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10 Business Skills Every Data Team Needs

October 2024
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Summary

In the discussion led by Cal Alderby, Head of AI and Data Science at Further, the focus is shifted from purely technical skills to a broader skill set necessary for success in data careers. Underlining the importance of storytelling, empathy, and the ability to translate technical metrics into actionable business outcomes, Cal shared insights from his experience from the start of his company Pandata to its purchase. The conversation discussed how to engage stakeholders effectively, the importance of measuring actual business outcomes rather than just technical outputs, and perfecting the art of selling, even in non-customer facing roles. Additionally, there's an emphasis on the value of consulting skills, the necessity of humility in high-performing teams, and the importance of creating a culture of thought leadership to drive innovation and collaboration.

Key Takeaways:

  • Storytelling is a critical skill that helps in conveying complex ideas simply and effectively.
  • Measuring outcomes rather than output is essential for ensuring AI initiatives deliver tangible business value.
  • Empathy and humility are necessary traits that can and should be cultivated in professional settings.
  • Adopting a consultant mindset can enhance data professionals' effectiveness and client engagement.
  • Creating a rewarding culture around core values improves team cohesion and client trust.

Deep Dives

The Power of Storytelling

Storytelling emerged as a key theme, underlining its role in closing the gap between technical details and stakeholder understanding. Cal Alderby shared a personal anecdote from his early days pitching to venture capitalists, where despite a strong technical present ...
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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.


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