Overcoming Resistance in Data Science Through Strategic Influence | Dave Coughlin

Here we explore how to transform data insights into compelling business narratives to achieve executive buy-in. By leveraging strategic communication and understanding cognitive biases, we discuss practical techniques to enhance data-driven decision-making. The conversation delves into the integration of cognitive science into data analytics, providing actionable strategies to overcome resistance and build trust within organizations.

Connect with us:

• Website: https://thedatastorytellers.com/

• LinkedIn: https://www.linkedin.com/company/the-data-storytellers

• Spotify: https://open.spotify.com/show/2N0vZtHZHgod4Tll2LX2xa

• YouTube: https://www.youtube.com/channel/UCz9e56lhYUfORiOHMiLlPmA

• Apple Podcast: https://podcasts.apple.com/gb/podcast/the-data-storytellers-podcast/id1493766476

Chapters:

00:00 - Introduction to the podcast and today's theme

00:43 - Discussing the relevance of cognitive biases in data science

03:24 - Overview of McRaney's work on cognitive biases

06:25 - The importance of persuasion as a form of learning

08:35 - Strategies for updating models and overcoming resistance

13:47 - Choosing the right models to update and when people are open to change

17:48 - The significance of asking questions in data communication

22:50 - Preparing for truth battles and understanding different perspectives

27:30 - Identifying and addressing cognitive biases in corporate settings

31:34 - The role of frequency in shaping beliefs and updating models

36:11 - Building consensus and handling neutral or adversarial stakeholders

41:04 - The importance of multiple champions in driving data initiatives

46:17 - Navigating second chances and building support within the organization

52:30 - Practical applications of cognitive science in data roles

59:15 - Real-world examples of successful data storytelling

1:08:42 - Strategies for effective communication in high-stakes meetings

1:16:57 - Leveraging emotional intelligence in data-driven environments

1:24:45 - Final thoughts and concluding remarks