Dashboards and reports are how businesses view their data and make decisions. But even the most polished reports can be wrong. A broken measure, a stale extract, or a filter that silently drops rows can lead to inaccurate numbers and costly decisions. Manually validating reporting data, one dashboard at a time, does not scale beyond a handful of reports, and it still leaves gaps that can go unnoticed.
iceDQ automates BI report testing, ensuring every report across every platform is validated consistently and accurately before it reaches business users.
OVERVIEW
Compare and Test Visual Report with Database with Any BI Platforms
Regardless of which BI tool a report lives in, iceDQ follows the same repeatable workflow:
- Connect or embed the report: Securely connect to the BI platform and embed the report, dashboard, or app so its live output is available for testing.
- Select what to test: Choose the specific page, tab, visual, KPI, or sub-report you want to validate - down to an individual chart or table.
- Apply real-world conditions: Configure filters, slicers, selections, parameters, or bookmarks so the test replicates how an actual user would interact with the report.
- Run data checks: Apply reconciliation, validation, and business-rule checks against the visual's underlying data to confirm it's accurate, complete, and consistent.
Because this workflow isn't tied to any one vendor's architecture, the same test logic and process extend across your entire BI landscape, not just a single tool.
Compare and Test Visual Report with Database with Any BI Platforms
Regardless of which BI tool a report lives in, iceDQ follows the same repeatable workflow:
- Connect or embed the report: Securely connect to the BI platform and embed the report, dashboard, or app so its live output is available for testing.
- Select what to test: Choose the specific page, tab, visual, KPI, or sub-report you want to validate — down to an individual chart or table.
- Apply real-world conditions: Configure filters, slicers, selections, parameters, or bookmarks so the test replicates how an actual user would interact with the report.
- Run data checks: Apply reconciliation, validation, and business-rule checks against the visual's underlying data to confirm it's accurate, complete, and consistent.
Because this workflow isn't tied to any one vendor's architecture, the same test logic and process extend across your entire BI landscape, not just a single tool.
Test Semantic Layer
A report can only be trusted if every layer feeding it is trustworthy. iceDQ validates each one:
- Semantic and model layer: Connect directly to the underlying model - measures, relationships, calculated fields, and model-level logic - and confirm the logic itself produces correct results, not just the visual it renders into.
- Report vs. source: Reconcile what the report displays against the database, warehouse, lakehouse, or file it's built on, so you know the visualization reflects reality.
- Report vs. report: Compare a report against another version of itself - across environments (dev, test, prod), across a migration, or across an entirely different BI platform - to confirm nothing changed that shouldn't have.
- Row and column-level detail: Go beyond totals to check individual records and calculated values, catching mismatches that aggregate-level testing misses.
PRODUCT HIGHLIGHTS
 | Dynamic Filter and Slicer Testing: |  | Automatically generate combinations of filters, slicers, and parameters to validate interactions, defaults, and edge cases - not just the default view. |
| Row-Level Security Validation: | | Impersonate different roles and users to confirm each person sees only the data they're authorized to see. |
| Cross-Platform Comparison: | | Validate migrations and platform switches by comparing reports built in different BI tools against each other or against a common source. |
| Lineage and Impact Analysis: | | Trace dependencies across datasets, reports, and measures so you know exactly what's affected when a change happens upstream. |
| CI/CD Integration: | | Trigger BI test plans from your existing DevOps tools and publish results back into the pipeline, so report testing is part of deployment - not a separate manual step. |
| Scheduled, Continuous Testing: | | Run test plans on a recurring schedule and get notified when a report's data quality degrades, instead of waiting for a business user to notice. |
USE CASES
1
Power BI Testing
2
Cognos Testing
3
Qlik Testing
4
Tableau Testing
1
Power BI Testing
Tests and certifies Power BI semantic models, datasets, dashboards, and reports before they're deployed to business users.
Embeds Power BI reports directly, letting you select a sub-report, apply dynamic filters, and validate data across charts, tables, and KPIs.
Connects to the Power BI semantic layer via DAX queries to validate measures, relationships, and model-level logic.
Reconciles Power BI data against the source database, another report, or row/column-level values to catch mismatches before they reach stakeholders.
2
Cognos Testing
Embeds Cognos reports and dashboards to validate the data behind visualizations before they reach business users.
Applies filters and prompts to replicate real user scenarios and validate filtered report outputs.
Reconciles Cognos report data against the underlying database, warehouse, or another report to catch mismatches.
Validates calculations, measures, and business logic within Cognos packages against expected results.
3
Qlik Testing
Embeds Qlik Sense apps and sheets to validate the datasets powering your visualizations before they reach decision-makers.
Applies dynamic selections, alternate states, and bookmarks to replicate real user scenarios and validate filtered outputs.
Reconciles Qlik Sense app data against the underlying source, another Qlik app, or row/column-level values to confirm transformations and business logic are correct.
Validates measures, set expressions, KPIs, and aggregations by comparing results against expected logic or reference datasets.
4
Tableau Testing
Embeds Tableau workbooks and dashboards to validate the data powering visualizations before they're published.
Applies filters and parameters to replicate real user scenarios and validate filtered view outputs.
Reconciles Tableau extract or live-connection data against the source database or another workbook to catch mismatches.
Validates calculated fields, measures, and aggregations against expected logic or reference datasets.
1
Power BI Testing
Tests and certifies Power BI semantic models, datasets, dashboards, and reports before they're deployed to business users.
Embeds Power BI reports directly, letting you select a sub-report, apply dynamic filters, and validate data across charts, tables, and KPIs.
Connects to the Power BI semantic layer via DAX queries to validate measures, relationships, and model-level logic.
Reconciles Power BI data against the source database, another report, or row/column-level values to catch mismatches before they reach stakeholders.
2
Cognos Testing
Embeds Cognos reports and dashboards to validate the data behind visualizations before they reach business users.
Applies filters and prompts to replicate real user scenarios and validate filtered report outputs.
Reconciles Cognos report data against the underlying database, warehouse, or another report to catch mismatches.
Validates calculations, measures, and business logic within Cognos packages against expected results.
3
Qlik Testing
Embeds Qlik Sense apps and sheets to validate the datasets powering your visualizations before they reach decision-makers.
Applies dynamic selections, alternate states, and bookmarks to replicate real user scenarios and validate filtered outputs.
Reconciles Qlik Sense app data against the underlying source, another Qlik app, or row/column-level values to confirm transformations and business logic are correct.
Validates measures, set expressions, KPIs, and aggregations by comparing results against expected logic or reference datasets.
4
Tableau Testing
Embeds Tableau workbooks and dashboards to validate the data powering visualizations before they're published.
Applies filters and parameters to replicate real user scenarios and validate filtered view outputs.
Reconciles Tableau extract or live-connection data against the source database or another workbook to catch mismatches.
Validates calculated fields, measures, and aggregations against expected logic or reference datasets.
Automate your BI Report Testing with iceDQ today.
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FAQs: BI Report Testing with iceDQ
Which BI platforms does iceDQ support?
iceDQ supports automated testing across major BI platforms, including Power BI, Tableau, Qlik Sense, MicroStrategy, SAP BusinessObjects, and IBM Cognos, including dashboards, standard reports, paginated reports, and semantic/model layers.
Can iceDQ compare reports across different BI platforms?
Yes. iceDQ can reconcile a report in one BI platform against a report in another, which is useful during BI platform migrations or when standardizing reporting across business units.
Does iceDQ test the semantic or model layer, or just the visuals?
Both. iceDQ can validate the model layer directly – measures, relationships, and calculated logic – as well as the visuals and report elements built on top of it.
Do I need to manually click through every report to test it?
No. iceDQ automates the full workflow – connecting to the report, applying filters and selections, and running checks – so testing scales across hundreds of reports without manual effort.
How is this different from BI tool's built-in validation features?
BI platforms are built for visualization, not independent validation. iceDQ sits outside the BI tool and reconciles what it displays against the underlying data, catching discrepancies the BI tool itself has no way to surface.