About this Video

• Video Title: Data Literacy: The Key to Successful Project Management in the AI Age | Episode 485
• Channel: CorneliusFichtner
• Speakers: Cornelius Fichtner, Marcus Golos
• Duration: 01:06:51

Introduction

This episode discusses data literacy's crucial role in successful project management, especially in the context of artificial intelligence. The conversation aims to explain data literacy, its impact, and how to improve data literacy skills for project managers. A guest expert, Marcus Golos, shares insights and practical advice.

Key Takeaways

• Data Literacy Defined: Data literacy is the ability to understand, use, and communicate data effectively, encompassing analysis, interpretation, data-driven decision-making, and clear reporting to stakeholders. It involves connecting both digital and non-digital data points.
• Impact of Data Literacy: Data literacy is increasingly vital as organizations become more data-driven. Project management, often lagging, needs to catch up to leverage data effectively for better decision-making and improved project outcomes.
• Data-Driven Project Management: Building a data culture is critical, requiring diligent data production, sharing, and maintaining high data quality (both content and format). Transparency and truthfulness are essential to overcome biases in project reporting.
• AI's Role: AI is not a simple off-the-shelf tool; it requires a strong data foundation and data literacy to use effectively. AI should be seen as a team member, augmenting, not replacing, project managers. Human skills like emotional intelligence remain irreplaceable.
• Upskilling: Improving data literacy involves using tools like Excel and Power BI, asking insightful questions (including "why"), and fostering a culture of learning from both successes and failures. The five steps outlined are: treat data as an asset, build trust and transparency, build insights, make data-informed decisions, and reinforce data culture.

Based on the provided transcript, the key skills and mindset that project managers need to cultivate to become more data-oriented include:

• Transparency: Creating an open environment where failures are discussed as learning opportunities.
• Learning: Cultivating a learning culture to constantly learn from insights gained from data analytics and AI. This includes sharpening intuition through data exposure.
• Authentic Leadership: Leading by example, showing that it's acceptable to make mistakes and learn from them.
• Curiosity: Exploring the unknown, viewing uncertainty as an opportunity for exploration, and not just a threat to mitigate.
• Critical Thinking: Developing skills in questioning and critically evaluating data and insights. Using methods like pre-mortems to anticipate and address potential problems.

According to the speaker, the two key takeaways from the conversation are:

1. Start with the basics: Before jumping into AI and advanced data analytics, focus on fundamental data literacy skills. Don't rush into AI implementation without a solid data foundation. Get the "driver's license" before driving the "AI car".

2. Embrace openness and a data culture: Foster transparency, truthfulness, and a willingness to be challenged by data. Learn from both successes and failures. The ability to be challenged and learn from the challenges is essential for growth in a data-driven environment.