DATA FOUNDATIONS
Measurement Layer of Modern Decision Systems
Before a model can simulate a scenario, estimate risk, or optimize a plan, the underlying data must be understood, structured, validated, and governed so that it’s reliable and aligned to the “ask“.
This page outlines the two complimentary disciplines, Data Science & Data Analytics, that work together to create meaningful, reliable measurement systems for operational, strategic AI-era decisions.
Measurement Pipelines in Practice, a non-exhaustive ideation:
• How raw data becomes interpretable
• How measurement choices shape model outcomes
• How to align measurement with decision intent
• Quality, governance, and biases that distort, distract, &/or diffuse insights
Tools & Techniques, a non-exhaustive sampling:
- Data engineering & storage
- PostgreSQL
- AWS (data pipelines, storage layers, compute)
- Data processing & modeling
- Python, R
- Excel – addins, DAX, PowerPivot
- Governance, lineage, and integrity
- GitHub (versioning, collaboration, and change control)
- Blockchain (immutability, event audit trails)
- Data engineering & storage
Exploration, a non-exhaustive sampling:
- Metric design
- Feature engineering
- Uncertainty quantification
- New measurement challenges emerging in the AI era
- Four major anticipated/projected classes
- Interference (feedback loops, performative prediction, gaming & adversarial adaptation)
- Variable entanglements (human, tech, context)
- Drift, shift, and environmental volatility (distribution, model, data, &/or regime)
- Epistemic: what’s “true” in AI-mediated systems (proxy, black-box, synthetic, label)
- Four major anticipated/projected classes
Actionable takeaways, for inspiration:
- Assess your measurement readiness
- Identity gaps in your data systems
- Governance
- Quality
- Bias
- Identity gaps in your data systems
- Strengthen your data for better decisions
- Internal frameworks, external deliverables
- VaR
- Optimization
- Scenario planning
- Internal frameworks, external deliverables
- Evaluate you data models’ readiness
- Integrity for the age of AI
- GRC (governance, risk, compliance)
- Drift
- Bias, hallucination
- Integrity for the age of AI
- Assess your measurement readiness
Data foundations must evolve beyond ‘classics’ to adapt and overcome the new challenges AI introduces.
If you’d like to see this modeling applied in real contexts, visit Applied Research Topics
If you’re looking for tools, frameworks, or recommended references, visit Resources
Systems work at the boundary of people, policy, and technology.
Porteolas · Operations Research · Decision Assurance · AI-era Readiness
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