MLOps Weekly Podcast
MLOps Week 23: Data Quality & The Future of DataOps with Maxim Lukichev
Nov 14, 2023
Maxim Lukichev, Co-founder and CTO at Telmai, discusses the importance of proactive data quality, improving collaboration on data teams, treating data as a product, data governance, and the impact of data on analytics, ML, and AI.
27:38
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Quick takeaways
- Data quality is crucial in preventing the corruption of outcomes and ensuring accurate results in data solutions, requiring collaboration among various teams.
- Data observability is important due to the constant stream of data and the need to ensure the reliability and quality of the delivered data, necessitating effective teamwork and communication.
Deep dives
The Importance of Data Quality
Data quality is crucial in preventing the corruption of outcomes and ensuring accurate results in data solutions. Bad data can ruin processes and lead to costly resolutions. Telmine was inspired by the need to prevent bad data from entering systems, rather than fixing it afterwards. The complexity and variety of data make it a challenging problem to solve. Data quality issues span across multiple areas of an organization, requiring collaboration among various teams. Addressing data quality is both a technical and people problem, necessitating effective teamwork and communication.
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