The Modern Data Stack Catch-22: Why Your Boss Won't Fund What You Can't Prove

"We need to see a proof of concept before we approve budget for proper analytics tools."

Sound familiar? You're stuck in the classic corporate chicken-and-egg problem: your boss wants to see modern analytics in action before investing in the tools that make modern analytics possible. Meanwhile, you're supposed to build something impressive with whatever free tiers you can scrounge together.

The traditional approach to this dilemma fails because teams think they need to choose between two extremes: either build something basic that doesn't impress anyone, or wait until they have the budget for enterprise tools. But there's a third way—one that requires thinking strategically about where to invest your limited time and energy.

quadrantChart
    title Strategic Analytics Progression
    x-axis Normal --> Original
    y-axis Easy to implement --> Hard to implment
    
    quadrant-1 HOW
    quadrant-2 _
    quadrant-3 NOW
    quadrant-4 WOW
    
    GitHub API Setup: [0.3, 0.2]
    Basic Dashboard: [0.2, 0.1]
    
    Correlation Insights: [0.6, 0.3]
    Churn Prediction: [0.6, 0.4]
    
    Enterprise Migration: [0.7, 0.7]
    Real Customer Data: [0.8, 0.8]

The smart approach is following a NOW → WOW → HOW progression. Start with achievable quick wins that build momentum, create impressive capabilities that generate executive excitement, then scale to enterprise solutions. Most teams skip straight to the HOW without building the foundation—and that's why they fail.

The Challenge: Breaking the Budget Deadlock

Here's exactly what you're facing, and why the traditional solutions struggle. The budget approval process creates an impossible situation where success depends on having the very resources you're trying to secure.

You need to demonstrate modern analytics value before getting budget for modern analytics tools. But you can't build a convincing proof-of-concept with Excel and basic free trials. Executives want to see it working with "real data," but you won't get access to customer data until after budget approval. Meanwhile, you have maybe four weeks to build something that looks enterprise-ready, using only whatever free tiers and open source tools you can find.

The predictable result? Teams either give up and build another Excel report that nobody cares about, or they spend months building an over-engineered solution that's technically impressive but doesn't solve actual business problems.

My Approach: The €5 Customer Analytics Challenge

I'm taking a completely different approach that breaks this deadlock. Over the next four weeks, I'm going to build a complete modern data stack for €5/month (😅) that proves customer analytics capabilities using publicly available data.

The key insight is this: you don't need customer data to prove customer analytics capabilities. What you need is to demonstrate the methodology, the insights you can generate, and the business value of real-time analytics. The specific data source is just implementation detail.

Here's my strategy. I'll use GitHub contributors as a proxy for customers because their behavior patterns are identical to what you'd see in customer engagement data. Think about it: contribution frequency equals usage patterns, commit message sentiment reflects customer satisfaction, and collaboration patterns show engagement depth. Same analytical challenges, same business questions, but with publicly available data.

💡 GitHub: A collaborative workspace where software developers share code, discuss problems, and work together on projects—generating rich behavioral data similar to customer interactions.

My weekly delivery strategy ensures I'm building momentum throughout the process. Instead of waiting four weeks to show results, I'll ship something tangible each week to maintain executive interest and gather feedback as I build.

Project Requirements: What We're Actually Building

The business question driving this entire project is: How do we identify customers at risk of churning before they actually leave? This isn't just an analytics exercise—it's about proving we can deliver insights that directly impact revenue and customer success.

To answer this question effectively, we need several types of customer data that traditional companies struggle to access during the proof-of-concept phase. Here's what we need and how GitHub provides it:

GitHub as Proxy for Every Business System:

We need product usage frequency data to identify engagement patterns—GitHub gives us commit frequency across repositories. We need support interaction sentiment to gauge customer satisfaction—pull request and issue comments provide rich sentiment data. We need feature engagement patterns to understand product adoption—repository activity across different projects shows us exactly this. And we need customer collaboration and referral data—code collaboration patterns reveal network effects and community engagement.

Technical Requirements

The system needs to handle several complex technical challenges that mirror real customer analytics platforms. Here's how the architecture progresses from methodology to implementation:

Methodology We're Proving

flowchart LR
    A[Source] --> B[Ingestion]
    B --> C[Warehouse]
    C --> D[Transform]
    D --> E[AI Enhancement]
    E --> F[Visualization]

End Goal Architecture

flowchart LR
    A[GitHub API] --> B[Airbyte]
    B --> C[BigQuery]
    C --> D[dbt]
    D --> E[AI Analysis]
    E --> F[Looker Studio]

Lo-Fi Prototype

flowchart LR
    A[GitHub API] --> B[CSV Export]
    B --> C[Evidence.dev]

The technical implementation proves key capabilities executives expect: real-time data ingestion (GitHub API), sentiment analysis of communications (commit messages via local LLM), automated risk scoring, multi-source data joins (commits, PRs, issues, users), and daily-updated dashboards.

The business logic has direct customer success applications. Declining usage frequency signals early churn risk, negative sentiment indicates growing frustration, reduced participation shows disengagement, and communication tone trends reveal relationship health.

What makes this approach powerful is that we're not just building a dashboard—we're proving a complete methodology for customer success analytics. The insights we generate will demonstrate that CSAT surveys are lagging indicators, while behavioral monitoring and sentiment analysis provide leading indicators of churn risk.

We'll identify at-risk customers 2-3 weeks before they reduce activity, show clear correlations between support resolution quality and long-term retention, and provide actionable insights rather than just reporting. Most importantly, we'll recommend specific A/B tests to establish causal relationships rather than claiming causation from correlation alone.

Timeline: What Gets Delivered Each Week

The four-week timeline is designed to show continuous value delivery while building toward an impressive final result. Each week has specific deliverables that prove different aspects of modern analytics capabilities.

Week 1 focuses on getting the foundation right and delivering the first working prototype. I'll build the basic data pipeline from GitHub to CSV export to Evidence.dev creating a simple customer engagement dashboard that proves the concept works. This week is about showing that we can get real data flowing and generate basic insights quickly using lo-fi prototyping for rapid feedback.

Week 2 introduces LLM integration for sentiment analysis of customer communications and automated insight generation. The key here is demonstrating AI capabilities while being scientifically rigorous—recommending A/B tests to validate insights rather than claiming causation from correlation alone.

Week 3 dives into advanced customer analytics, where we'll conduct correlation analysis between support resolution quality and future engagement patterns. This is where we'll identify Critical-to-Quality (CTQ) factors, proving that we can move beyond basic reporting to actual business intelligence.

Week 4 delivers the executive-ready dashboard using Looker Studio as a proxy for Power BI or Tableau. This week proves we can scale the approach to enterprise visualization tools while providing customer success alerts and a clear strategy for production implementation.

The Experiment Rules

This entire project operates under strict constraints that mirror real-world proof-of-concept limitations. The budget limit is €5/month maximum, and I'll track every cent to prove modern analytics doesn't require massive infrastructure investment. The time constraint is four weeks total—typical for most proof-of-concept timelines.

The success criteria is simple but challenging: convince executives to fund proper analytics infrastructure. If someone can look at the final dashboard and say "I want this for our real customer data," then we've succeeded. Everything will be documented publicly as I build it—including code, costs, screenshots, and failures—to prove the methodology is repeatable.

🎯 Success Metric: If an executive can look at this dashboard and say "I want this for our real customer data," we've won.

Most importantly, the methodology proves analytical capabilities rather than dependence on specific data sources. The techniques, insights, and business value we demonstrate will transfer directly to any customer analytics implementation.

Why This Matters

Companies that master customer analytics first will have a massive competitive advantage in customer retention and growth. The difference between reactive customer success (responding to churn after it happens) and predictive customer success (preventing churn before it starts) is the difference between surviving and thriving in competitive markets.

This isn't just about building a demo—it's about proving that modern analytics can transform how businesses operate, even under tight resource constraints. The €5 budget limit isn't a limitation; it's a feature that forces focus on what actually delivers value versus what looks impressive in vendor demos.

Next up: Why lo-fi prototyping isn't a waste of time—how it actually leads to more empathy and feedback, resulting in better end products and shorter perceived time-to-value.

(it's also just a great excuse to play around with Evidence.dev)