, and onboard users and datasets with less friction. Governance also increases confidence in data by promoting transparency and shared understanding, while expanding self-service capabilities across business functions. Over time, these practices build trust in data by making it easier to understand, access, and reuse without adding unnecessary manual effort or administrative overhead.
Making Data Governance Work for You
Data governance should never exist as an abstract framework that lives on a shelf. At its best, it is a practical, evolving model that reflects organizational priorities, supports the way teams work, and grows alongside the data platform. When governance is designed to meet the organization where it is–and adapt as needs change–it becomes an enabler rather than an obligation.
By focusing on high-impact data, delivering governance iteratively, and aligning enablement with real business needs, organizations can turn governance into a multiplier, not a constraint. The path forward begins with a clear understanding of your current state and a deliberate governance model designed to support where you want to go next–unlocking greater trust, scalability, and value from your data.