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Increase the Quality and Reliability of Your Data

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Manage episode 298407717 series 2881014
Content provided by Travis Lawrence. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Travis Lawrence or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this episode, we speak with Lior Gavish, the co-founder of Monte Carlo to explore all things data quality. Monte Carlo is a data lineage and observability tool that lowers your data downtime.
Top 3 Value Bombs:

  1. Data products should be thought of in it’s entirely from the source to the consumer.
  2. No one data stakeholder can solve data quality issues, it’s a collaboration of the data engineers, business, data consumer and even software to help automate certain aspects of cataloging and capturing meaningful metadata.
  3. Good data quality processes should alert you to anomalies in your metrics before your data consumers do.
  continue reading

43 episodes

Artwork
iconShare
 
Manage episode 298407717 series 2881014
Content provided by Travis Lawrence. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Travis Lawrence or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

In this episode, we speak with Lior Gavish, the co-founder of Monte Carlo to explore all things data quality. Monte Carlo is a data lineage and observability tool that lowers your data downtime.
Top 3 Value Bombs:

  1. Data products should be thought of in it’s entirely from the source to the consumer.
  2. No one data stakeholder can solve data quality issues, it’s a collaboration of the data engineers, business, data consumer and even software to help automate certain aspects of cataloging and capturing meaningful metadata.
  3. Good data quality processes should alert you to anomalies in your metrics before your data consumers do.
  continue reading

43 episodes

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