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Design the data governance that works for your organization

A visual diagram with interconnected coral, blue, and white rectangular nodes of varying sizes, linked by gradient lines on a dark blue, grid-like background.

At a Glance:

  • Many organizations disproportionately invest in technology at the expense of data governance.
  • Good data governance focuses on data architecture, quality, security, and privacy, but many factors, such as business ownership, roles, and enterprise culture, impact data governance.
  • Assessing where you are on the data governance maturity journey and evaluating different governance models can provide more clarity on what will work for you.
  • Data governance starts with an enterprise commitment: engaging stakeholders to create alignment, defining core use cases, and better understanding your risks, challenges, and objectives to turn your data into the powerful business advantage that it can be.

To keep up or get ahead in the race towards a fully data-centric and artificial intelligence-enabled world, many organizations invest heavily in technology at the expense of creating the capabilities and governance needed to manage data as an enterprise imperative. This leads to failed or stalled efforts and missed opportunities.

A recent Wavestone survey found that while 80% of organizations view governance issues as their primary data challenge, 75% of investments are directed at technology—and product-centric initiatives.

Leaders are often challenged with establishing the core components of effective data governance for their organization and creating the conditions for defining and adopting the operating model, roles, and responsibilities that will enable the path toward successful governance.

By adopting a holistic approach that encompasses data ownership, adaptability, culture, and dedicated roles, you can establish a data governance model that meets your short-term needs while also starting the journey toward a more mature, adaptive governance capability.

What does effective data governance look like?

Data governance must address at least four dimensions to enable long-term value realization:

  • Data Architecture to establish standards and guidelines for the design and structure of data to ensure availability and interoperability.
  • Data Quality ensures that data is accurate, complete, valid, and consistent.
  • Data Security protects data from unauthorized access, use, and disclosure and enables access to data to those who need it.
  • Data Privacy is the practice of maintaining consumer trust by ensuring that personal information is used in compliance with applicable laws and regulations.

However, those four dimensions are dramatically impacted by your organization’s profile, make-up, and history. As you examine the state of data governance in your enterprise, these attributes can help you assess where you are:

A circular flow diagram with seven blue rectangles connected by a pale purple arrow, representing a continuous cycle. The rectangles contain the following labels: "Enterprise Responsibility," "Adapted roles," "Business ownership," "Scenario-tested," "Skilled workforce," "Ingrained in culture," and "Agile-adaptive." The diagram highlights key attributes to assess the state of data governance in an organization.
  • Does your organization recognize data governance as an enterprise responsibility?
  • Have governance roles been established and resourced?
  • Does the business recognize ownership of the data (vs. the IT team)?
  • Has your governance model been scenario-tested?
  • Has your organization invested in upskilling your workforce on data governance?
  • Is data governance part of your training, ways of working and culture?
  • Can your data governance quickly and nimbly adjust in an agile way?
  • Is your data governance reducing risk across the enterprise?

Where are you on your data governance maturity journey?

Creating resilient, value-driving data governance often requires an extensive journey toward maturity. The graphic below illustrates the maturity evolution towards more effective governance value delivery.

  • Relatively immature organizations usually have a “chaotic” model, with siloed and reactive approaches increasing risks, or a “rigid/centralized” model, which can constrain the business by being highly inflexible and slow.
Governance value delivery diagram
  • Over time, organizations tend to move toward an “improvised” model, which is loosely coordinated and still shallow in its application, or an “experimental” model, which, in an effort to scale, combines elements of self-service with some collaboration between a central entity and the business units.
  • More mature organizations evolve towards a “standardized” model with proactive, planned, and federated governance or even a fully “adaptive” model with strong resilience and accountable federated governance.

Highly centralized models can look attractive at first and are easier to implement. However, they often are too slow to adjust to business changes and limit the influence of distributed business teams.

On the other hand, highly decentralized governance models struggle to prioritize data requests, lack a unified view, and can lead to duplicate efforts and data sources.

One model that has gained much traction because it combines enterprise visibility and guidance with business unit-level engagement is the “Hub and Spoke” model:

Hub and Spoke Model diagram
  • The Data Hub stands up and streamlines the data and analytics foundation for the enterprise.
  • It includes strategic capabilities that are best maintained at the enterprise level, such as enterprise planning, platform and architecture management and overall direction.
  • The Spokes are where tactical capabilities are executed, with an emphasis on quick value realization, data product development/execution and adaptability to changing business needs, while staying within the enterprise standards.
  • Both Hub and Spoke have dedicated roles and resources that collaborate to minimize risks and enhance resilience.
  • Defining clear roles and accountabilities is key and often requires an investment of resources, skills and an adjustment to the ways of working across the enterprise.

What to ask to launch your journey towards effective data governance

So, where should you focus your efforts to determine what’s right for you? Start by engaging with your stakeholders to create alignment. To create the path to the optimal data governance model for your organization, alignment between the business and technology/data teams is essential, and the following questions are a great starting point:

  • What are the core governance use cases for your organization?
  • How complex are your data challenges?
  • Who should have decision power related to data governance across the enterprise?
  • How will cross-enterprise data needs be met?
  • How will data quality and consistency be enhanced?
  • What state of data maturity and business engagement exists today?

These questions can be the foundation for planning workshops with key stakeholders to assess your needs, where you are in your journey, and how to outline the path toward progress. In the end, effective data governance requires an enterprise commitment, clear and shared responsibilities, and the progressive development of a culture of collaboration, experimentation, and enablement to turn your data into the powerful business advantage that it can be.

Alain Paolini Headshot
By Alain Paolini
Managing Vice President
Mr. Alain Paolini is a Business Executive with US and international experience in leading digital transformation initiatives, managing organizational change, and improving sales/operations performance.

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