Data Governance Operating Models

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Data Governance Operating Models

Similar to many areas of data governance, the “best” data governance operating model for an organization is up for debate. Selecting the right data governance operating model for an organization requires careful consideration of its unique characteristics and circumstances. Key factors like company culture, executive buy-in, corporate structure, and available funding all influence the ideal choice between centralized, federated, hybrid, or decentralized approaches. There is no one-size-fits-all model. A successful governance framework must align with ingrained habits around decision-making, data maturity, and collaboration. By taking a holistic view of the organizational dynamics at play, leaders and teams can determine the operating model best positioned to deliver cohesive and effective data governance.

Centralized Model

In a centralized approach, a central authority or team, such as a Chief Data Officer or central data governance committee, makes all key data governance decisions. They serve as a single point of reference for activities and strategic approaches. The centralized team will create policies, procedures, standards, and processes. The centralized model aims to deliver consistent data governance across the organization through centralized coordination. However, some believe there are risks of being perceived as bureaucratic and unresponsive to the needs of each line of business.

Federated Model

In recent years, the Federated Operating model has gained popularity. Many technology and consulting organizations are leveraging the term “Data Mesh” to describe the federated approach. The federated model shares data governance responsibilities across different teams in an organization. It creates local data teams in each business unit or region. These teams get to make decisions about data policies and practices for their units, but they need to follow standards set by a central data council, data governance team, or group that is responsible for these activities.

With federated models, communication and collaboration are a must. The central and local teams need to be in close contact. If not, gaps can emerge. However if done well, federated data governance can empower specialized teams while keeping everything connected.

Hybrid Model

The hybrid model is exactly what it sounds like – a blended approach from both the centralized and federated models. Many organizations find themselves in a hybrid approach given their company culture, data maturity, and sponsorship from business executives. It’s a balancing act between standardization across the company and customization where required. The central and smaller groups have to collaborate closely. The central team provides guidance to keep everything aligned, but they often have in-team stewards adapt as needed for their situation. Communication is also a significant factor in a hybrid or blended approach. The teams have to stay in sync on strategy while allowing customized governance.

Organizations often evolve their data governance operating models over time as needs change. Many members of our DataQG community have transitioned between centralized, federated, and hybrid approaches. Some have moved from centralized to federated models to allow for more localization. Others went from federated to centralized to drive more consistency. Some migrated from hybrid structures to fully centralized ones to simplify oversight. There is no perfect model that suits every organization forever. As priorities and challenges shift, governance frameworks have to keep pace. By regularly re-evaluating their operating models, our members can adapt them based on new learnings and experience.