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Messy Excel data → Decision-ready dashboard 📊
A recent Excel workflow I worked on involved raw, unstructured data that wasn’t usable for analysis or reporting.
What I did:
Cleaned and structured the raw dataset
Fixed formatting and consistency issues
Built pivot tables and an interactive Excel dashboard
Result:
✔️ Clear insights
✔️ Faster reporting
✔️ No manual rework
This is the kind of Excel cleanup + reporting support I help teams with regularly.
If you’re dealing with messy spreadsheets and need clean, reliable Excel reports — happy to help.
Thank you so much for the support😊
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Most dashboards don’t have a visualization problem.
They have a KPI problem.
I’ve reviewed dozens of reporting systems recently and the pattern is consistent:
Metrics are defined differently across sheets
“Revenue” means one thing in finance and another in marketing
KPIs are tracked… but not tied to decisions
Dashboards look clean but don’t answer operational questions
When metric logic isn’t aligned, teams don’t have a data problem.
They have a decision problem.
That’s why I’ve started offering a structured KPI & Revenue Diagnostic Audit — focused on:
• Metric consistency
• Reporting logic
• Revenue driver alignment
• Decision-readiness
If you're building dashboards or scaling reporting systems, this layer matters more than design.
Data VisualizationData AnalysisProduct Data AnalyticsMicrosoft ExcelMicrosoft WordGoogle Sheetsexceldashboardexceldataanalytics
Completely agree. Most dashboards optimize for visuals, not decision velocity. If KPIs aren’t standardized across teams, you’re basically scaling confusion. The real leverage is in metric governance before visualization.
Clean structure. ERP dashboards get complex fast — especially with filtering, exports, and real-time metrics. Curious how you handled data fetching and state management at scale?
This dashboard was created to look at the distribution of federal contract awards in FY25 through a specific contracting medium. The first page is looking at all department contract awards and the second is looking at awards for one departmnt in particular and then broken out by component within that department
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