Public Data Quality Report Automator

3.35

Derivation Chain

Step 1 Public disclosure of public data provision evaluation results

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Step 2 Increasing pressure on public agencies to improve data quality

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Step 3 Automated quality Report generation service for evaluation preparation

Problem

As the Ministry of the Interior and Safety conducts annual public data provision evaluations, data officers at local governments and public agencies (typically Grade 6–7 civil servants) manually inspect their agency's API response rates, data accuracy, and update frequency to prepare for evaluation. Each agency manages 30–100 APIs on average, and full inspection takes 2–4 weeks. Late discovery of deficiencies leaves 40% of agencies ranked in the bottom tier.

Solution

A SaaS where public agencies register their data API endpoints for automated monitoring of response rate, response time, data accuracy, schema consistency, and update frequency, with auto-generated quality Reports aligned to Ministry evaluation criteria. Provides improvement guides and priority rankings for each deficient item.

Target: IT officers at local governments and public agencies (Grade 6–7 civil servants), public data management outsourcing firms

Revenue Model: SaaS Monthly Subscription at 99,000 KRW (~$74)/agency (monitoring up to 50 APIs), additional APIs at 1,000 KRW (~$0.75)/month each. Evaluation season Report generation at 50,000 KRW (~$37.50) Per Transaction.

Ecosystem Role: Consumer

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

2.0/5

U Urgency

3.0/5

M Market

3.0/5

R Realizability

4.0/5

V Validation

4.0/5

NUMR-V Scoring System

N Novelty	1-5	How uncommon the service is in market context.
U Urgency	1-5	How urgently users need this problem solved now.
M Market	1-5	Market size and growth potential from proxy indicators.
R Realizability	1-5	Buildability for a small team with realistic constraints.
V Validation	1-5	Validation signal quality from competition and demand data.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (70%)

Tech Complexity

29.3/40

Data Availability

20.8/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

Tech Complexity	/ 40	Difficulty of core implementation stack.
Data Availability	/ 25	Practical availability and cost of required data.
MVP Timeline	/ 20	Expected time to ship a usable MVP.
API Bonus	/ 15	Bonus for viable public API leverage.

Market Validation (63/100)

Competition

8.0/20

Market Demand

9.4/20

Timing

16.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

12.0/15

Solo Buildability

7.0/10

Validation Breakdown

Competition	/ 20	Signal quality from competitor landscape.
Market Demand	/ 20	Demand proxies from search and mention patterns.
Timing	/ 20	Fit with current shifts in tech, behavior, and regulation.
Revenue Signals	/ 15	Reference evidence for monetization viability.
Pick-Axe Fit	/ 15	How well the concept serves participants in a trend.
Solo Buildability	/ 10	Practicality for lean-team implementation.

Technical Requirements

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

Dashboard