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Don't fix bad data, do this instead

30 minutes


In a time where GenAI is quickly growing in popularity, along with prescriptive analytics and online ML models, the question is raised whether we still need to care about data quality? We strongly believe that the answer is yes, and even more so than before!

Our expectations of data are high, and this often leads to frustrations when reality does not meet these expectations.

In the pursuit of data quality, expectations must be grounded in reality. It is often the case that a gap exists between anticipated outcomes and the actual data reality, which leads to frustration and mistrust.

This talk delves into pragmatic strategies that can be employed to bridge this gap. The talk will discuss both the technical (hard) and cultural (soft) measures implemented to uphold these standards.

Key Takeaways:

  1. Integration tests serve as a proactive barrier, preempting the violation of data contracts, unlike reactive data quality checks.
  2. Prioritisation is crucial; a product-centric mindset is key when evaluating the balance between resource investment and potential gain.
  3. Data quality management is requiring both hard and soft measures

Are you a data scientist, software engineer, product manager, or data engineer? Join us in this discussion; data quality concerns us all.

The speaker

Martina Ivanicova

Martina Ivanicova

Martina serves as the Head of Data Engineering at, where she oversees teams of analytics engineers and the data platform team. Her current focus lies in the adoption of the Data Mesh paradigm, driving the development of a self-service data platform for both batch and real-time data analytics, and setting up an ML platform to optimize data scientist workflows. Despite graduating with a degree in theoretical physics, Martina’s career has consistently been linked to data engineering. Her experience spans creating data solutions for smart buildings to developing data warehouse solutions for major agencies.