Data, Analytics & AI
Turn analytical insight into a better operating decision.
Luna Sol connects data quality, forecasting and AI use cases to the decisions that move a business. The starting point is the operating problem, the user and the action the analysis should support.
Representative photography / Unsplash
WHAT WE DO
Connect the business problem to an executable response.
More data does not automatically produce better decisions. Competing definitions, unstable baselines and models without an action owner can make a business more confident without making it more effective. We connect the analytical question to a defined decision, measurable consequences and a workable response.
Our work spans decision-centered analytics, predictive operations, model evaluation and data governance. The scope of engineering, integration and specialist support is established for each engagement. A model moves forward when its evidence, economics and operating controls support its intended use.
Data foundations for decisions
Define the business question, reconcile source systems and standardize the unit of analysis. Make lineage, access, freshness and metric ownership explicit.
TYPICAL OUTPUTS
Decision map, data requirements and measurement controls
Prediction & AI evaluation
Assess whether forecasting or AI adds value beyond a credible baseline. Compare performance at the decision threshold, including false alarms, missed events and human review workload.
TYPICAL OUTPUTS
Use-case assessment, evaluation plan and release criteria
Analytics in the workflow
Connect outputs to accountable users, action queues and escalation paths. Establish monitoring for data changes, model drift and the quality of downstream decisions.
TYPICAL OUTPUTS
Decision workflow, monitoring specification and ownership model
OUR APPROACH
Diagnose precisely. Design for use. Verify what changes.
A practical sequence, adapted to the operating question and the evidence available.
01
Define the decision
Specify who will act, what options they have, when the outcome matures and what an incorrect decision costs.
02
Validate the signal
Check definitions, coverage, missingness and population changes. Compare a proposed model with a simple operational baseline.
03
Evaluate the complete workflow
Test model output together with human review and the action it triggers. Measure valid decisions across all requests, not accuracy only on answered cases.
04
Govern adoption & learning
Set release gates, monitoring, fallback behavior and ownership. Expand use only when observed performance and incremental economics justify it.
EXECUTIVE PROJECT EXPERIENCE
The work behind the approach.
These cases document real work Ryan Miller performed at Amazon in prior executive roles. Each report describes the operating challenge, his contribution and the evidence supporting its conclusions.
15 executive project cases
AI Model Selection & Release Governance
An anonymized project case study of forecast-model research: comparative testing, a constrained-runtime champion, a partial engine handoff and an evidence-based decision to defer neural development.
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Customer Defect Analytics & Prioritization
An anonymized operating case on reconciling fragmented defect categories, responding to executive review, and connecting an analytical finding to a testable action and accountable owner.
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Data Access & Investment Readiness
An anonymized operating-design case on an analytics program blocked by source fitness and access. The investigation identified a candidate incumbent model, corrected the data roadmap and specified an application-identity access plan.
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Data Delivery & Service Continuity
An anonymized data-architecture case study of a multi-source operating platform: testing interactive and unattended access, reconciling partial data coverage, and preserving an explicit snapshot fallback when the intended live feed remained unavailable.
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Decision-Centered Analytics
An anonymized operating case on an executive performance readout, its rejection at review, and a proposed evidence contract that connects analytical quality to the decision a sponsor must make.
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KPI Definition & Reporting Reliability
An anonymized operating case on an executive reporting application whose KPI names, filters and aggregations produced conflicting answers. The register describes identifier conventions, arithmetic checks and explicit unavailable states for unsupported values and attribution.
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Leading Indicators for Performance Management
An anonymized project case study of a service network’s leading-indicator audit: reconciling reporting clocks, model populations and metric definitions before using analytical findings to direct operating investment.
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Metric Baselines & Signal Validation
A recurring performance-reporting case that separates source version, event cohort, maturation, goal basis and action evidence. It tests the value of resolving uncertainty before publishing a direction.
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Operational Command-Center Governance
An anonymized operating-design case study of a leadership command center: reconciling customer-quality measures, incomplete reporting periods and agent-assisted analytics before translating dashboard signals into operating decisions.
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Operational Measurement Assurance
An anonymized operating case on conflicting logistics measurements. The project established a reference canon for table choice, counting unit, filters and interpretation, and documented corrections that changed how reports should be used.
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Performance Measurement & Credit Integrity
An operating-project case on challenging benefit attribution after a delivery-control launch. It separates customer outcomes, claim access and reporting classifications, reconciles benefit claims, and proposes a controlled test of credit-review decisions.
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Performance Shift Diagnostics
An operating-project case on investigating abrupt delivery-claim changes across business lines. It tests competing physical and reporting explanations, reconciles daily and weekly measures, and designs a controlled response that protects service while evidence matures.
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Predictive Operations & Decision Support
An anonymized project case study of an operational forecasting engine: designing prediction, data-source and alerting controls while separating runnable software from validated live decisions and customer outcomes.
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Preventive Action Design & Controls
An anonymized operating-design case study of a versioned action library: policy-linked instructions, cross-functional ownership, agent-assisted construction and an incomplete handoff into the operating application.
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Self-Service Analytics & Decision Quality
An anonymized operating case on adopting AI-assisted self-service analytics. The register describes a working installation, adversarial data checks and documented defects, while evidence of submitted reports, accepted fixes and wider adoption remains incomplete.
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START WITH THE DECISION