Barbara Forth is a seasoned executive who has navigated the high-stakes worlds of financial services and industrial giants, holding leadership roles at firms like Fidelity Investments, Capital Group, and General Electric. With over 30 years of experience, she eventually transitioned into the public sector to become the inaugural Chief Data Officer at William & Mary. Her unique career path has made her an expert in bridging the gap between corporate efficiency and academic mission, focusing on how to make data easier to trust, use, and act on. Today, she shares her insights on the evolving role of the CDO, the importance of data foundations, and the transformative potential of artificial intelligence.
You transitioned from executive roles at major firms like Fidelity and General Electric to being an inaugural Chief Data Officer at a university. What were the biggest cultural hurdles during this pivot, and how did you adapt your strategy to fit an academic environment?
The shift from the fast-paced, profit-driven corporate world to a historic institution like William & Mary required a fundamental change in how I approached influence and consensus. In industry, decisions are often top-down and driven by quarterly financial metrics, but in academia, the culture is deeply collaborative and mission-oriented. One of the biggest hurdles was navigating a landscape where authority is distributed across academic, administrative, and technology leadership. To adapt, I had to stop leading with technology and start leading with the most important questions facing the university’s leadership. Instead of just implementing a system, I spent my first months building relationships and translating academic priorities into clear KPIs that resonated with both faculty and administrators. This pivot was less about being the loudest voice in the room and more about being a facilitator who helps others see the value of a shared data strategy.
Many organizations struggle with fragmented, system-driven environments where data is hard to trust. How do you align people and technology to move toward decision-focused data?
Moving from a fragmented environment to one where data is a trusted asset starts with shifting the focus away from the systems themselves and toward the decisions they are meant to support. My approach begins by gathering executive leaders to define the specific, high-priority questions that need answering, whether they relate to student rankings or athletic performance. Once we have that clarity, we work backward to strengthen the data foundation, which involves improving data quality and establishing clear stewardship protocols. We align people by creating a governance structure where everyone understands their role in the data lifecycle, ensuring that the technology serves the process rather than dictating it. This step-by-step foundation is what makes data reliable enough for advanced analytics and, eventually, the successful implementation of AI.
Establishing a Data Fellows program embeds PhD-level talent directly into analytics teams to solve high-impact challenges. What specific operational problems has this initiative addressed so far?
The Data Fellows program has been a game-changer because it allows us to tap into the incredible intellectual capital already present at the university. By embedding PhD-level talent within our Data and Analytics team, we have been able to tackle complex problems that require deep research capabilities alongside technical execution. We have applied this expertise to high-impact areas such as refining our institutional rankings and optimizing operational challenges within university athletics. Measuring the success of this integration comes down to the speed and quality of our decision-making; we look at how quickly we can turn raw data into actionable insights that administrative and technology leaders can use. It’s about more than just solving a math problem; it’s about creating a bridge between rigorous academic inquiry and the practical needs of university operations.
The role of the Chief Data Officer is evolving rapidly with the rise of artificial intelligence. How should a leader balance long-term data governance with the immediate pressure to deliver AI-driven results?
The pressure to deliver immediate AI results is intense, but as a CDO, you must remain the guardian of the data foundation, because without quality data, AI is just a “black box” that produces unreliable outcomes. I balance this by treating data governance not as a restrictive set of rules, but as an essential prerequisite for any AI project. For example, when we look at implementing AI, we first evaluate the underlying data debt and stewardship to ensure the results won’t be skewed or misleading. I often tell my peers that if you skip the governance phase, you’ll spend three times as much effort later trying to fix the reputational damage of a failed AI model. By focusing on practical approaches to data foundations through my work with the EDUCAUSE group, I ensure that our AI initiatives are built on a bedrock of trust rather than just a desire for a quick win.
Career paths in data management often involve moments of being stuck or needing a significant pivot. What “superpowers” or leadership traits have helped you navigate these transitions?
Navigating a 30-year career across different sectors has required the “superpower” of adaptability and a relentless focus on the “why” behind the data. I’ve had many moments of being stuck, and in those times, I relied on mentors who helped me see that being a leader in data is actually about being a leader in organizational change. One specific mentor taught me that in a complex, multi-departmental organization, your technical expertise is secondary to your ability to build consensus and empathy. That shift in perspective changed how I lead; I stopped trying to solve every problem with a dashboard and started solving them by listening to the pain points of my colleagues. This ability to translate technical challenges into human stories has been the most important trait in making my transitions from financial services to consulting, and finally to higher education, successful.
What is your forecast for the future of data leadership in higher education?
I believe the future of data leadership in higher education lies in the transition from being data “providers” to being strategic “enablers” who sit at the very center of the university’s mission. As AI becomes more integrated into every aspect of the campus—from personalized student learning to predictive financial modeling—the CDO will need to evolve into a role that is as much about ethics and policy as it is about architecture. We will see a shift toward more collaborative, cross-institutional data standards, much like the work we are doing in the EDUCAUSE Chief Data Officer group. Ultimately, the leaders who thrive will be those who can maintain a rigorous data foundation while moving at the speed of institutional need, ensuring that data is not just a collection of numbers, but a catalyst for better, faster decisions that benefit students and faculty alike.
