In this episode of Data Team Success, we sit down with Annarita Roscino, Global Head of Data and Insight at Zurich Insurance Company. With over 20 years of experience in data leadership, Annarita shares invaluable insights on building and developing successful data teams, bridging technical and business perspectives, and navigating the complexities of ethical decision-making in data projects.

Guest Introduction

Annarita Roscino brings a wealth of experience to the table, having set up data teams from scratch throughout her career. Her journey from academia to industry leadership provides a unique perspective on the evolution of data roles and the challenges of building effective teams in the corporate world.

Key Insights

Building and Developing Data Teams

1. Diversity is Key: Annarita emphasizes the importance of building diverse teams, bringing together people from various backgrounds:

• Academia (PhDs)
• Business professionals with a passion for data
• Young talents from apprenticeship schemes
• Individuals with unconventional backgrounds (e.g., philosophy graduates with a knack for data)

1. Balancing Skills:

• Technical expertise is crucial, but commercial skills are equally important.
• The team needs to work at a business pace, not an academic one.

1. Evolving Team Structure:

• Started with data analysts, then data scientists.
• Recognized the need for data engineers, data quality experts, and data architects.

1. Hiring Beyond the CV:

• Look for passion and practical application of skills.
• Example: Hiring a philosophy graduate who built football prediction models in their spare time.

Bridging Technical and Business Perspectives

1. Alignment with Business Goals:

• Ensure data strategies are driven by business needs and pain points.
• Start with understanding the business strategy, then develop a data strategy to support it.

1. Collaboration is Crucial:

• Involve business stakeholders throughout the model-building process.
• Create transparency and build trust in data-driven decision making.

1. Quantifiable Impact:

• Tie data projects to tangible business outcomes.
• Example: Recovering costs from catastrophe claims to fund the entire data project.

1. Cross-functional Communication:

• Create frameworks that facilitate conversation between data experts and business professionals.
• Focus on creating a shared language and understanding.

Ethical Decision Making in Data Projects

1. Beyond Regulations:

• Ethics in data is about what you should do, not just what you can do legally.
• Example: The UK's GCSE grading model during COVID-19.

1. Questioning the Use of Data:

• Just because you can build a model doesn't mean you should.
• Consider the broader implications and potential biases.

1. Transparency and Explainability:

• Ensure models are transparent and understandable to those using them.
• Be aware of potential biases and work to mitigate them.

1. Ethical Framework:

• Develop a framework to assess and highlight potential ethical issues before deployment.
• Regularly review and update ethical guidelines as technology and society evolve.

Key Takeaways for Data Management Leaders

1. Cultivate Diversity: Build teams with varied backgrounds and skillsets to drive innovation and problem-solving.
2. Bridge the Gap: Create processes that facilitate ongoing communication between data teams and business units.
3. Focus on Impact: Align data strategies with clear business outcomes and quantify the value of data projects.
4. Ethical Consideration: Develop robust ethical frameworks and continuously question the implications of data use.
5. Adaptability: Stay flexible in your approach to team building and project management as the field of data analytics continues to evolve.

By implementing these strategies, data management leaders can build more effective teams, deliver greater value to their organizations, and navigate the complex ethical landscape of modern data analytics.