I’m thrilled to sit down with Chloe Maraina, a renowned Business Intelligence expert with a deep passion for transforming big data into compelling visual stories. With her extensive background in data science and a forward-thinking vision for data management and integration, Chloe has become a guiding light for organizations navigating the complex landscape of data governance. In this interview, we explore the critical importance of data governance in today’s tech-driven world, the challenges posed by emerging technologies, and the strategies needed to build a robust data culture. We’ll dive into how leadership, training, and frameworks play pivotal roles in ensuring data quality and compliance while fostering innovation.
What does data governance mean to you, and why do you believe it’s become such a vital focus for organizations in recent years?
Data governance, at its core, is about establishing the policies, processes, and accountability needed to manage an organization’s data effectively. It’s ensuring that data is accurate, secure, and aligned with business goals. Today, it’s more vital than ever because of the sheer volume of data we’re dealing with and the rapid adoption of technologies like AI. Without governance, organizations risk poor data quality, security breaches, and regulatory penalties. It’s not just about control—it’s about enabling trust in data so that every decision, from daily operations to strategic planning, is grounded in reliability.
How do you see data governance intersecting with the rise of technologies like generative AI, especially when it comes to data quality?
Generative AI is a game-changer, but it’s heavily dependent on the quality of the data it’s fed. If the input data is inconsistent or biased, the outputs will be flawed, sometimes in ways that are hard to detect. Data governance steps in by setting standards for data integrity and context, ensuring that AI tools produce meaningful and ethical results. I’ve seen cases where companies skipped these steps, and the AI models amplified errors in their data, leading to costly missteps. Governance acts as the foundation that makes AI not just innovative but also trustworthy.
With the increasing use of shadow AI in workplaces, what challenges does this pose to data security and privacy, and how can governance help?
Shadow AI—when employees use AI tools without oversight—creates significant risks. Data security and privacy are at stake because sensitive information might be processed through unapproved platforms that lack proper safeguards. This can lead to breaches or violations of laws like GDPR. Strong data governance counters this by establishing clear policies on tool usage, access controls, and data handling. It’s about creating visibility and accountability so that even if employees experiment with new tech, there are guardrails in place to protect the organization.
Can you walk us through your approach to crafting a data strategy that aligns with an organization’s broader business objectives?
Developing a data strategy starts with understanding the organization’s mission and goals. I begin by engaging with stakeholders across departments to identify what they need from data—whether it’s better customer insights or operational efficiency. Then, I map out how data can drive those outcomes, focusing on priorities like quality, accessibility, and security. It’s critical to ensure the strategy isn’t static; it must evolve with the business. I build in flexibility by setting regular review points to adapt to market shifts or new technologies, ensuring data remains a strategic asset rather than a liability.
Why is executive buy-in so crucial for the success of a data governance program, and how do you secure it?
Without leadership support, data governance often gets sidelined for flashier projects. Executives control budgets and set priorities, so their understanding and backing are essential to allocate resources and drive cultural change. I’ve seen programs falter when leaders don’t grasp the value—teams lose momentum, and initiatives stall. To secure buy-in, I focus on translating data governance into business terms: showing how it reduces risks, cuts costs, or boosts revenue. Tailoring the message to their pain points, like regulatory fines or missed opportunities, usually gets their attention and commitment.
How significant is ongoing training in building effective data governance, and what kind of programs do you recommend?
Training is absolutely fundamental. Data governance isn’t a one-and-done effort; it requires everyone to be on the same page about processes and responsibilities. Continuous training builds data literacy, helping teams understand not just the ‘how’ but the ‘why’ behind governance. I recommend a mix of foundational courses on data management principles and specialized programs, like certifications in data privacy or AI ethics, especially for complex challenges. Regular workshops or refreshers also help keep skills sharp as regulations or technologies evolve.
What role does cultural change play in successfully implementing data governance, and how do you manage resistance?
Cultural change is often the make-or-break factor. You can have the best policies and tools, but if people don’t embrace them, governance fails. Resistance usually comes from a lack of understanding or fear of added workload. I tackle this by focusing on transparency—explaining why governance matters to their daily work and involving them in the process. Building trust through open communication and small, visible wins, like streamlining a cumbersome report, helps shift mindsets. It’s about showing that governance empowers rather than restricts.
Can you share your process for developing a data governance framework that balances compliance with innovation?
Creating a framework starts with assessing the organization’s current state—looking at data maturity, existing processes, and regulatory needs. I prioritize key elements like clear roles, decision-making rights, and data standards to ensure compliance and quality. But I also design it to support innovation by embedding flexibility, like scalable policies that can adapt to new tools like AI. Collaboration is key; I work with IT, legal, and business units to ensure the framework isn’t a barrier but a facilitator, allowing teams to experiment safely within defined boundaries.
How do you recommend organizations measure the success of their data governance efforts?
Measuring success requires smart metrics that tie directly to business value. I look at indicators like improved data quality—say, fewer errors in reports—or reduced compliance risks, such as passing audits without issues. Financial metrics, like cost savings from streamlined processes, or revenue growth from better insights, also matter. The key is to tailor these metrics to the organization’s goals and maturity level, and to communicate progress in a way that resonates with stakeholders, whether it’s through dashboards or quarterly reviews.
What’s your forecast for the future of data governance, especially with the rapid evolution of AI and data privacy regulations?
I see data governance becoming even more intertwined with AI and privacy in the coming years. As AI adoption grows, governance will need to address not just data quality but ethical usage—ensuring fairness and transparency in AI outputs. Privacy regulations will continue to tighten globally, pushing organizations to be more proactive in compliance. I predict we’ll see more integration of governance with AI oversight, creating unified frameworks that manage both data and technology risks. It’s an exciting time, but it will demand agility and a deeper commitment to building trust in how data is handled.
