Data Mesh Radio
#253 Data Mesh Implementation Success Metrics - Data Quality - Mesh Musings 53
Sep 22, 2023
The podcast discusses the importance of measuring data quality and setting up implementation metrics in Data Mesh. It explores the concept of data quality, measuring trust in data quality, and measuring data quality at the implementation level. It emphasizes the significance of trust and the need for data to be both trustworthy and useful for decision-making. The podcast provides insights on metrics such as time to detect and fix issues, compliance with quality SLAs, incident detection and resolution, and the impact of trust on implementation success.
40:58
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Quick takeaways
- One important aspect of measuring data quality at the implementation level is tracking compliance with service level agreements (SLAs) and the ability to detect and recover from incidents.
- Measuring trust in the data and its impact on decision-making is a crucial factor in assessing data quality at the implementation level.
Deep dives
Measuring Data Quality at the Implementation Level
To measure data quality at the implementation level, it is important to track compliance with quality service level agreements (SLAs) and the ability to detect and recover from incidents. This includes measuring how often data products meet their SLAs and the severity of incidents. Trust is another key factor to consider, measuring how much trust exists in the data and its impact on decision-making. Additionally, the quality of metadata should be assessed, although measuring this can be challenging. Overall, setting up a data quality measurement framework early on and focusing on the key metrics of SLA compliance, incident detection, trust, and metadata quality can help ensure data quality in the implementation.
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