Data Analytics & Business Intelligence
Startup Portfolio Executive Dashboard
Designed and validated an interactive Power BI dashboard that transforms 25,000 startup records into an executive view of funding, recurring revenue, runway risk, workforce changes, company outcomes, and data quality.
- Role
- Data Analyst and Dashboard Designer
- Status
- Completed portfolio project
- Environment
- Power BI Desktop and Power BI Service
Converted a complex raw dataset into a decision-oriented dashboard and independently verified every major KPI against the source data.
Project Overview
The raw CSV contained 25,000 startup records and multiple financial, operational, workforce, and company-outcome fields. The goal was to transform it into an executive dashboard that communicates portfolio condition, performance, and risk clearly.
This is a portfolio demonstration using a sample dataset and should not be interpreted as a real-world investment report.
The Challenge
- The raw data was difficult to interpret in spreadsheet form.
- Financial, workforce, runway, outcome, and quality indicators needed clear definitions.
- Derived categories and measures had to remain responsive to filters.
- Financial values already stored in USD millions could easily be misinterpreted.
- Dashboard totals needed to be independently checked against the raw source.
My Approach
- 1Inspected the raw CSV and assigned appropriate data types.
- 2Cleaned and standardized fields in Power Query.
- 3Created derived categories such as runway-risk bands and acquisition-status groupings.
- 4Built reusable DAX measures for financial, workforce, outcome, and data-quality KPIs.
- 5Designed an interactive executive dashboard with Country, Domain, and Acquisition Status filters.
- 6Recalculated and audited the results against all 25,000 raw records.
- 7Refined labels and units to make the dashboard easier to interpret.
Interactive Dashboard
This demonstration report uses Power BI Publish to web, which makes the report publicly accessible. It contains sample portfolio data only and does not expose private credentials, workspace links, or private data.
Open interactive dashboard in a new tabKey Findings
- 25,000
- source records analyzed
- 24,999
- distinct company IDs, with one duplicated ID
- ≈ $1.491T
- total reported funding
- ≈ $832.52B
- total reported annual recurring revenue
- 12.19 months
- average runway
- ≈ 57.57%
- of records in the High or Critical runway-risk groups
- 14.91%
- portfolio-level layoff rate
- ≈ 17.83%
- reached an exit event
- ≈ 20.33%
- flagged under the implemented data-quality rules
An exit event is not automatically a successful outcome because this calculation includes acquisitions, fire-sale acquisitions, and IPOs.
The source stores financial values in USD millions. For clarity, 1,490,777.46 USD M is presented as approximately $1.491 trillion in total funding, while 832,522.26 USD M is presented as approximately $832.52 billion in annual recurring revenue.
Data Validation and Quality Audit
The final dashboard was cross-checked against the raw CSV across all 25,000 records.
- All primary KPI totals matched the raw data.
- Workforce values reconciled exactly: peak headcount minus layoffs equaled current headcount.
- One duplicated company ID was identified.
- Most flagged records were closed companies that still reported current employees.
- Missing AI-adoption values revealed an opportunity to broaden the data-quality rules.
- Financial values were converted or presented in clearer units to prevent confusion.
My Contribution
- Inspected and cleaned the raw dataset.
- Created Power Query transformations and derived fields.
- Developed DAX measures and KPI definitions.
- Designed the dashboard layout and interaction model.
- Defined and reviewed data-quality checks.
- Independently recalculated the dashboard results against the raw CSV.
- Translated technical measures into plain business language.
What Worked and What I Would Improve
What worked
- The dashboard gives executives a fast overview while retaining interactive filtering.
- Grouped visuals create clear financial, workforce, risk, outcome, and quality narratives.
- Independent validation increased confidence in the displayed results.
What I would improve
- Broaden the data-quality rules to account for missing categorical values.
- Add clearer KPI definitions and risk thresholds through tooltips.
- Consider a separate detailed analysis page for drill-down exploration.
- Use a larger or real-world dataset with documented source lineage in a future version.
Reflection
“This project strengthened my ability to move beyond creating charts and focus on the full analytical process: understanding the data, defining useful measures, validating the results, and communicating what the numbers actually mean.”
Disclaimer
This portfolio project uses a sample dataset for demonstration and learning purposes. The figures and findings should not be interpreted as investment advice or as a representation of a specific real-world startup portfolio.