Round table: Honest Story About QA in Big Data
More and more organizations are attempting to implement their solutions for Data Analytics, labeling them as Big Data solutions. It sounds intriguing, but in most cases, when it comes to QA, testing Big Data is limited to integrating with synthetic data and trusting the Data Analyst. But what if the product demands more?
Using the example of testing a product that works with hypotheses, I will address the following questions in my presentation:
- What does QA need to know when testing the logic of mathematical models?
- How to proceed when it's unclear what goes in, what comes out, and what the expected outcome should be, especially when the simplest calculation takes up to 2 hours?
- How to provide value to the product without turning into a Data Quality expert?