Join Ross Webb as he embarks on an enlightening conversation with Panos Lazardis, Senior Data Product Manager at The Economist, in this special episode brought to you in partnership with Amplitude. Panos shares his invaluable insights on data product best practices, the challenges faced in this field, and the future of data product management.

0:00 Introduction
1:10 What are data product best practices?
User-centricity, governance, quality, and agile development are key pillars in data product management. Panos shares the importance of focusing on user problems and decision-making while ensuring data integrity, consistency, security, and privacy.

4:23 What are the biggest challenges in data products?
Panos breaks down the challenges based on different types of data product management:
- Data Platform Product Management: Estimating the value of infrastructural growth
- Analytics Product Management: Navigating stakeholder opinions and finding the right solutions
- AI and Machine Learning Products: Balancing R&D efforts with tangible outcomes

8:23 What challenges have your course students overcome?
Panos's course has helped technical professionals understand the impact of their work on the organization, while business and product managers gain insights into how data products are created to solve their pain points. The course has improved communication between data teams and product managers.

11:16 What do you see in the future of data product management?
- Linking the value of data teams' efforts to quantifiable outcomes
- Increased focus on data quality, especially with the rise of AI
- The growing importance of data ethics in an AI-driven world

As the episode concludes, Ross encourages viewers to connect with Panos on LinkedIn and continue exploring the world of data product management. Stay tuned for more insightful interviews that will guide you on your path to success in this dynamic field.

Thank you for joining us on this journey, and a special thanks to our partners at Amplitude for making this episode possible. Until next time, keep learning and growing in your data product management career.