Building Bridges in Data: The Creation and Impact of Data Champions with Luis from PensionBee
- S2E9
- 16:15
- December 30th 2023
Welcome to Data Team Success, the podcast where data teams come to find out how to build bridges to success.
Your host, Ross Webb, is joined by data architecture virtuoso, Luis, Visionary VP of Data at Pension B.
This episode is proudly brought to you in partnership with Amplitude, leaders in product analytics.
Episode Highlights:
The 720-10 Rule & Reimagining Data Teams
Luis discusses the innovative 720-10 rule for data team management.
Learn how to supercharge your data career and elevate your data team's capabilities.
Data Champions: A Strategy for Scalability
The concept of 'Data Champions' is unveiled, showcasing how to enhance productivity without scaling team size.
Luis shares how Pension B became a data-driven company by empowering each department with data.
The Balancing Act: Development vs Ad-Hoc Requests
Discover Luis's strategy for balancing 70% development with 10% ad-hoc tasks to keep data teams focused on long-term impact.
Understand the importance of not drowning in ad-hoc requests and the significance of project-based mindsets for career growth.
Internal Career Development & Cross-Functional Expertise
Hear about the success stories at Pension B, where team members have transitioned roles, showcasing the versatility and adaptability of the workforce.
Learn how internal career development has positively impacted team dynamics and data insights.
Conclusion:
Luis has provided a masterclass on the role of Data Champions and the management of successful data teams.
Connect with Luis on LinkedIn to continue gaining insights from his wealth of experience.
Acknowledgments:
Special thanks to Amplitude for their support in producing this series.
MLTeam Success
Welcome to ML Team Success — the show for ML engineers, data scientists, and MLOps practitioners who want to actually ship AI that works in production.
I'm Ross Webb. I've led data product teams and ML engineering teams at places like Amazon and Just Eat, building platforms used by thousands of professionals. I've seen what works, what breaks, and why 90% of ML projects never make it to production.
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