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Gridavera uses optimized CAML queries to fetch data from SharePoint and writes data in batches of 20 records to avoid throttling. Change detection ensures only modified rows are submitted, minimizing network traffic.

Yes. Each user loads their own copy of the data into Excel. When they submit, Gridavera writes their changes to SharePoint. Since changes are submitted row by row, multiple users can work on different rows simultaneously. However, if two users modify the same row, the last submission wins.

Your work in Excel is safe — it’s a local file. However, you won’t be able to submit changes or load new data until you’re back online. Gridavera shows an offline banner and will prompt you to reconnect when you try to submit.

Yes. Users with the “Append” permission can add new rows directly from Excel. New rows are highlighted in green and validated for required fields before submission.

Row deletion is not supported for transaction lists through the Excel plugin. For dimension lists, authorized users can delete selected rows using the Dimensions tab. This design prevents accidental mass deletion of financial data.

Cascade lookups are dependent dropdowns where the options in one column are filtered based on the value in another column. For example, selecting a “Region” filters the “Country” dropdown to only show countries in that region.

Yes. The admin portal has a one-click Power BI Template (.pbit) export that produces a Power BI Desktop file pre-wired to your tenant’s SharePoint configuration:

• The data model is auto-injected — tables, columns, lineage tags, and relationships are generated from your transaction lists, dimension lists, and dimension relationships
• An M (Power Query) script is injected for each list so it pulls live from your SharePoint site
• Download the .pbit, open in Power BI Desktop, sign in with your Microsoft 365 account, and click Refresh — no manual model construction

This is intended for finance and BI teams that want to layer dashboards on top of their Gridavera planning data without rebuilding the data model from scratch.