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

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:

## 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.

Last Updated: November 21, 2025 Version: 1.0