S1E3 - Building trusted analytics at Potloc with Stéphane Burwash

Data Matas

In this episode of Data Matas, host Aaron Phethean and his guest Stéphane Burwash dive deep into what it takes to build a true data-driven culture. Recently promoted to Data Engineering Lead at Potloc, Stéphane shares his thoughts on building trusted analytics, where quality data is at the foundation. The conversation digs into the hot topics of AI and self-service analytics - and questioning their relevance - as well as the application of modern technologies such as Meltano and BigQuery and "the separation of church and state" in the data space. Not only that but the two touch on the importance of the people element and emphasise the need for open and honest stakeholder management in an organisations journey to data excellence.

Takeaways

• Stéphane started his data engineering journey alone, relying on community support.
• Building a community is crucial for learning and growth in data engineering.
• Potloc evolved from a market insights company to a data-driven organization.
• Navigating data engineering challenges requires asking questions and seeking help.
• Stakeholder management is essential for successful data projects.
• Technologies like Meltano and DBT are integral to Potloc's data stack.
• AI is being leveraged to improve data quality and analytics processes.
• Self-service analytics can empower users but requires careful governance.
• Data quality issues often arise from a lack of awareness and communication.
• The role of a data practitioner is to maintain a big picture perspective.

Sound Bites

• "Ask questions, don't be afraid to learn."
• "Everybody has been in that position."
• "We shouldn't be trying to do the custom solution."

Chapters

00:00
Introduction and Background of Potloc

04:43
Role of a Data Engineer at Potluck

06:34
Data Sources and Technologies Used

09:58
Balancing Complexity and Impactful Work

15:30
Working with BI Analysts and Data Modeling

23:46
Focus on Data Quality and Maintenance

25:42
Challenges of Data Quality and Data Integrity

36:12
The Importance of Stakeholder Engagement

41:14
The Concept of Self-Serve Analytics

43:25
The Value of a Holistic Understanding of Data

47:14
The Role of Data Practitioners

48:15
Introduction

49:24
The Value of Online Communities and Asking Questions

50:22
Overcoming the Fear of Feeling Lost

50:48
The Generosity of the Data Community

52:10
Networking and Learning at Meetup Events

53:21
Building Connections and Getting Insights