Oak View Group

Rachel Rappe Morfitt

Decision Intelligence Champion

Rachel Morfitt is a Senior Manager of Data Analytics at Oak View Group, global leader in venue development and live entertainment, with a career rooted in sports and entertainment including roles at ESPN, Disney/ESPN Media Networks, the Boston Red Sox and the New Orleans Saints and Pelicans. She brings deep expertise in data analytics, business intelligence, and revenue strategy across the sports and live events industry. Rachel is also an active member of Women in Sports and Events (WISE) Chicago and a Northeastern University Young Alumni Board Chair alumna.

Designing Analytics for the People Who Use It

If I do my job correctly, data is approachable, easy to comprehend and exciting to work with. That belief has shaped how I lead analytics in fast-paced environments where decisions cannot wait for perfect models or pristine dashboards—they need clarity, confidence and context.

In industries like sports and entertainment, the margin for error is small and the pace is relentless. Every event, every guest interaction and every operational decision presents an opportunity to improve performance. Analytics plays a critical role in informing decisions across pricing, staffing and concessions strategy, but its value is only realized when stakeholders can quickly understand and trust what they are seeing.

Too often, data teams over-index on complexity. Sophisticated models, layered dashboards and technical jargon can unintentionally create friction. The result is not better decisions, but slower ones. My focus has been to reverse that dynamic—designing analytics that meet stakeholders where they are, not where we expect them to be.

This starts with understanding the psychology of data consumption. Different stakeholders interpret information in different ways. What resonates with an operations leader may not resonate with a finance executive. There is no single “correct” way to present data and I do not believe any approach is inherently wrong. Instead, the role of a data leader is to synthesize complexity into formats that are intuitive, relevant and actionable for each audience.

Balancing analytical rigor with synthetic clarity is where meaningful impact happens. It is not enough to produce accurate outputs; those outputs must tell a story that drives action. In practice, this means prioritizing usability as much as accuracy—ensuring that dashboards answer questions before they are asked and highlight insights before they are buried in detail.

Where AI Adds Value and Where it Creates Risk

Nowhere is that balance between rigour and clarity more important than in the integration of artificial intelligence. There is a growing urgency to adopt AI-driven capabilities, but there is also a significant risk in doing so prematurely. Applying AI to poorly structured or unverified data can amplify errors at scale, creating more confusion rather than clarity.

 The organizations that succeed will not be the ones with the most complex models, but the ones that make data the easiest to use. 

I firmly believe that AI should only be applied to well-governed, gold-layer data—datasets that are trusted, documented and transparent in how they are produced. Equally important, data teams must remain accountable for validating and operationalizing AI outputs. When AI is deployed without that ownership, organizations risk losing control over the very insights they depend on.

Where I see the most immediate value in AI is not in replacing analysts, but in augmenting them. Capabilities that help troubleshoot code, accelerate development and bridge knowledge gaps across programming languages can significantly improve productivity. These tools empower teams to focus less on syntax and more on solving business problems, which is ultimately where their value lies.

Building Data Cultures That Scales

Another ongoing challenge in scaling analytics is navigating the tension between standardization and creativity. Working across multiple teams and venues, each with its own identity and operational preferences, requires a thoughtful approach. Customization is important, but without a foundation of standardized data models, definitions and best practices, scalability becomes impossible.

My approach has been to lead with standardization as the baseline for partnership. Establishing consistent data structures and reporting frameworks creates a common language across organizations. From there, teams can layer in the creativity and customization that reflect their unique needs. This balance allows for both efficiency and flexibility, ensuring that analytics can scale without becoming rigid.

Building a data-driven culture ultimately comes down to trust and accessibility. Leaders must feel confident in the data, and teams must feel empowered to use it. This requires collaboration across functions, clear communication and a shared commitment to making data part of everyday decision-making.

For professionals looking to grow in this space, technical expertise is only one part of the equation. The ability to translate data into meaningful narratives, understand stakeholder needs and operate within real-world constraints is what differentiates impactful leaders. Data does not exist in isolation—it exists to inform action.

As analytics continues to evolve, the organizations that succeed will not be the ones with the most complex models, but the ones that make data the easiest to use. When data becomes approachable and intuitive, it stops being a resource and starts becoming a competitive advantage.