Episode description
Episode description
SummaryIn this episode of the Data Engineering Podcast Dan Bruckner, co-founder and CTO of Tamr, talks about the application of machine learning (ML) and artificial intelligence (AI) in master data management (MDM). Dan shares his journey from working at CERN to becoming a data expert and discusses the challenges of reconciling large-scale organizational data. He explains how data silos arise from independent teams and highlights the importance of combining traditional techniques with modern AI to address the nuances of data reconciliation. Dan emphasizes the transformative potential of large language models (LLMs) in creating more natural user experiences, improving trust in AI-driven data solutions, and simplifying complex data management processes. He also discusses the balance between using AI for complex data problems and the necessity of human oversight to ensure accuracy and trust.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I'm interviewing Dan Bruckner about the application of ML and AI techniques to the challenge of reconciling data at the scale of business
- Introduction
- How did you get involved in the area of data management?
- Can you start by giving an overview of the different ways that organizational data becomes unwieldy and needs to be consolidated and reconciled?
- How does that reconciliation relate to the practice of "master data management"
- What are the scaling challenges with the current set of practices for reconciling data?
- ML has been applied to data cleaning for a long time in the form of entity resolution, etc. How has the landscape evolved or matured in recent years?
- What (if any) transformative capabilities do LLMs introduce?
- What are the missing pieces/improvements that are necessary to make current AI systems usable out-of-the-box for data cleaning?
- What are the strategic decisions that need to be addressed when implementing ML/AI techniques in the data cleaning/reconciliation process?
- What are the risks involved in bringing ML to bear on data cleaning for inexperienced teams?
- What are the most interesting, innovative, or unexpected ways that you have seen ML techniques used in data resolution?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on using ML/AI in master data management?
- When is ML/AI the wrong choice for data cleaning/reconciliation?
- What are your hopes/predictions for the future of ML/AI applications in MDM and data cleaning?
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
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- Tamr
- Master Data Management
- CERN
- LHC
- Michael Stonebraker
- Conway's Law
- Expert Systems
- Information Retrieval
- Active Learning