Peaka unifies data sources behind governed definitions and lineage. Strawberry can inspect the datasets behind a question, compare metric meaning across sources, trace a result to its origin, and prepare a data-quality review before a team relies on the number.
01
Ask where a number came from before putting it in a deck.
A metric is not trustworthy merely because it has a clean label.
Strawberry can follow visible Peaka lineage from the data product through its inputs and definitions, exposing changed sources and undocumented assumptions.
02
Find when two teams are using the same word differently.
Terms such as revenue, active, and qualified often conceal different date windows or exclusions. Strawberry can compare Peaka semantic definitions and surface the precise mismatch instead of treating a reporting dispute as a spreadsheet problem.
03
Review new sources before they muddy the shared layer.
A connector can introduce duplicate identities, incomplete histories, or information only one department may see. Strawberry can prepare an intake review around expected fields, ownership, overlaps, and downstream impact.
04
Give definitions a midweek ownership check.
Wednesday catches recent definition changes while the people who introduced them can still explain their context. A Peaka governance brief can consolidate altered terms, lineage gaps, and orphaned products for review.
Peaka Strawberry