Every Power BI consultant knows the scenario: you've built a polished report for one client, and now a second client wants something similar but pointing at their own data. The report structure is the same, the DAX logic is the same — only the datasource is different.
So you copy the .pbix, open it, and start manually updating every table reference, every measure, every filter. Hours of tedious, error-prone work.
There has to be a better way. And there is.
The datasource switching problem
When you change a Power BI report's datasource, it's not just one connection string. A typical report has:
- Tables that need to point at different source tables
- Columns that may have different names in the new dataset
- DAX measures containing hardcoded table and column references
- Visual queries in PBIR JSON files referencing specific entities
- Filters at the visual, page, and report level
- Bookmarks that store filter state snapshots
Change one and miss another, and you get broken visuals, missing data, or subtle calculation errors that are hard to catch.
The manual approach (and why it fails at scale)
The traditional approach is:
- Duplicate the report
- Update the data source connection
- Fix every broken reference manually
- Test every page
This works for one report. But if you're a consultancy that builds templated reports — one for retail, one for healthcare, one for logistics — and each client gets a customised version, you're doing this dozens of times a year. Mistakes are inevitable.
How Dummy BI Automate handles it
Dummy BI Automate uses a 6-phase wizard that reads your PBIP project folder and systematically rewrites everything:
Phase 1: Table mappings
Map each source table to its new equivalent. The tool reads your TMDL model files and shows you every table in the semantic model.
Phase 2: Column mappings
For each mapped table, map the columns. The tool suggests matches based on name similarity.
Phase 3: Measure mappings
Map measures that reference specific tables or columns. The tool parses DAX expressions to find references.
Phase 4: Hierarchy mappings
Map any hierarchies (Year → Quarter → Month) to their equivalents in the new dataset.
Phase 5: Calculation group mappings
Map calculation group items — common in reports using time intelligence patterns.
Phase 6: Field parameter mappings
Map field parameters — used in dynamic visuals where users can switch which measure is displayed.
After mapping, the tool rewrites:
- All TMDL files (model definition, table definitions, relationships)
- All DAX expressions (measures, calculated columns, calculated tables)
- All PBIR JSON files (visual queries, filters, bookmarks, drillthrough parameters)
The entire process takes minutes, not hours. And because it's systematic, nothing gets missed.
Why local processing matters
Your Power BI data often contains sensitive business information — revenue figures, customer data, employee records. Dummy BI Automate processes everything on your local machine. Your datasource mappings, your TMDL files, your DAX expressions — none of it leaves your computer.
This isn't a marketing angle. It's an architectural decision. The tool reads files from your filesystem and writes files to your filesystem. There is no cloud service, no upload step, no API call that carries your data.
Getting started
- Save your Power BI report in PBIP format (File → Save As → Power BI Project)
- Open Dummy BI Automate
- Select your project folder
- Run the Switch Datasource wizard
- Map your tables, columns, and measures
- Apply — and you have a switched report ready to open in Power BI Desktop
If you're a consultant or a team that builds repeatable Power BI solutions, datasource switching automation saves measurable time on every engagement.