Richard Iannone
Rich is a software engineer that enjoys working with Python. He likes to create Python libraries that help people to accomplish things. While Rich very clearly digs programming, he enjoys other things as well! Examples include: playing and listening to music, reading books, watching films, meeting up with friends, and wandering through the many valleys and ravines of the Greater Toronto Area.
Session
Ensuring and communicating data quality (DQ) is one of the most persistent challenges in data-driven organizations. Data scientists, engineers, and analysts often struggle not just with detecting DQ issues, but with presenting those issues in actionable ways for diverse stakeholders across an organization (e.g., pipeline owners, fellow developers, less-technical colleagues, etc). On top of this, DQ work has an image problem as it can be seen as tedious, opaque, or even adversarial.
This talk introduces Pointblank, a Python package designed to make data quality validation and communication both robust and approachable. The library provides a comprehensive set of tools for profiling, validating, and reporting on data quality. There’s a strong focus on beautiful and actionable outputs as well. It can help you to generate tabular validation reports, data summaries, and granular error reporting that make it easy for anyone (technical or not) to understand what’s wrong and why.
Attendees will learn how Pointblank can help their teams not only catch data issues early, but also communicate them effectively, fostering a culture of shared responsibility for data quality. The talk will include live demos of common DQ workflows, showing how Pointblank turns a traditionally painful process into something transparent, productive, and even a little bit fun.