What this covers
Reports lie when the data underneath them is wrong. A lot of "Salesforce isn't working for us" is actually data quality, structure, or definition problems. The work here is fixing the data and then building the reporting on top.
- Data model review — are you using the right objects, the right relationships, the right keys?
- Data cleanup and migration — deduplication, normalization, mass updates done with a plan and a rollback path.
- Imports and one-time loads — Data Loader, Workbench, dataloader.io, or custom scripts when the data is unusual.
- Reports and dashboards that show what leadership needs to see, not what was easy to build.
- CRM Analytics / Tableau integration when standard reports run out of road.
- Data warehouse hand-off — Salesforce → Snowflake / BigQuery / Redshift, with the grain and timing the analytics team needs.
How we typically engage
Data work usually has two halves: the cleanup project (one-time, fixed scope) and the reporting build (iterative, often paired with stakeholder reviews). I'll scope them together but track them separately so you can see what you got from each.
What "done" usually looks like
- A clean dataset with documented rules for keeping it clean.
- A small set of dashboards leadership actually opens.
- A loading and refresh process that doesn't break when an admin changes a picklist value.