Strawberry can help prepare data-quality reviews, exception investigation, source reconciliation, and recurring operational checks. It can assemble the screens, documentation, and related browser context behind a data question so the team can inspect a proposed action before it affects a live environment.
01
Data exceptions are easier to resolve when their lineage is visible.
A result that looks wrong may come from an upstream source, a definition change, or a late operational event. Strawberry can structure the evidence around the Hana workspace so the next investigator starts with a clear trail instead of an empty ticket.
02
Definitions need a single reviewed interpretation.
Metrics and fields only stay trustworthy when their meaning matches current documentation.
A comparison pass can identify inconsistent definitions and show the exact pages that need an owner to reconcile them.
03
Corrective work should begin with a reviewable diagnosis.
Before anyone corrects a live record, the team needs a diagnosis: what is affected, what evidence supports the finding, and what alternative explanations remain. Prepare that decision record first and keep the corrective step separate.
04
A midweek quality pass keeps small breaks from becoming a month-end problem.
A Wednesday review gives data operators time to resolve emerging issues before weekly reporting locks in a bad assumption. The scheduled pass can assemble open exceptions and sources; a person still decides whether to modify production data.
Hana Strawberry