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.

There needs to be a clear distinction between the following:

1. Data infrastructure and platform management

2. Data usage management, and 

3. Data project management

4. Data assessment and monitoring

To understand this further, organizational contingencies determine the data governance configuration of an organization. In order to understand how contingencies affect the individual design of a company’s data governance operating model, two design parameters should be considered: 

1. Organizational placement of decision-making authority and 

2. Coordination of decision-making authority

The value pairs range from two choices based on design parameters

1. Centralized to decentralized, and 

2. Hierarchical to cooperative models. 

A design parameter influences the assignment of responsibilities in a RACI matrix for a data governance model.

Organizational Placement of Decision-Making Authority

The first design parameter for the data governance operating model is the organizational structure of the data management activities. Centralized data governance leads to greater control over data standards and enables better monetization of information at scale. Decentralized IT governance allows excellent responsiveness and flexibility regarding business needs and customized solutions for each business unit. 

However, a suitable model of data governance needs to balance trade-offs between:

• Standardization, on the one hand, and 
• Responsiveness to events, on the other hand. 

A digital function in a large financial services enterprise might be more offensive in its strategic direction. It will have to be nimble to react to market conditions, while other divisions may still be defensive in their strategy owing to higher regulatory environment conditions.

Role	Centralized Data Governance Design	Decentralized Data Governance Design
Executive sponsor	“Accountable” in some decisions of major relevance	“Consulted” (recommending, not commanding)
Data council	Many “Accountable”	Many “Consulted,” “Informed,” no “Accountable” alone
Business owner	Some “Accountable”	Mostly “Accountable”
Business and technical data steward	“Responsible”	Many “Responsible” and some “Accountable”

A decentralized data model involves all decision-making authority allocated to individual units, divisions, or lines of business. The centralized form is associated with smaller firms, defensive and conservative strategies, centralized control, and mechanistic decision-making. The decentralized form is associated with large firms, offensive or aggressive strategies, decentralized control, and organic decision-making.

Coordination of Decision-Making Authority

The effective coordination of decision-making authority and influence can be achieved through hierarchical and vertical lines or collaborative and horizontal capabilities. Companies that have distributed data management use these mechanisms to coordinate data sharing and decisions. To maximize the effectiveness of data governance design, it is important that it reflects and supports the decision-making style and culture of the company.

Role	Hierarchical Data Governance Design	Cooperative Data Governance Design
Executive sponsor	“Accountable” in most decisions	“Accountable” conjointly”
Business owner	“Accountable” separately	“Consulted,” “Informed,” few “Accountable”
Business and technical Steward	“Responsible,” “Informed,” few “Consulted”	Many “Accountable” (conjointly) and “Consulted”
Data council	“Accountable” separately	Many “Consulted” and “Accountable” (Conjointly)

The hierarchical data governance model is characterized by a top-down decision-making approach. Either the data officer or the data council has decision-making authority for a single data management activity. Tasks are delegated to business and technical data stewards. However, they will not be directly involved in decision-making.

In a cooperative data governance model, formal and informal coordination mechanisms are used to reach decisions. A data council or data officer is complemented by working groups, task forces, and committees with members from multiple disciplines. No single role can make a decision on its own. New integrator roles, such as process owners or data architects that report to business units, establish a high degree of cross-unit collaboration.