Lecture: Working with Stakeholders in Data Roles

Introduction

• Importance of working effectively with stakeholders in data roles.
• Stakeholders use data for business decisions.
• Key to driving change within a company.

Why Stakeholder Collaboration is Essential

• Data analytics work is crucial for informed business decisions.
• Lack of collaboration renders data efforts meaningless.
• Communication skills are critical, harder to teach than technical skills.
• Importance of learning communication through feedback and observation.

Types of Stakeholders

• Analytics engineers work with data analysts, marketers, sales teams.
• Example: Collaborating on revenue data models.
• Stakeholders request metrics, which are integrated into tools like HubSpot.

Stakeholder Engagement

• Communication involves translating business needs into data models.
• Balancing technical and non-technical communication.
• Executive vs. technical stakeholder communication nuances.

Common Pitfalls in Stakeholder Management

• Overcommitting to stakeholder requests leads to burnout.
• Importance of saying "no" and managing expectations.
• Use of project management tools to prioritize tasks.

Tips and Tricks for Effective Stakeholder Management

• Ask questions to clarify true needs.
• Avoid being reactive; understand the problem before acting.
• Use stakeholder feedback to refine communication.
• Avoid becoming a "yes person"; focus on business value.

Handling Difficult Stakeholders

• Managing stakeholders with unclear or changing requirements.
• Importance of documenting needs and setting expectations.
• Transparency and trust-building through honesty about mistakes.

Dealing with Conflicting Stakeholder Priorities

• Use business value to resolve conflicting priorities.
• Emphasize shared goals of business success.

Project Management and Scope

• Use of definitions of "done" and clear MVP guidelines.
• Prioritization of core functionalities over "nice-to-haves."

Stakeholder Presentations

• Focus on concise presentation and let questions lead.
• Communication through project updates and milestones.

Leveraging Non-Technical Backgrounds

• Use diverse perspectives to enhance data work.
• Non-technical backgrounds offer unique strengths.

Key Skills for Data Roles

• Importance of SQL, data modeling, and data warehousing.
• Understanding of data engineering and analytics engineering roles.

Additional Resources

• Subscribe to Madison's newsletter: Learn Analytics Engineering.
• Follow on LinkedIn for more insights.

Closing Remarks

• Encouragement to engage in stakeholder communication skill development.
• Reference to participating in Maven Analytics events for further learning.