MAD Data Podcast

Chad Sanderson Head of Product and Data Platform at Convoy, a digital freight network that’s solving problems in the $800B trucking industry. Chad has a lot on his plate, including being responsible for Convoy’s Data Engineering team, Data Warehouse, Data Pipeline tooling, Experimentation Platform, Machine Learning Platform, Analytics Platform, and Streaming. Chad swung by to talk about how his team is building one of the most advanced experimentations and machine learning platforms in the world from the ground up.

Colleen Tartow is the Director of Engineering for Starburst Data, a product suite that gives users the ability to self-manage their data infrastructure. We had a great time in the MAD Data studio as she shared insights on how data-driven companies should treat their data as a product and dove into the category of data mesh. Colleen also holds a Ph.D. in astrophysics which makes her truly out of this world!

George Firican is an award-winning data governance leader, founder of LightsOnData, and podcast host of the Lights On Data Show. George swung by the MAD Data studio to talk about what actually makes a company data-driven and why companies need to be more aware of their data quality issues.

Scott Taylor, The Data Whisperer, and Kate Strachnyi, Founder of DATAcated, discuss the relevance and irrelevance of data quality. In order to make data quality achievable, our guests explain that it must begin with a big picture business context.

What new data reality are we anticipating? Sr. Director of Product Management at Google Cloud, Sudhir Hasbe, shares insights from Google customers’ evolving needs. The modern data stack will take on greater variety and tooling must adapt to real-time demands.

As companies scale out their data operations and data demands continue to mount, communications and knowledge alignment form every team’s operational backbone. Castor CTO, Amaury Dumoulin, explains the foundational importance of data catalogs as teams expand in size and knowledge.

Data quality is universally important to data teams but where data quality checks should sit along the data process depends on your business use case. Netflix Sr. Software Engineer (Data Pipelines), Vivek Kaushal, shares his experience on the differences in data quality criteria from his time at Apple, Amazon, and now at Netflix.

How do you ensure data quality when your business relies on data from a high variety of external data sources? Johannes Leppä, Sr. Data Engineer at Komodo Health, shares his insights on how a data-intensive operation can design its data infrastructure to prevent common errors and high-impact issues. Johannes offers his tips for data teams to get ahead of data errors and proactively manage data quality.

Following up on our last episode on data quality, this episode takes aim at a common culture of reactivity in data teams. Gary Cheung, Staff Analytics Engineer at Eventribite, highlights the importance of taking a proactive approach to data quality and why prevention is the cure that every data team needs.

Data quality is the data industry’s holy grail – desired by all but mysteriously elusive. Sam Bail, who speaks frequently on the topic of data quality, joins Sarah Krasnik, Data Engineer at Perpay, to discuss the building blocks, organizational mindset, and strategic planning that are needed to take data quality from theory to practice.