Chloe Maraina is a leading voice in the evolution of business intelligence, possessing a unique ability to translate complex data science into actionable, human-centered narratives. With a career rooted in the strategic integration of data management and a forward-looking vision for technical ecosystems, she bridges the gap between raw information and organizational integrity. In this conversation, we explore the shifting landscape of data governance, focusing on how ethical frameworks and privacy principles are no longer just regulatory hurdles but the very bedrock of innovation. We delve into the necessity of moving beyond a simple compliance mindset, the importance of diverse expertise in shaping AI oversight, and the practical ways leaders can foster genuine trust in an increasingly automated world.
As organizations race to adopt AI, there is often a tension between rapid innovation and the rigorous demands of data governance. How can leaders shift their perspective to see ethical oversight not as a bottleneck, but as a fundamental driver of trust and long-term success?
The shift from seeing governance as a “checkbox” to viewing it as a foundation requires a fundamental change in organizational culture. When we look at the work being done by experts like Katrina Ingram, who was recognized as one of the 100 Brilliant Women in AI Ethics, we see that the most successful programs are those that bake ethics into every layer of the design process. It is about moving away from a reactive posture where you are simply trying to avoid a fine and moving toward a proactive stance where you are building a reliable data ecosystem. This deliberate approach ensures that when you deploy an AI system, your customers and stakeholders feel a sense of security because they know the underlying data has been handled with integrity. In the long run, this creates a more resilient business model that can weather the storms of shifting global regulations because the core principles of privacy and transparency are already in place.
With the complexity of modern data sets, the “human element” in governance is more critical than ever. How does the deep experience of seasoned professionals help navigate the nuances of privacy and policy that automated tools might miss?
Automated tools are excellent for scale, but they lack the historical context and nuanced judgment that a professional with over 20 years of experience, like Mark Horseman, brings to the table. Data management is as much about people and processes as it is about technology, and a CDMP practitioner understands that the “why” behind data usage is often more important than the “how.” These veterans have seen the evolution of data quality and master data management since the early 2000s, giving them a unique perspective on how small ethical lapses can snowball into massive liabilities over time. They can identify the subtle ways that bias might creep into a model or recognize when a privacy policy doesn’t quite align with the lived reality of the users. This human oversight acts as a necessary bridge, ensuring that as we scale our technical capabilities, we don’t lose sight of the ethical obligations we have to the individuals whose data we are processing.
Education is a vital component of this ethical shift, as seen with the development of specialized credentials in the field. Why is it so important for organizations to invest in formal ethics training, and what impact does this have on the development of AI systems?
The creation of Canada’s first AI ethics micro-credential at Athabasca University highlights a growing recognition that we need a standardized language for discussing these challenges. When a team has a shared understanding of ethical AI design, they are better equipped to identify risks early in the development lifecycle, which saves both time and resources. It’s about empowering every person in the organization—from the data scientist to the executive—to speak up when something feels off, backed by a formal framework of knowledge. This educational foundation transforms ethics from an abstract concept into a practical toolkit that can be applied to real-world data strategies. By investing in this level of training, a company signals to its employees and its clients that it is committed to doing things the right way, rather than just the fast way.
Data storytelling is often discussed in the context of sales or marketing, but it has a massive role to play in governance. How can leaders use insight-driven narratives to gain buy-in for complex governance initiatives across a large enterprise?
Governance can often feel dry or overly technical to those outside the data department, which is why storytelling is such a powerful tool for influence. By framing governance as a “business enabler” that drives ROI and fuels innovation, leaders can make a compelling case for why these programs deserve funding and attention. You have to be able to show, through concrete examples, how better data quality or more transparent privacy policies directly lead to better customer experiences and more reliable business outcomes. It is about taking the complex intersections of regulatory requirements and ethical design and turning them into a narrative that resonates with stakeholders at every level. When people understand that governance is the engine that makes high-quality AI possible, they are much more likely to support the initiatives necessary to keep that engine running smoothly.
As we look toward the next few years, specifically with major industry gatherings like the one in Providence, Rhode Island, in 2026, how do these physical connections and hallway conversations shape the future of the industry?
There is something irreplaceable about the energy of an in-person event where online conversations finally turn into real-world breakthroughs. While we can learn a lot from webinars and digital series, the collaborative breakthroughs often happen in those unplanned moments between sessions when practitioners share their “war stories” from the frontlines. In 2026, as we gather to discuss the wrap-up of the previous years’ lessons, those connections will be what truly drive the industry forward. We are moving toward a future where governance at scale is the norm, and being able to sit down with a peer and discuss how they handled a specific cybersecurity alignment or a reference data challenge is invaluable. These gatherings provide a sense of community for privacy professionals and data leaders who are often working in silos, reminding us that we are all part of a larger movement toward a more ethical digital world.
What is your forecast for the state of data governance by the end of 2026?
By the end of 2026, I anticipate that the line between “AI governance” and “general business strategy” will have nearly vanished. We will see organizations moving away from siloed privacy departments and toward a model where ethical oversight is a decentralized responsibility shared by every department. The lessons we are learning now—from the importance of master data management to the necessity of automated oversight—will have matured into standardized global frameworks that make compliance much more intuitive. We will likely see a significant rise in the number of certified ethics professionals within the corporate sector, as the micro-credentials being developed today become the industry standard for hiring. Ultimately, the companies that will be leading the market in 2026 are the ones currently doing the hard, manual work of building trust through transparent, ethical data practices, proving that integrity is the ultimate competitive advantage.
