57: Improving Data Quality Using Data Product SLAs with Egor Gryaznov of Bigeye
This week on The Data Stack Show, Eric and Kostas spend some time with Egor Gryaznov, co-founder and CTO of Bigeye. Egor discusses issues surrounding data quality in organizations of various sizes and uses helpful analogies from software engineering to help define data quality.
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Show Notes
Highlights from this week’s conversation include:
- Egor’s software engineering background and history with Uber (2:19)
- Experimentation platforms and analytics definitions (7:49)
- Bigeye’s function and use cases (9:40)
- Managing the relationship between the data engineer maintaining the pipelines and the downstream teams providing the context (18:49)
- Pinpointing problems in data compared to problems in software (21:55)
- Defining data quality at Bigeye (24:13)
- Machine learning models as a data product (28:38)
- Determining SLAs (32:22)
- How Bigeye brings different parties together and addresses natural communication barriers (36:42)
- Looking at when an organization needs to implement data quality tooling (45:54)
The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.
RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.
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