Session Outline
It is often the case that a gap exists between anticipated outcomes and the actual data quality, which leads to frustration and mistrust. This talk at the Data Innovation Summit 2024 delves into pragmatic strategies that we employed to bridge this gap. This talk, led by Martina Ivaničová from Kiwi.com, discusses both the technical (hard) and cultural (soft) measures implemented to uphold these standards. Are you a data scientist, software engineer, product manager, or data engineer? Join us in this discussion; data quality concerns us all.
Key Takeaways
- Integration tests serve as a proactive barrier, preempting the violation of data contracts, unlike reactive data quality checks.
- Prioritization is crucial; a product-centric mindset is key when evaluating the balance between resource investment and potential gain.
- Data quality management is requiring both hard and soft measures.
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