Before you change a table, rename a column, or deprecate a dataset — see exactly what will be affected, who needs to know, and how to make the change safely.
Data changes are inevitable. Business requirements evolve, systems get upgraded, and pipelines need refactoring. But most organizations make changes blindly — hoping nothing breaks, unsure who to notify, and unable to predict the downstream consequences until something fails in production.
Impact Analysis changes this. Before you commit a change, it shows you the complete blast radius: every downstream table, every pipeline, every dashboard, and every stakeholder that depends on the data you're about to modify. It turns a risky, reactive process into a safe, planned operation.
Make changes with confidence. Know what will break before you break it.
Choose the table, column, or dataset you plan to modify, deprecate, or delete.
Caninsoft traces lineage downstream to show every dependent pipeline, report, dashboard, and data asset.
Export a stakeholder list, generate a change summary, and coordinate rollout with everyone who will be affected.
See every pipeline, table, dashboard, and report that depends on what you're changing.
Automatically identify data owners and consumers who need to be notified about changes.
Classify changes as low, medium, or high risk based on the number and criticality of dependencies.
Generate detailed impact reports for review, approval, or documentation.
Route high-risk changes through governance workflows before implementation.
Preview the impact of a proposed change without actually making it.
Refactor pipelines and schemas without breaking downstream dependencies.
Know which reports and dashboards will be affected before changing a dbt model.
Govern data changes with approval workflows and impact documentation.
See which dashboards and metrics depend on a data source before it gets deprecated.
Coordinate changes across teams by knowing exactly who will be affected.
surprise outages from unplanned schema or pipeline changes
change rollouts — coordinate with stakeholders proactively instead of reactively
trust in data — teams know changes are planned, reviewed, and communicated
risk — changes are approved and documented before they go live