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Alerts & KPI Monitoring

Automated threshold checks on key performance indicators

These alerts are generated by running threshold checks against the current dataset. In a production BI system, these would trigger on new data ingestion. Here they run client-side against the pre-computed aggregates to demonstrate event-driven monitoring.

Critical

3

Warnings

4

Informational

4

US geographic concentration exceeds 40%

critical

65.6% of companies are US-based. The dataset has severe geographic bias: insights for non-US markets are less reliable.

Metric: US ShareThreshold: < 40%Actual: 65.6%

Major Seed → Series A attrition

critical

Only 8,172 of 11,102 seed-funded companies reached Series A (73.6%). This is the primary bottleneck in the funding pipeline.

Metric: Seed→A ConversionThreshold: > 80%Actual: 73.6%

US captures 75% of total funding

critical

The US accounts for $442.3B of $591.5B total tracked funding. Non-US funding analysis has limited depth.

Metric: US Funding ShareThreshold: < 50%Actual: 74.8%

Extreme funding distribution skew

warning

Average funding ($17.1M) is 7× the median ($2.4M). A small number of mega-rounds dominate aggregate totals: medians are more reliable than averages.

Metric: Avg/Median RatioThreshold: < 3×Actual:

Market concentration: Trading

warning

Trading represents 0.0% of all companies (20). Heavy sector concentration may bias aggregate success rates.

Metric: Top Market ShareThreshold: < 5%Actual: 0.0%

2,146 companies have closed

warning

These companies received funding but ultimately failed. At an average dataset funding of $17.1M, this represents significant capital destruction.

Metric: Closed CompaniesThreshold: MonitorActual: 2,146

High attrition from Series A to Series C

warning

Only 33% of Series A companies reach Series C (2,705 of 8,172). The middle stages are a significant filter.

Metric: A→C SurvivalThreshold: > 40%Actual: 33%

86% of companies are still "operating"

info

Many may have closed without updating their Crunchbase status. Survivorship bias is likely; the real closure rate is probably higher than 5.3%.

Metric: Operating %Threshold: AwarenessActual: 86.4%

3 countries have < 50 companies

info

Success rates and funding averages for these countries (MYS, TWN, BMU...) are statistically unreliable due to small sample sizes.

Metric: Small Sample CountriesThreshold: AwarenessActual: 3 of 30

Dataset covers companies founded through ~2014

info

Post-2014 dynamics (crypto boom, AI explosion, COVID, ZIRP-era mega-rounds) are not captured. Trends and predictions may not reflect current market conditions.

Metric: Data FreshnessThreshold: < 3 years oldActual: ~10 years old

Success rate is 61.0% ; but "acquired" ≠ success

info

The model treats acquisition as success and closure as failure. But acqui-hires (fire-sale acquisitions) are counted as "success," and many "operating" companies are effectively dead. The real picture is more nuanced.

Metric: Label QualityThreshold: AwarenessActual: 61.0%

How Alerts Work

In a production Business Intelligence system, alerts are event-driven: they trigger automatically when new data enters the warehouse and a KPI crosses a predefined threshold.

This demo generates alerts by checking the current dataset against rules: geographic concentration limits, funnel conversion benchmarks, distribution skew ratios and data quality indicators. Each alert includes the metric name, the expected threshold and the actual observed value, following the standard BI alerting pattern of metric → threshold → action.