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This second part continues the journey started in the previous article, where we explored the strategic relevance of data governance and its particular impact on the utilities sector.

The next step is effective implementation. This requires organizational readiness, a hands-on approach, and avoiding common mistakes that can compromise results.

Implementation: practical steps and recommended approaches

With the right preparation, organizations can move toward effective implementation. Key elements include:

• Defining clear objectives aligned with business priorities and regulatory frameworks.
• Establishing roles and responsibilities, including a data governance committee.
• Selecting appropriate tools and processes, based on technological and organizational maturity.
• Continuous monitoring and adjustment to prevent stagnation or misalignment.

Gustavo Mesa emphasizes that each organization must tailor its strategy to its own context: “There’s no universal formula; every organizational culture requires a specific approach”.

That’s why he recommends starting with a maturity assessment and prioritizing quick wins, even before formalizing extensive policies.

Common mistakes and how to avoid them

Challenges in data governance are not only technical. Among the most common pitfalls are:

• Lack of committed leadership
• Overambitious plans at the outset, without practical solutions
• Overreliance on technology, without aligned processes or people
• No clear metrics to evaluate impact
• Weak communication on the purpose of governance policies
• Treating all data with the same level of priority

Mesa stresses: “Test before you formalize. Implement before you document”.

Nicole Halm adds: “Governance is a dynamic process that must evolve alongside data generation”.

Key indicators to measure governance success

Success in data governance initiatives varies by organizational goals, but some common indicators include:

• Data quality: Are the data accurate, complete, consistent, and up-to-date?
• Operational efficiency: Is governance reducing redundancies and optimizing processes?
• Business impact: Does it contribute to strategic objectives, like increased revenue, better decision-making, or higher customer satisfaction?
• Regulatory compliance: Does it ensure adherence to local standards and regulations?
• Accessibility and usability: Are data available to authorized users at the right time, without compromising security?
• Adoption and organizational culture: Is governance accepted and embedded across teams and collaborators?

When can a data governance policy be considered successful?

According to Gustavo Mesa, success is achieved “when the organization meets its goals, acknowledges governance as a process enabler, and addresses previously overlooked issues”.

He points out that, commonly, “80% of project time is spent on data acquisition”. Often, this falls to staff without technical training, “which increases time and errors”.

Therefore, he proposes to “measure success by reducing that time, allowing teams to focus on their core responsibilities”. In other words, the strategy should enable clients to “shift the conversation from data to decisions”.

Practical tips to get started

• Define a clear objective
• Assess the current state of data and processes
• Secure active support from senior leadership
• Form a dedicated data governance team
• Start with a limited-scope pilot project
• Prioritize simple, practical policies at the outset
• Communicate benefits and train teams
• Choose technologies aligned with your needs
• Measure results and adjust continuously

Data governance at Quanam: our experience

Gustavo Mesa shared that Quanam implemented a metadata catalog to classify reports and define business terms, responding to frequent queries from the Chief Financial Officer (CFO).

He added that this catalog, which maps data lineage, helps streamline knowledge transfer and system maintenance.

“One might assume a CFO wouldn’t typically lead a data governance strategy. But there are always opportunities to deliver value in unexpected areas, responding to real needs that optimize processes”, he concluded.

A success story: utility in Florida, USA

Martín Cal, Data & Analytics Project Manager at Quanam, shared the experience of working with a public utilities company in Orlando, Florida:

“In our work with the client, we implemented KeeDATA, a data catalog based on CKAN. This catalog registers all data assets daily, including 6,363 objects in Oracle databases, 912 Control-M ¹ job objects, 637 objects in the Cognos production environment, 17 Talend ² jobs, and 17 external files used in jobs as CSVs”, he explained.

Cal highlighted that “the catalog enables users to search concepts, measures, and tables, check information availability, understand how data is loaded and distributed across the organization, and identify data owners and contact points to request access”.

Author: Journalist Alejandro Acle
Co-written with: Nicole Halm and Gustavo Mesa

¹ Control-M is OUC’s corporate tool for scheduling and automating script execution.
² Talend is an ETL tool whose processes are scheduled and executed from Control-M.