Good Enough Data Strategy

Ethical AI & Data Governance

Introduction

Data development begins and ends with people—or data development stalls.
The complexity of data systems is not just a technical challenge; it is a human one.

At Good Enough Data & Systems Lab, we advocate for an opinionated, test-driven approach that prioritizes:

✅ Democratizing knowledge → Ensuring all stakeholders can meaningfully contribute to data products.
✅ Parallelizing development → Structuring workflows so that bottlenecks are eliminated.
✅ Prioritizing developer and analyst wellbeing → Recognizing that sustainable, high-quality data work requires ethical treatment of both people and data.

🚀 This is not just an alternative to conventional data strategies—it is a necessary shift toward scalable, ethical, and transparent AI & data governance.

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🏗️ Opinionated, Test-Driven Data Development

A test-driven approach ensures that data development remains structured, reproducible, and verifiable.

🔹 Why Test-Driven Data Development?

🔹 Prevents Questionable Analytical Observations (QAOs) → If data lineage isn’t tested, analysis results cannot be trusted.
🔹 Encourages Transparency → Automated tests act as living documentation, reducing siloed knowledge.
🔹 Reduces Data Debt → Catching problems early prevents technical debt from accumulating.

🔹 How This Works in Practice

1️⃣ Define “Version 0” of a Data Product
- Establish FAIR (Findable, Accessible, Interoperable, Reusable) validation criteria.
- Scope minimal requirements for productionalization.

2️⃣ Automate Data Quality Tests
- Implement test-driven data lineage to ensure transformations produce expected results.
- Design edge-case simulations to validate statistical robustness.

3️⃣ Treat Analytics as Software
- Version-control dashboards and prevent ad-hoc analysis drift.
- Use simulation-based validation to test statistical models under different conditions.

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