data-strategy-architect
Skill from AIBPM42/hodgesfooshee-site-spark
- Category: discovered
- Tags: github, community
- Version: 1.0.0
- Author: walid
- Downloads: 0
- Updated: 2025-11-22
Download the latest version (v1.0.0) from this page.
SKILL.md
# Data Strategy Architect
## Description
Strategic thinking and architecture planning for data acquisition, workflow optimization, and business intelligence systems. Use when making decisions about data sources, scraping strategies, API integrations, cost-benefit analysis, or pipeline architecture. Helps identify opportunities, evaluate trade-offs, and design scalable data systems.
## When to Use This Skill
Trigger this skill when the user:
- Asks "should we use..." or "what's the best way to..."
- Needs to evaluate multiple data sources or approaches
- Wants to optimize existing workflows or reduce costs
- Is planning a new data integration or pipeline
- Needs strategic advice on scraping, APIs, or automation
- Asks about scalability, performance, or architecture
- Wants to understand business impact of technical decisions
## Core Capabilities
### 1. Data Source Evaluation
- Compare multiple data sources (free vs paid, API vs scraping)
- Assess data quality, reliability, and freshness
- Calculate total cost of ownership (TCO)
- Identify hidden costs (maintenance, rate limits, legal risk)
- Recommend optimal data source mix
### 2. Workflow Architecture
- Design efficient data pipelines
- Identify bottlenecks and optimization opportunities
- Plan for scale (10x, 100x volume)
- Create fallback strategies and error handling
- Balance cost, speed, and reliability
### 3. Cost-Benefit Analysis
- Calculate ROI for data investments
- Compare build vs buy decisions
- Identify cost reduction opportunities
- Project long-term savings
- Evaluate time-to-value trade-offs
### 4. Strategic Planning
- Prioritize features and data sources
- Plan phased rollouts (MVP → Scale → Optimize)
- Identify competitive advantages
- Recommend quick wins vs long-term plays
- Create decision frameworks
## Approach
When activated:
1. **Understand Context**
- What problem are we solving?
- What's the business goal?
- What constraints exist? (budget, time, technical)
- What's already built?
2. **Analyze Options**
- List all viable approaches
- For each option, evaluate:
- Cost (setup + ongoing)
- Complexity (build time + maintenance)
- Reliability (uptime, data quality)
- Scalability (can it handle 10x growth?)
- Legal/ethical considerations
3. **Recommend Strategy**
- Primary recommendation with reasoning
- Alternative approaches ranked
- Quick wins to start immediately
- Long-term optimization path
- Risk mitigation plan
4. **Create Action Plan**
- Prioritized steps
- Success metrics
- Decision points (when to pivot)
- Timeline estimates
## Example Decision Frameworks
### Data Source Selection Matrix
| Factor | Weight | Free Source | Paid API | Build Custom |
|--------|--------|-------------|----------|--------------|
| Cost | 20% | ✅ High | ❌ Low | ⚠️ Medium |
| Reliability | 25% | ⚠️ Medium | ✅ High | ⚠️ Medium |
| Data Quality | 25% | ⚠️ Medium | ✅ High | ✅ High |
| Maintenance | 15% | ❌ High | ✅ Low | ❌ High |
| Scalability | 15% | ⚠️ Medium | ✅ High | ✅ High |
### Build vs Buy Threshold
**Build if:**
- Unique requirements not met by existing solutions
- Long-term cost savings >50%
- Need full control over data quality
- Have technical expertise in-house
- Volume justifies custom solution (>10,000 requests/month)
**Buy if:**
- Commodity data available from vendors
- Time to market is critical (<30 days)
- Maintenance burden is concern
- Volume is low (<1,000 requests/month)
- Vendor provides additional value (support, updates)
### Scale Planning Triggers
**Current State → Next Phase:**
- **MVP** (0-100 leads/month)
- Manual processes acceptable
- Focus on proving concept
- Optimize for learning, not efficiency
- **Growth** (100-1,000 leads/month)
- Automate repetitive tasks
- Basic error handling
- Monitor key metrics
- **Scale** (1,000-10,000 leads/month)
- Optimize for cost and speed
- Advanced error recovery
- A/B testing and iteration
- **Enterprise** (10,000+ leads/month)
- Multi-source redundancy
- Real-time monitoring
- Predictive optimization
## Strategic Thinking Prompts
When analyzing a situation, ask:
### Opportunity Questions
- What's the 10x bigger opportunity here?
- What adjacent problems could we solve with this infrastructure?
- What data are we collecting but not using?
- What would make this 10x more valuable?
### Risk Questions
- What breaks if this scales 10x?
- What's our backup plan if the primary source fails?
- What legal/ethical risks exist?
- What happens if our competitor does this first?
### Efficiency Questions
- What's the bottleneck right now?
- What manual work could be automated?
- What redundant work exists?
- What's the lowest-effort, highest-impact improvement?
### Business Questions
- What's the revenue impact of this decision?
- How does this create competitive advantage?
- What's the time-to-payback?
- What does this enable downstream?
## Integration with Other Skills
- **error-annihilator**: Call when implementation hits technical blockers
- **roadmap-builder**: Call when prioritizing features
- **launch-planner**: Call when defining MVP scope
- **lead-hunter**: Call for domain-specific Lead Hunter decisions
## Output Format
Provide recommendations in this structure:
```markdown
## Strategic Analysis: [Topic]
### Context
- Current state
- Goal
- Constraints
### Options Evaluated
1. **Option A**: [Name]
- Pros: [List]
- Cons: [List]
- Cost: [Estimate]
- Timeline: [Estimate]
2. **Option B**: [Name]
- [Same format]
### Recommendation: [Primary Choice]
**Why:** [2-3 sentences on reasoning]
**Quick Wins** (Start Today):
- [ ] Action 1
- [ ] Action 2
**30-Day Plan**:
- Week 1: [Milestone]
- Week 2: [Milestone]
- Week 3: [Milestone]
- Week 4: [Milestone]
**90-Day Vision**:
- [What success looks like]
**Risks & Mitigation**:
- Risk 1 → Mitigation
- Risk 2 → Mitigation
**Success Metrics**:
- Metric 1: [Target]
- Metric 2: [Target]
**When to Pivot**:
- If [condition], then [alternative]
```
## Key Principles
1. **Start Small, Think Big**
- MVP first, optimize later
- But design for scale from day 1
2. **Measure Everything**
- You can't optimize what you don't measure
- Build instrumentation from the start
3. **Automate the Boring**
- Manual work doesn't scale
- Time saved compounds
4. **Build for Change**
- Requirements evolve
- Design for flexibility
5. **Know Your Numbers**
- Cost per lead
- Time to value
- Break-even point
- ROI timeline
6. **Stack Leverage**
- Use existing tools/platforms
- Build only what's unique
- Compound advantages
## Example Use Cases
### Use Case 1: Choosing a Data Source
**Question**: "Should we use Apify, Manus, or build custom scrapers?"
**Analysis**:
- Apify: $49/month, established, but can't handle iframes
- Manus: [pricing TBD], handles iframes, newer platform
- Custom: $0/month tools, full control, high maintenance
**Recommendation**: Start with Manus for complex sites (CaseLink), use Apify for simple sites (tncrtinfo counties). Evaluate custom scrapers after 90 days if volume justifies.
### Use Case 2: Scaling Strategy
**Question**: "We're getting 100 leads/month. How do we get to 1,000?"
**Analysis**:
- Current: 1 county (Davidson)
- Opportunity: 8 more counties available
- Constraint: Each county needs configuration
**Recommendation**: Add 2 counties per week. Use template approach. Aim for 9 counties by Week 5 = 900 leads/month.
### Use Case 3: Cost Optimization
**Question**: "Skip tracing costs $1,000/month. How do we reduce this?"
**Analysis**:
- Current: Paid API at $2/lookup × 500 leads
- Alternative: Free sources (FastPeopleSearch, TruePeopleSearch)
- Trade-off: 75% success rate vs 95%, manual fallback needed
**Recommendation**: Use free sources first, paid API for high-value leads only (equity >$100k). Projected savings: $750/month.
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**Last Updated**: November 21, 2025
**Version**: 1.0