Data-Driven SME Intelligence: Leveraging Machine Learning for Operational Insight Extraction in Small and Medium-Sized Enterprises

Daniel A. Morales Ávila

Elysium λ Development & Research — Portugal, European Union

Abstract

Research Problem & Motivation

II.

Theoretical Framework

III.

Research Questions

RQ1

What minimum data volume and quality thresholds are required for machine learning models to generate reliable operational insights in typical SME digital environments?

RQ2

Which machine learning methodologies — supervised classification, clustering, anomaly detection, or time-series forecasting — produce the most actionable and interpretable outputs for non-specialist SME operators?

RQ3

To what extent does the provision of natural language explanations alongside ML model outputs increase SME operator confidence in, and adoption of, data-driven recommendations?

IV.

Methodology

Data Sources & Collection Protocol

VI.

Preliminary Observations

VII.

Limitations

VIII.

Future Work

References

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