I’m thrilled to sit down with Chloe Maraina, a Business Intelligence expert whose passion for creating compelling visual stories through big data analysis has made her a visionary in data management and integration. With her deep expertise in data science, Chloe offers invaluable insights into the evolving landscape of data governance. In this interview, we dive into the critical importance of data governance in today’s world, explore best practices for building robust programs, and discuss how organizations can navigate challenges like AI-driven risks and cultural resistance while driving innovation through strategic data oversight.
How has the rapid adoption of technologies like generative AI highlighted the urgency of strong data governance in organizations?
The surge in generative AI tools has really put data governance under the spotlight. Employees are using these tools for everyday tasks, often without proper oversight, which can lead to what we call “shadow AI.” This unchecked usage opens up serious risks around data security and privacy, not to mention potential violations of regulations like GDPR or the California Consumer Privacy Act. Without solid governance, organizations can’t ensure the quality or context of the data feeding into these AI systems, which often results in flawed outputs or ethical concerns. It’s a wake-up call that governance isn’t just a nice-to-have—it’s essential for managing these emerging technologies responsibly.
What are some of the biggest risks associated with poor data quality when it comes to AI projects, and how can governance address them?
Poor data quality can completely derail AI projects. If the data going into an AI model is incomplete, inconsistent, or biased, the results will be unreliable at best and harmful at worst. This can lead to bad business decisions, eroded trust, and even legal issues if the outputs violate privacy or ethical standards. Data governance steps in by establishing clear controls and oversight—think data quality checks, standardized processes, and accountability structures. It ensures that the data fueling AI is accurate, relevant, and ethically sourced, which is critical for both innovation and risk mitigation.
When building a data governance program, why is it so important to start with a clear data strategy rather than jumping straight to tools and technology?
Starting with a data strategy is like plotting your course before setting sail. Too often, leaders think the latest shiny tool will solve all their data problems, but without a strategy, they’re just throwing money at tech without direction. A data strategy acts as your north star—it aligns governance efforts with business goals, identifies what data matters most, and defines how to measure success. It ensures you’re not just collecting data for the sake of it but using it to drive value. Only after this foundation is set should you consider tools, and even then, they need to support the strategy, not dictate it.
How does having a well-defined data strategy contribute to a better return on investment for organizations?
A solid data strategy directly ties data initiatives to business outcomes, which is how you maximize ROI. It helps prioritize where to focus resources—whether that’s improving data quality for better decision-making or streamlining compliance to avoid fines. By mapping out how data supports specific goals, like increasing revenue or cutting costs, organizations can measure tangible impacts. It also prevents wasted spend on unnecessary tools or projects that don’t align with priorities. Essentially, it’s about making sure every data effort delivers real value, not just theoretical benefits.
Why is continuous training such a cornerstone of successful data governance, and what skills should teams focus on developing?
Data governance isn’t a one-and-done deal; it’s a living process that evolves with technology and regulations. Continuous training keeps everyone on the same page and builds the skills needed to handle complex challenges, like protecting privacy in AI projects or staying compliant with new laws. It also boosts data literacy across the organization, which is crucial for adoption. Teams should focus on skills like understanding governance frameworks, data ethics, and practical applications of standards. Certifications like the CDMP or courses on the Data Management Body of Knowledge (DMBOK) can be incredibly helpful for building that expertise.
How critical is executive buy-in for the success of a data governance program, and what happens without it?
Executive buy-in is make-or-break for data governance. Senior leaders control funding and set the tone for organizational priorities. When they understand and champion governance, it gets the resources and visibility it needs to thrive. Their support also signals to the rest of the company that this matters, which helps with adoption. Without it, governance often gets sidelined for flashier projects. You might see underfunded initiatives, lack of accountability, or just plain resistance because there’s no top-down push. It’s a recipe for a program that fizzles out before it can deliver results.
Can you explain what a data governance maturity assessment is and why it’s so valuable for organizations?
A data governance maturity assessment is essentially a diagnostic tool. It evaluates where an organization stands in terms of its governance capabilities—looking at processes, policies, roles, and data quality controls. It helps identify strengths, gaps, and realistic goals for improvement. The value lies in creating a shared understanding across teams about where you are and where you need to go. It’s not just a one-time thing either; as the organization grows or as new challenges like AI emerge, revisiting the assessment ensures your governance evolves to stay relevant and effective.
Why is it often more effective to build on existing resources and processes for data governance rather than starting from scratch?
Building on what’s already in place is a non-invasive way to gain traction. Most organizations are already doing some form of data management, even if it’s informal—think team members who naturally define or handle data as part of their roles. Recognizing these folks as data stewards or owners formalizes their contributions without reinventing the wheel. It’s less disruptive, builds on existing trust, and leverages current strengths. Starting from scratch, on the other hand, can feel overwhelming and often meets more resistance because it seems like a massive, unnecessary overhaul.
How can treating data governance as a service, similar to HR or finance, shift the way organizations approach it?
When you think of data governance as a service, it reframes it as a core organizational capability, not just a tech project or compliance chore. Like HR or finance, it becomes a support function that touches every part of the business with consistent standards, tools, and policies. This mindset shifts the focus to enabling teams—providing templates, guidance, and processes that make data management easier, not burdensome. It also helps position governance as a strategic asset, integral to operations and innovation, rather than an afterthought or a box to check.
What are some of the biggest cultural challenges you’ve seen when implementing data governance, and how can they be overcome?
Cultural resistance is often the biggest hurdle. People might not see why governance matters, or they fear it’ll add bureaucracy to their work. Sometimes, there’s a lack of trust—folks worry about losing control over “their” data. Overcoming this starts with clear, transparent communication about the “why” behind governance—how it improves efficiency, reduces risks, and supports their goals. Building trust is key, whether through involving teams in decision-making or showing quick wins, like better data access. It’s about making governance a collaborative effort, not a top-down mandate.
What’s your forecast for the future of data governance, especially with the growing integration of AI and other emerging technologies?
I see data governance becoming even more intertwined with AI and emerging tech over the next few years. As AI adoption grows, governance will need to focus heavily on ethical data use, bias mitigation, and transparency in AI outputs. We’ll likely see tighter regulations around data privacy and AI accountability, pushing organizations to integrate governance directly into tech development cycles. I also think we’ll see governance evolve into a more proactive, predictive function—using analytics to spot risks before they happen. It’s an exciting time, but it’ll demand agility and a commitment to continuous learning to stay ahead of the curve.
