10 Business Skills Every Data Team Needs

ation, the message was lost on the audience. "I really like you, but I have no idea what you just said," was the feedback from a venture capitalist, which sparked Cal's dedication to mastering storytelling. By using relatable analogies and humor, data professionals can engage non-expert audiences, facilitating clearer communication and creating stronger stakeholder relationships. This skill is not only beneficial for external pitches but also key for internal team dynamics, enabling more effective collaboration and decision-making.

Measuring Outcomes vs. Output

Cal stressed the importance of focusing on outcomes rather than outputs when evaluating the success of AI projects. This approach shifts the focus from mere efficiency metrics to the actual impact on business goals, such as customer engagement or clinical outcomes. He illustrated this with a case study where a theoretically superior model failed in practice due to human factors and lack of trust. By adjusting the interaction between humans and AI, rather than the model itself, the team achieved a substantial improvement in business performance. This example highlights the need for AI projects to be evaluated on their ability to drive meaningful changes and align with broader organizational objectives.

Embracing Empathy and Humility

Empathy and humility were highlighted as necessary skills that can be developed over time. Cal discussed the importance of understanding stakeholders' perspectives and the value of humility in high-performing teams. He shared strategies for practicing empathy, such as avoiding jargon and ensuring clear communication even among experts. Humility, on the other hand, involves acknowledging the limits of one's knowledge and remaining open to learning. These traits are particularly important in data teams where complex problem-solving often requires collaboration and the integration of diverse viewpoints. By cultivating these skills, professionals can enhance their interpersonal effectiveness and foster a more inclusive and innovative work environment.

Consulting Skills for Data Professionals

Cal advocated for data professionals to adopt a consulting mindset, which involves active listening, relationship building, and strategic negotiation. These skills enable data scientists to better understand client needs, communicate value propositions, and manage the trade-offs inherent in AI projects. He recounted how adopting a structured discovery and design process transformed his consulting business, leading to more successful client engagements. By saying no to poorly defined projects and focusing on understanding client requirements upfront, Cal was able to secure larger, more impactful projects. This approach not only improves project outcomes but also helps data teams position themselves as strategic partners rather than just service providers.