Building a strong data community for data governance success

In a world overflowing with data, many organizations face the same challenge:

How can we ensure that our data is secure, confidential, and accessible to the right people?

The first instinct is often to design a detailed Data Governance Framework which can be a document or presentation full of rules, standards and policies defining what’s allowed and what’s not. Once the framework is written, it’s distributed across the organization assuming work is done.

Unfortunately, that’s not how it works. Data governance is not a one-time project but we need to see it as a continuous and people-driven process.

“Strong data communities are grown, not given.”

Why Is Data Governance So Important?

Effective data governance increases the value you get from your data assets and directly improves how people work with data.

1. Saves time and effort: Governance ensures consistent, high-quality and well-documented data. This means users spend less time searching for the right data and more time creating insights.

2. Makes work meaningful: Employees' efforts have a noticeable effect when data is reliable.. People can make confident decisions based on facts rather than assumptions.

3. Reduces stress: Poor data quality, unclear ownership or inconsistent definitions are major sources of frustration. Governance clarifies responsibilities and creates predictable, transparent processes.

4. Promotes community: Shared standards and practices encourage collaboration across departments and help create a stronger data-driven culture.

Key Roles in a Data Community

If you want to build a strong data community, you will need to identify some personas in your organization who are committed to help in this process. There are mainly 3 different roles, but it is possible that you expand the roles if needed:

• Data Governance Lead: Coordinates governance activities and ensures people are actively involved in the process. Will also take the lead in initiatives and organize meetings with all the corresponding stakeholders.

  
  

• Data Owner: Responsible for a specific dataset or data domain. This person secures resources, makes decisions, and defines data objectives.

  
  

• Data Steward: A person, often someone from the business or IT side, who handles data management within a business process or tool to ensure its quality and security. 

Common Challenges

Of course, every governance journey comes with its own challenges, such as:

• Lack of ownership: Often, no one feels truly responsible for specific datasets. Defining clear ownership early through roles like Data Owner and Data Steward prevents this issue.

  
  

• Lack of authority: Even with roles in place, governance can fail if data stewards and owners lack authority to enforce standards. That’s why strong leadership support is essential.

  
  

• Blurred boundaries: When responsibilities between business, IT and governance teams are unclear or overlap, inefficiency will probably follow. Regular alignment sessions and clear frameworks help to keep roles separate and synchronized. Don’t work against each other, but which each other.

  
  

• System landscape complexity: Data often resides in multiple systems, making it hard for users to know where to find what they need. Using tools like data catalogs improves discoverability and transparency because everyone knows where to find the right data they are looking for.

How to Organize a Data Community

To grow a strong data community, the Data Governance Lead needs to create initiatives that connect people and promote collaboration across the whole organization.

• Monthly community meetings: Bring the data community together to discuss decisions, share progress and present upcoming plans or reports. Make sure that all the data owners and data stewards are present during this meeting.

  
  

• Monthly steering committee: Engage senior management and key stakeholders to share overall progress, escalate issues and identify potential risks.

  
  

• 1-on-1 sessions with Data Owners: Dive deeper into department-specific challenges, experiences and ideas. These conversations help you understand what’s really happening within the organization.

  
  

In a smaller organization, data governance doesn’t need to be complex or heavily structured to be effective. The key is to keep it simple, practical and people focused.

Even if you don’t have resources for a full data governance team with all the roles defined above, it is still important you try to embed governance principles in your daily work. One person can wear multiple hats but you need to make sure that everyone knows who is responsible for which data. Instead of organizing large committees, you can keep it short and focused. If you don’t want to overload your team with new meetings, you can integrate them into existing team meetings or project reviews.

But also in a small organization, it is important to foster a culture of accountability and collaboration and to build a shared data catalog or a documentation hub so everyone understand where data lives and how to use it responsibly.

Tuckman’s Stages of Group Development in Data Governance

Building a data community is similar to building any team and it evolves through stages. Tuckman’s model describes five phases that every group goes through:

1. Forming: The team comes together for the first time. Focus on defining goals, scope and basic routines. Start small and engage those who are already motivated.

   
   

2. Storming: Conflicts and tension arise as individuals test boundaries. Go out into the field to listen actively and identify early ambassadors to help manage conflicts constructively.

   
   

3. Norming: The team begins to resolve conflicts and build cohesion. Encourage collaboration, agree on KPIs, and integrate them into performance targets. Demonstrate the value governance delivers to make sure everyone also sees the additional value.

   
   

4. Performing: The team now works smoothly and efficiently toward shared goals. Keep learning, iterating and growing. If it is possible, this is the perfect time to expand your community and bring new people on board.

   
   

5. Adjourning: When the project or phase ends, it is also key to celebrate achievements but don’t forget to facilitate handovers and reflect on lessons learned. Review the scope and goals to prepare for the next cycle and you are ready to start over again.

Data governance is not just about rules or technology, it’s about people, culture, and collaboration. A successful governance program grows from strong communities that share ownership, communicate openly and continuously learn from each other instead of publishing a lot of theoretical presentations and workbooks with strict guidelines and being a police officer. Acting like a police officer, you will lose people's engagement and your community. Make sure you listen actively, communicate honestly and transparent and try to create a ‘sharing is caring’ mindset.