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In the digital era, data governance has become a foundational pillar for organizations aiming to maximize the value of their information assets.

Fostering a culture that views data as a strategic asset is therefore an essential prerequisite.

Gustavo Mesa, DAMA Certified Data Management Professional (CDMP) and Data Governance & Data Management Specialist at Quanam, emphasizes this by stating: “Data is a critical resource that must be managed and embedded into all strategic decision-making”.

What does this concept entail?

Formally, data governance refers to the set of processes, policies, tools, and roles that ensure data is managed efficiently, securely, and in alignment with the organization’s strategic goals.

Mesa highlights two key dimensions: “The goal is to create value and protect data. Governance must strike a balance between both objectives”.

Why is it indispensable?

When data is disorganized, duplicated, or lacks reliability, decision-making becomes inefficient and risky.

“Data governance ensures that all teams work with trustworthy, high-quality information”, Mesa explains.

Nicole Halm, Chief Sales Officer at Quanam, notes a shift from centralized reporting to a decentralized model, driven by tools like Power BI, which enable users to create their own reports. In this context, the role of the technology and analytics teams is to ensure data quality for effective self-service. Halm underlines: “Data governance is critical to ensure that this decentralization is accurate and orderly”.

Why is it especially crucial in the utilities sector?

Utilities face both challenges and opportunities across their commercial and operational fronts, where data analysis plays a key role in delivering actionable solutions.

That’s why effective data governance is not just recommended: it’s imperative.

As highlighted by Marc-Eric LaRocque and Mark Peco in the Utility Analytics Institute publication “A Practical Guide for Establishing Data Governance in Utilities”, the benefits span multiple areas:

• Regulatory compliance: Enhances adherence to regulations, reducing legal risks and exposure to penalties.
• Operational efficiency: Enables maximum output with minimal resources, improving the delivery of electricity, gas, or water.
• Innovation and technology integration: Supports the adoption of advanced solutions that modernize operations and address evolving market demands.
• Asset management: Optimizes the lifecycle of critical infrastructure, ensuring long-term performance and durability.
• Customer service: Increases service reliability, streamlines support, and boosts user satisfaction.
• Sustainability and environmental compliance: Reduces environmental impact and ensures adherence to environmental standards.
• Safety: Introduces proactive measures to protect employees, customers, and communities, minimizing operational risks.
• Capital asset management: Optimizes financial resource allocation for infrastructure development, maintenance, and modernization.
• Data ethics: Promotes responsible and fair use of information, minimizing negative impacts and generating social value.

Organizational prerequisites: the foundation for sustainable governance

Before launching a data governance strategy, the organization must meet certain structural, cultural, and human conditions to increase the likelihood of success.

These include executive commitment, a data-driven culture, motivated teams, cross-functional collaboration, and a structure that supports continuous improvement.

Such conditions can be assessed using tools like checklists or diagnostic workshops, which help identify weaknesses, anticipate resistance, and refine the approach before moving forward.

To dive deeper into how to implement a data governance strategy, continue to part 2 of this article, which includes practical steps, common challenges, and real-world use cases.

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