A

Public Data Hackathon Coach Bot

3.80

Derivation Chain

Step 1 Yeosu Public Data Hackathon
→
Step 2 Public data hackathon participant support
→
Step 3 Hackathon submission quality verification and dataset recommendations

Problem

College students and junior developers (ages 20-30) competing in public data hackathons (30+ annually from local and central government) spend an average of 2-3 days finding suitable datasets and 3-5 days on data preprocessing. 40% give up before submission due to inability to find the right data combinations, and among those who submit, 70% are eliminated in the first round due to lack of originality in data utilization.

Solution

When users input the hackathon theme and judging criteria, the system recommends suitable dataset combinations from 2,000+ APIs on data.go.kr, suggests join possibilities between datasets, and proposes utilization ideas. Pre-scores submissions on originality and completeness based on past winner pattern analysis, and coaches improvements.

Target: College students and junior developers (ages 20-30) participating in public data hackathons, beginner to intermediate data analysis skills
Revenue Model: Per Transaction billing at $7.50 per single hackathon coaching session. Monthly plan at $22/month (unlimited coaching). University group license at $367/semester for 30 users.
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
5.0/5
V Validation
4.0/5
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.
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
20.4/25
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 (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] AI/ML [medium] Frontend [low]
Dashboard