You want to implement data democratization, so you deployed all the new tooling and data infrastructure. You have a data catalog to manage metadata and ensure data lineage and a data marketplace to enable data discovery and self-service analytics. You’ve invested in the latest technologies to enable full self-service operation. 

The data management architecture you built is ready for automation and collaboration. Yet people are not using it, and adoption is slower than expected. 

What’s going on? Often, IT departments focus heavily on the technological aspects but neglect the essential organizational and cultural changes needed for success. In this article, we will explore the areas organizations need to take into account as they roll out data democratization.

The Principles of Data Democratization

Data democratization empowers employees to take ownership of the data, make agile data-driven decisions, and foster innovation. One of the core principles of data democratization is decentralization. Moving from a centralized IT control model to a domain-oriented structure is necessary. 

Cross-functional domain teams are essential to unlock innovation and accelerate business initiatives. This means each domain team takes full ownership of their data and is responsible for data governance, quality, and compliance. This is also one of the main challenges to adoption since this new approach fundamentally changes how the teams operate and engage with the core IT organization.

While decentralization is crucial, it does not imply the absence of centralized oversight. A central governance body should be established to set global policies, standards, and ensure compliance across all domains. This body functions as the glue that binds the decentralized teams, ensuring alignment and consistency. 

However, this approach can cause friction between teams. Without the proper context and support of the new IT team structure, it may feel like a return to overbearing central governance and an excessive burden on domain teams.

The Cultural Shift Required

You can have the best architected and maintained data infrastructure, but it won’t matter if nobody uses it. Companies must enact cultural change for data democratization to work, affecting both domain and  IT teams. 

To operate decentralized data governance, people need to understand its principles, requirements, and the value it brings to the organization. While rolling out a new operational model based on data democratization, there are a couple of principles important to foster cultural shift that are often overlooked:

• Empowerment and trust
• Training and awareness
• Recognition and incentives
• Leadership support and commitment

Let’s look at how we can utilize these principles to enact a cultural shift in the organization.

Empowerment and Trust

Empowering domain teams to make independent decisions is the most significant cultural shift that comes with data democratization. Building a culture of trust, where teams are accountable and take ownership of their data, is essential. At the same time, the teams originally in charge of the data need to come to terms with the need to relinquish ownership and change their mindset from control to empower domain teams.  

Encouraging autonomy involves giving domain teams the freedom to make decisions related to their data. This means they are free to use the best tools available for data collection, storage, and processing allowing them to innovate and find the best solutions for their unique challenges without waiting for central IT approval.

However, this does not mean that domain teams can do anything they want. With autonomy and ownership comes accountability (there is no free lunch). Domain teams are required to adhere to a set of standards to promote interoperability between teams and standardize reporting across the organization. 

Accountability materializes in several areas, such as maintaining data quality, adhering to data security requirements, or classification and regulatory policies. Domain teams can often feel that many of the requirements are beyond their abilities, either from an expertise or a technology standpoint. 

This is where IT teams must change their mindset and structure to provide help, services, and tooling to domain teams. Domain teams effectively become IT’s internal customers.

Example: Trust Framework for Data Quality

A robust framework for accountability can be built around data quality metrics by the IT team and central governance body. Domain teams should then implement it. This framework should include:

• Data Quality Standards: Establish clear data quality standards to which all domain teams must adhere. These standards should cover aspects such as accuracy, completeness, consistency, and timeliness and are usually set by the IT team. 
• Domain Quality Standards: These are metrics for specific business rules that need to be tracked for defined business domains. Often driven by regulatory requirements and standardized across the enterprise to ensure accurate reporting, such standards are usually set by subject matter experts or domain teams.
• Regular Audits and Reporting: Implement regular audits and require domain teams to report on their data quality metrics in standardized ways. This ensures ongoing accountability and highlights areas needing improvement.

These requirements may feel overwhelming to domain teams that don’t have previous expertise with data quality practice or enterprise reporting. They may see it as discouraging or feel that all this additional governance is slowing them down.  

There are multiple approaches IT teams should implement to support domain teams to comply with these new requirements:

• Self-service tooling: Provide self-service, easy-to-use tooling for domain teams to implement the requirements. It should be an easily deployable and maintainable part of your data stack.
• Focus on automation: The best kind of governance is the one you don’t have to set up and maintain yourself. Many governance requirements, especially in the data quality space, can be automated.
• Provide best practices and training: Tell them how to use the tooling you are providing and expectations for their participation.
• Provide expertise for advanced use cases: Your team knows the tooling and requirements best. You should be providing your expertise in situations where self-service tooling is not enough or best practices are not documented yet.

This all seems like a lot of work for the IT teams that will require re-thinking how the role of centralized IT will evolve in this new decentralized environment. 

Training and Awareness

Training and awareness are crucial for fostering a culture that supports data democratization. It’s not enough to train just the IT and data teams; the entire organization must understand the principles of data democratization. Training must be intentional and supported by the organization’s leaders to add credibility. 

Set clear expectations for all employees regarding their participation in and support for the data democratization model. They must understand how their work impacts data quality and governance and their role in the overall data strategy.

This ensures that the domain teams and IT understand not only the new organizational structure but also their responsibilities and governance requirements.

Example: Organization-Wide Training on Data Democratization Principles

Training programs should be comprehensive and inclusive, covering the following areas:

• Principles of Data Democratization: Educate employees on the basics of data democratization, including its benefits and how it differs from traditional data governance models.
• Tools and Technologies: Provide training on the tools and technologies implemented for data democratization, such as data catalogs and data marketplaces. Be clear about the intended usage of the tools. Ideally, support the training with practical examples. 
• Roles and Responsibilities: Clearly define and communicate the roles and responsibilities of each team and individual in the new governance model. Provide this information as contextual documentation in the self-service tools. 

Recognition and Reward Programs

An often overlooked aspect of culture change and data democratization is the need for new approaches to recognition, rewards, and internal marketing. This does not seem like a crucial aspect of data democratization, but it greatly accelerates the adoption in the real world.

Developing recognition programs to reward teams and individuals excelling in data governance is essential. Including governance metrics in performance evaluations reinforces the importance of these practices.

Recognition programs should be intentional. Promoting the projects with tangible business benefits and individuals with the greatest impact creates vocal advocates for change internally. 

Example: Internal Marketing Campaign 

As seen implemented in a large telco org: Treat internal recognition programs as internal marketing campaigns. You are selling a new approach to data governance to the organization. 

• Create catchy names for the advocates (e.g., “data heroes”) and visible internal promotion materials (posters, t-shirts, themed offices). 
• Recognize the best teams at all-hands meetings and encourage them to do a data roadshow, showing other departments how they successfully delivered projects through these new approaches. 

Leadership Commitment and Participation

Securing executive support is vital. Leaders must communicate the vision, provide necessary resources, and exemplify the desired behaviors. This top-down commitment sets the tone for the entire organization.

Management should act as advocates and role models for the new governance model. This means actively participating in governance activities, promoting the importance of data quality, and recognizing the efforts of teams and individuals who excel in these areas.

Example: Leaders as Advocates

Leaders’ participation in data democratization can take many forms. The goal is to support the initiative and show that they believe in the process. 

• Leaders should participate in the governance council and provide feedback on the governance initiatives.
• Participate in tool training to demonstrate the value of the self-service, hands-on approach.
• Promote success stories where the outcome of data democratization has a measurable business impact.

Evolving Role of the IT Organization

Implementing data democratization and changing from a centralized to decentralized data management organization requires new approaches to communication and collaboration between teams. 

Where the creation and principles of cross-functional domain teams are well documented, the changes needed for IT organizations are often overlooked. We explored multiple aspects of the cultural shift needed for this transition that affect how IT teams operate. 

From practical experience, the “Center of Excellence” approach usually delivers the best results for IT team transformation to a federated governance model. 

Center of Excellence Approach for IT organizations

Collaboration between IT, data teams, and domain teams is often well served by the Center of Excellence (CoE) approach. 

A Center of Excellence can play a pivotal role in supporting data democratization. The CoE serves as a hub of expertise, providing resources, tools, and guidance to domain teams. It ensures consistency in standards and practices across the organization while allowing domain teams the autonomy to manage their data.

The CoE model positions the IT department as a service provider to other departments, treating them as customers. This shift in perspective fosters a customer-centric approach, where the CoE focuses on enabling and empowering domain teams.

The CoE should provide a range of resources and expertise, including:

• Cross-Domain Projects Support: Encouraging and supporting cross-domain projects that require collaboration between different teams and identifying opportunities for cross-domain collaboration.
• Technical Support: Helping domain teams with advanced tool configuration and issue resolution.
• Best Practices: Maintaining the best practices for data infrastructure usage.
• Training and Development: Organizing training sessions and professional development opportunities for domain teams. 
• Knowledge Sharing Platforms: Creating and maintaining platforms where teams can share knowledge, best practices, and lessons learned. 
• Streamline repeated tasks: Analyze engagements with other teams and look for patterns where new tools or automations can be utilized. Build repeatable processes and technical resources that can be added to the best practices documentation and supported by the team. CoE acts as a software vendor, maintaining these solutions. 

By building a Center of Excellence and treating other departments as customers, IT organizations provide the necessary support and resources to enable effective data governance across the enterprise. 

Example: How CoE Provides Resources and Expertise

• The CoE offers weekly office hours where domain teams can seek advice on data governance issues, access a library of best practices, and attend specialized training sessions.
• A company’s CoE organizes a monthly “Data Governance Roundtable” where representatives from each domain team discuss challenges, share solutions, and align on best practices.
• When a domain team starts a new project, they can ask for consultation from CoE to help with difficult architecture or tooling decisions. CoE enables the expert to collaborate with the domain team for the duration of the project.
• CoE meets internally every quarter to discuss and identify opportunities to create new automations based on frequent, repeated requests from domain teams (e.g., creating a reusable reporting package or automating another data quality check).

Conclusion

Transitioning to a data democratization model requires more than just technological changes. Organizational and cultural shifts are equally important to ensure success. Embracing and supporting these changes ensures that data democratization efforts are not only technologically sound but also culturally and organizationally sustainable.

The changes necessary for establishing cross-functional domain teams and the new role IT teams are playing in this setup need to be intentional and clearly explained to the whole organization. Leadership support and internal marketing are crucial for the success of data democratization initiatives.