{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Data Matas","title":"S1E3 - Building trusted analytics at Potloc with Stéphane Burwash","html":"","width":"100%","height":180,"duration":3271,"description":"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.\nTakeaways\nStéphane started his data engineering journey alone, relying on community support.\nBuilding a community is crucial for learning and growth in data engineering.\nPotloc evolved from a market insights company to a data-driven organization.\nNavigating data engineering challenges requires asking questions and seeking help.\nStakeholder management is essential for successful data projects.\nTechnologies like Meltano and DBT are integral to Potloc's data stack.\nAI is being leveraged to improve data quality and analytics processes.\nSelf-service analytics can empower users but requires careful governance.\nData quality issues often arise from a lack of awareness and communication.\nThe role of a data practitioner is to maintain a big picture perspective.\nSound Bites\n\n\"Ask questions, don't be afraid to learn.\"\n\"Everybody has been in that position.\"\n\"We shouldn't be trying to do the custom solution.\"\nChapters\n\n00:00\nIntroduction and Background of Potloc\n04:43\nRole of a Data Engineer at Potluck\n06:34\nData Sources and Technologies Used\n09:58\nBalancing Complexity and Impactful Work\n15:30\nWorking with BI Analysts and Data Modeling\n23:46\nFocus on Data Quality and Maintenance\n25:42\nChallenges of Data Quality and Data Integrity\n36:12\nThe Importance...","thumbnail_url":"https://img.transistorcdn.com/mWwcfSnLuQLNm6NccVv3MblL5SzvexWb7NRIDCF97xQ/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS82OGQ5/YmQ1NjhhOTVmZGU4/MGQ4NzdiYmZkZDQ2/ZWYxNS5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}