Building Trust and Reputation in Data Science

Key Points of the Episode:

• Innovation Waves: Data mining, big data, real-world evidence, AI, machine learning.

• Hype Management: Handling initial excitement, realistic expectations.

• Pressure on Statisticians: Validation of value, skepticism from stakeholders.

• Rigor and Honesty: Importance of rigorous assessments, clear assumptions.

• Building Trust: Establishing credibility within organizations.

• External Pressure: Influence of consultants and external vendors.

• Practical Examples: Futility analysis, real-world data usage.

• Proactive Approach: Staying ahead of trends, internal and external communication.

• Reputation and Networking: Internal trust, external credibility.

• Implementation Challenges: Balancing innovation with practical application.

• Educational Initiatives: Internal seminars, panel discussions, expert invitations.

• Simple Solutions: Focusing on fundamental, proven methods.

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