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The Critical Importance of Choosing the Right Data Governance Operating Model for business benefits

To embark on data governance in an enterprise that spans divisions and diverse stakeholders, a well-defined operating model plays a vital role in achieving expected business benefits. Data mesh is a new concept that encourages data democratization within an organization in a decentralized way by promoting data products. Unlike the vintage architecture, which is centralized, data mesh enables users across the enterprise to access any data, resulting in more business units being able to monetize data and drive business transformation. To manage and govern data in an organization, irrespective of the architectural choices used, there must be a data governance operating model and roadmap that considers the following aspects:


  • Organizational scope, data scope, and domain scope management

  • Identifying stakeholders and responsibility; defining handshakes and hand-offs for coordination

  • Organizational placement of decision-making for data-related decisions

  • Antecedents, contingencies, and impact on data being managed actively

  • Motivation, goals, and performance assessment plan to measure progress and report

  • Discovering and standardizing processes and procedures based on policies and guidelines

  • Change control with well-planned stakeholder communication strategy

  • Implementation roadmap with a work breakdown structure

  • Risks, value, and benefits management


For this article, data quality assessment and monitoring will be used for illustration. The approach to defining an operating model should consider the aspects of control, management, and existing capabilities in business units. The socio-cultural aspects of an organization govern a successful data quality service implementation. Other important aspects include shared business accountability, sponsorship, attitude toward data governance, knowledge, and tendency to embrace changes. Attaining the right balance among the above aspects defines a near-perfect operating model that will assist the enterprise in reaching its data quality goals. For businesses to progress and stay ahead of the competition, it is crucial to continuously refine their processes and embrace feedback. This gradual improvement ensures that the enterprise remains relevant and leads to better outcomes and increased success over time.


When deciding on the data governance operating model, you cannot simply pick one approach without evaluating the benefits each one offers. You need to weigh the potential benefits of centralized and decentralized governance models before making a decision. If you find that the benefits of centralizing your governance operations exceed those of a decentralized model by at least 20%, then it’s best to centralize. With a centralized governance model, you can bridge the skills gap, enjoy consistent outcomes across all business units, easily report on operations, ensure executive buy-in at the C-level, and plan for effectiveness in continuous feedback elicitation, improvements, and change management. However, the downside is that it often leads to operation rigidity, which reduces motivation among mid-level managers, and bureaucracy often outweighs the benefits.


It’s important to consider socio-cultural aspects when formulating your operating model, as they can significantly influence the success of your organization. If you want your business to stand out and stay ahead of the competition, you need to continuously refine your processes and embrace feedback. This gradual improvement ensures that your enterprise remains relevant and leads to better outcomes and increased success over time.



Having to define an operating model that sways to either end of a centralized or distributed management does not serve the intended purpose. Data mesh provides a better alternative to hub-spoke model, which splits up data ownership and responsibility across a network of teams. This approach allows for decentralized ownership and decision-making while providing the necessary structure to ensure data integrity. The key aspects that need consideration include integration, quality, security, collaboration, and privacy, to name a few. It also ensures that data is shared securely and that the data is accessible and usable by all teams. While both the operating model variants will fit the organizations embracing data mesh, however, other sociocultural factors will have to be considered to arrive at an operating model. Attaining a delicate equilibrium between control and management and requiring capabilities that allow stakeholders to embrace the data quality initiative with little re-skilling for self-service is possible.

A hybrid approach that allows for a holistic view of data governance with central control over policies, framework, reporting, and local management of other aspects suits many organizations’ cultures.




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