B

Public Data Contest Submission Helper

3.30

Derivation Chain

Step 1 Increase in local government public data utilization contests like the Yeosu contest
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Step 2 Demand for data exploration, visualization, and submission preparation among contest participants

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

Local governments and public institutions hold more than 50 public data utilization contests annually, such as the Yeosu Island Expo contest. Participants (university students and office workers in their 20s-30s, including non-majors) spend an average of 8-15 hours finding suitable datasets on the Public Data Portal, and 40% give up at the data collection stage because they don't know how to call APIs. The submission format (report structure, visualization criteria) also changes each time, requiring an additional 5-10 hours to meet the format.

Solution

A web service that automatically recommends relevant public datasets based on the contest topic, and generates one-click data collection, cleaning, visualization, and submission report drafts. (1) When a contest announcement URL is entered, it analyzes the topic and recommends the top 10 related datasets from the Public Data Portal API catalog, (2) automatically calls and cleans the selected dataset and generates basic statistics, (3) auto-fills a report template matching the contest submission format.

Target: University students (20s) participating in public data contests, office workers new to data analysis (20s-30s), and job seekers looking to build award-winning experience.
Revenue Model: Free tier (1 contest, up to 3 datasets), Pro plan at 19,000 KRW (approx. $14.25) per contest (unlimited datasets + automatic report generation + 5 visualization types). 50% student discount.
Ecosystem Role: Education
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
4.0/5
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 (70%)

Tech Complexity
29.3/40
Data Availability
20.4/25
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 (56/100)

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
7.0/10
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] AI/ML [low] Frontend [medium]
Dashboard