As long as data processes are built fast and cheap, we will always struggle to understand who is accountable for data quality issues.
When leadership asks for a dashboard, the complexity that goes into defining the query, understanding the business semantics of upstream sources, and layering filters on top of sketchy data is abstracted away from them.
Many data ”professionals” who are called up by a non-technical data consumer blame them for 'breaking' their report. The data team is confused by this, considering they didn't make any changes for the last week, only to discover an upstream source table suddenly dropped a column, or modified business logic, or failed to refresh causing the dashboard to show unexpected or stale results.
Data teams take on the burden of fixing these issues retroactively (opposed to proactively). Monitoring tools and data catalogs help them do this faster and easier, but there is no preventative mechanism to A.) inform data teams when change is about to occur in the first place and B.) alert data consumers their dashboards are about to fail and why.
Do better.