Sean MacCarthy, VP of Analytics, Camping World/Good Sam
MacCarthy is a Strategy and Insights executive dedicated to creating profitable solutions across industries by leading data science and analytics teams. He equips end users with valuable insights, actionable recommendations and AI/ML tools that automate data-intensive tasks, driving efficiency and decisionmaking.
Insights about Professional Journey and Current Responsibilities
I began my career as a philosophy professor but transitioned to the corporate sector to support my growing family, where compensation was more competitive. I was fortunate to join a company with advanced data practices, where I learned from experts across finance, operations, data analytics and marketing. Every project was data-driven, requiring proof through operational or financial metrics. Since then, I’ve held roles in various sectors—supply chain, B2B, delivery, retail, pricing and customer loyalty—always focused on leveraging data to enhance company performance and customer service.
At Camping World, we offer a complete lifestyle experience for RV enthusiasts, from selling and servicing RVs to providing essential insurance, financing and travel resources. Joining Camping World and Good Sam has allowed me to expand from traditional retail to a model more akin to the auto industry.
Currently, as VP and Head of Analytics and Data Science, I lead data initiatives across the business and collaborate with IT and business partners in our Good Sam Protection and Travel products as well as our retail and RV Sales and Service groups. Our team, in collaboration with our IT partners, is modernizing our data infrastructure and tackling legacy systems. Our goal is to build a 360-degree view of each customer’s journey—from purchasing and servicing RVs to finding insurance, financing and travel resources to serve their evolving needs.
Democratizing Analytics: Balancing Accessibility with Governance
I was fortunate to join an organization with a culture of empowerment, even back in the “Excel days.” Here, it was normal for operators and marketers to seek out the information they needed to drive decisions independently. When we started working with our IT partner already implementing Power BI and Snowflake, we were able to enhance that culture by providing the tools to ensure data consistency and accuracy.
In Excel, everyone applied their own filters or embedded queries, which often led to discrepancies in definitions—like different interpretations of “sales.” Centralized reporting in Power BI allowed us to address this by standardizing definitions and creating data norms, ensuring that everyone was working with consistent insights tailored to their roles. The key challenge was capturing the same business insights that people were used to getting from Excel and informal discussions. It was critical that our analytics efforts truly served the business’s needs, not just by delivering visually appealing reports, but by representing the deeper knowledge embedded in the organization’s practices.
Data and analytics must align with business objectives and reflect the organization’s unique insights. To achieve this, we worked closely with the business to understand their goals, existing data and knowledge gaps, integrating these insights into our data models and semantic layers. This approach ensured that new or transitioning team members could access the organization’s knowledge within the data itself.
In the long run, this investment in structured, codified business logic has paid off significantly. It allows us to adapt seamlessly to changes like shifting marketing attribution models. With the right governance, tools and foundational knowledge captured within our systems, we can pivot or scale as the business evolves, efficiently and effectively.
Ensuring Fair and Unbiased AI in Critical Applications
Bias in AI typically falls into two categories – regulatory and data-related. Regulatory bias refers to biases subject to legal requirements, which demand strict compliance with industryspecific rules. For example, fields like financial services, insurance or hiring must align with relevant national, state or local laws. To stay compliant, companies need to collaborate with legal counsel and monitor any legislative changes that may impact their AI models.
Embedding analytics within the business isn’t just a best practice—it’s how insights become action. Proximity to the teams that act on data ensures relevance and speed
The second type, data bias, arises when models are trained on incomplete or irrelevant data, affecting accuracy and outcomes. Even when regulations don’t apply, the quality of data remains critical. For instance, a churn prediction model without data on call center interactions (like call duration, hold times or customer sentiment) might accurately predict customer churn but fail to pinpoint the real reasons, leaving businesses without effective strategies to reduce churn. Addressing AI bias, therefore, requires legal compliance and ensuring comprehensive, well-governed data that aligns closely with the business outcomes you aim to influence.
The Future of Analytics in Enterprise Tech
The future of AI in business hinges on data quality. Companies investing in AI without first ensuring robust, well-governed data foundations are unlikely to see meaningful returns. As businesses increasingly recognize this, we’ll see significant investments in data enrichment, governance and cleansing. With high-quality data, AI models—especially mixed models like large language models (LLMs)—will soon be capable of answering complex business questions in minutes, a task that traditionally required days or weeks of analysis.
In the next five to ten years, businesses with strong data foundations will gain a substantial edge. Executives, marketers and analysts will be able to ask AI engines targeted questions like identifying causes of a customer segment’s decline or spotting inefficiencies and receive immediate, data-backed insights. This quick access to insights will accelerate the testand-learn cycles essential to problem-solving and strategy.
In short, AI’s potential to drive business transformation depends on data. Companies building clean, structured, and well-maintained data infrastructure today will be the ones reaping the rewards of advanced, actionable AI insights tomorrow.
Aligning Analytics with Business Goals across Departments
Effectively supporting business operations requires an analytics team embedded within the business to participate in reviews and align closely with business goals—much like a marketing department. Being involved in quarterly or monthly reviews allows the team to understand business needs, helping drive actionable insights that align with operator goals.
On a tactical level, I’ve found setting up regular “study halls” is invaluable. These sessions, held bi-weekly or monthly, give business partners an open space to ask any data-related questions. It could range from basic Excel functions, like VLOOKUPs, to more complex analytics for new product launches. This accessibility builds trust and reinforces the analytics team as a valuable partner.
Building trust is essential, as analytics often require insights into operational details. With established trust, the team is kept informed about key changes, like power outages affecting store performance, reducing lag in understanding data anomalies. By participating in regular business reviews and holding open sessions, analytics teams can stay connected, anticipate issues and provide more responsive, insightful support.
Advice for Senior Leaders and Upcoming Professionals
The internet is a powerful resource for learning coding and data analytics, even if you have no formal background. Online coding classes, forums and tools like ChatGPT can quickly help you go from zero to proficient. My own journey is proof—I was a philosophy professor with no meaningful analytics/coding background, yet I learned much of the technical skills I needed quickly from with the availability of online resources, help from colleagues, and applying logic from my teaching experience. There’s a thriving online community where you can learn and grow by accessing real-world data sets (like public health or sports data) for hands-on practice, which is typically messier than classroom examples, and thus closer to business data sets.
As you build your skills and career, remember that business impact is the key. Whether you’re saving costs, generating revenue or saving time, always link your work to business value. While you may not directly create or sell a product, your role is to support those who do, driving business success through data and analytics. This focus on delivering measurable results is essential for career growth.