Shailvi Wakhlu, Former Head of Data & Analytics, Ex-Strava, Ex-Komodo Health
DescriptionData insights can only be as good as the quality of the data they're based on. Inaccuracies and biases in your data can result in costly mistakes. In this talk, I highlight the typical lifecycle of data and the phases where bad data sneaks in. I also cover ways to prevent, diagnose & fix issues, using real-world and relatable examples from subscription businesses
Businesses use data to make good decisions that can help supercharge positive outcomes. If the data they use to make those decisions are of low quality, there is a high chance that the results will be low quality as well. Thus, it is very important to prevent bad data from occurring in the first place, by understanding how it gets created. The typical lifecycle of data, and the phases where bad data gets introduced help us with that deeper understanding. Having an intentional plan to find and fix bad data early in the pipeline is the best way businesses can protect themselves from potential dire outcomes
Takeaways- Proactive steps to preventing bad data
- Identifying and diagnosing bad data
- Fixing bad data problems in a scalable manner
