On-Demand Webinar: Seven Sins Of Data Quality

Confess your data sins. Find redemption. Maybe even laugh about it.

Written by Chris Bergh on January 20, 2026

DataOps Data Quality DataOps TestGen On-Demand Webinar Open Source

On-Demand Webinar: Seven Sins Of Data Quality

Key points

The seven personal sins are shame, denial, avoidance, passivity, laziness, gluttony, and ignorance. Each one comes with a story from a real data team rather than a definition.
The five organisational sins are low relational coordination, blame culture, data blinders, project rather than product focus, and lack of process curation.
The personal sins are almost always a reflection of the system. Chris cites Deming's split — 97% of problems are the process and 3% are specific — and says a leader's first job is recognising that the disarray is theirs to fix.
Avoidance has a measurable price: teams that keep putting data quality off spend up to 40% of their time firefighting, and their stakeholders find the issues first.
Gluttony looks like ambition. One insurance team spent seven years building a data warehouse that never got a customer, and a pharma team expected a platform migration to fix data quality by itself.
The counter to all twelve is to get something done rather than something perfect: pick one field, add a check, and iterate to influence.

Confess your data sins. Find redemption. Maybe even laugh about it.

Your data team has sinned. Maybe you’ve rationalized away data quality issues, such as “the users have a workaround” or “I haven’t heard any complaints lately.” Or perhaps you’ve hoped that by not examining the data closely, you won’t be held responsible if issues arise. Welcome to the club. We had an honest conversation about the behavioral sins that sabotage even the most talented data teams.

In this recorded webinar, DataKitchen CEO Chris Bergh takes you through the Seven Deadly Sins of Data Quality: Shame, Denial, Avoidance, Passivity, Laziness, Gluttony, and Ignorance. You’ll see how to spot and address these human tendencies that turn data engineers into firefighters, business users into skeptics, and dashboards into elaborate works of fiction. Learn practical ways to shift from blame and firefighting to proactive quality, avoid failed “boil the ocean” initiatives, and finally understand what it takes to move beyond the mindset of “the pipelines ran all green.”

Ready to break free from data sins? You’ll have a few laughs at our collective misery over data quality. Because if we can’t laugh about our data disasters, we’ll definitely cry.