Organizations depend on data to make informed decisions, however the quality of data in most companies is ... not good. Data is also the foundation for data science, and when the data is bad, everything that follows is also bad.

In this talk we'll share sightings of data found in the wild. We'll then discuss schemas and why they are not enough and raise questions about how can we make our data pipeline (ETL) better. We'll cover error detection and recovery, data versioning, data KPIs and more.