Artwork for podcast The Data Storytellers Podcast
Reshaping Game Development through Data-Driven Decisions | Francisco Rius (Head of Data Science and Data Engineering at Minecraft)
4th August 2022 • The Data Storytellers Podcast • The Data Storytellers Podcast
00:00:00 01:00:46

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On this episode of The Data Storytellers podcast, we delve into the vital role of data, technology, and human capital in enhancing video game development. Our guest, Francisco Rius, head of data science at Minecraft, provides a compelling narrative on how advanced data analytics not only shapes better gaming experiences but also serves as a bridge between creative vision and strategic business outcomes. We discuss the intersection of player behavior analysis with game development, highlighting the importance of cross-functional perspectives, collaboration, and ethical considerations in using AI and machine learning to craft some of the most engaging gaming experiences in the industry.

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• 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


00:07 - Introduction to Francisco Rius and his role at Minecraft

00:30 - Background and current focus at Minecraft, including player data analytics

02:03 - Evolution of video game analytics and its impact on game development

07:22 - The balance of creativity and data in game design

11:00 - Comprehensive role of data science in improving player engagement

16:06 - Importance of data-driven insights in maintaining a safe online environment for players

20:55 - Strategies for integrating data science directly with product teams

25:46 - The blend of technical expertise and soft skills in data science

33:10 - Challenges in aligning data science with corporate strategies in large studios

41:45 - The importance of objectivity and avoiding biases in data interpretation

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