A

Public Data Quality Monitoring Alerts

4.00

Derivation Chain

Step 1 Expanded corporate use of public data (76.5% positive)
→
Step 2 Increase in services dependent on public data APIs
→
Step 3 Monitoring of public data API quality and availability
→
Step 4 Automatic detection and alerting of API failures and schema changes

Problem

Startups (5-15 employees) using public data APIs as core data sources experience service disruptions due to API response delays, schema changes, and temporary suspensions occurring without prior notice. The absence of API status monitoring on the Public Data Portal means it takes an average of 4-6 hours to detect an issue, and schema changes are only discovered after customer complaints are received. This results in an average of 2-3 data-related incidents per month, each taking half a day to recover.

Solution

(1) Perform health checks every 5 minutes on registered public data APIs (response time, status code, schema consistency verification), (2) Send immediate Slack/Kakao notifications when failures or schema changes are detected, (3) Automatically generate monthly SLA reports on availability and response time per API, reducing data incident detection time to under 5 minutes.

Target: Development team leads and CTOs at data-driven startups with 5-15 employees that use 3 or more public data APIs.
Revenue Model: SaaS monthly subscription: 29,000 KRW ($22) per month (monitoring 10 APIs), Premium: 59,000 KRW ($44) per month (50 APIs + SLA reports + schema change diff). Free plan: 3 APIs.
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
5.0/5
M Market
3.0/5
R Realizability
5.0/5
V Validation
3.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (76%)

Tech Complexity
34.7/40
Data Availability
20.8/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (57/100)

Competition
8.0/20
Market Demand
9.4/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
8.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Backend [medium] Frontend [low] Data Pipeline [low]
Dashboard