Data Intelligence & Insights

Structured data audit cataloguing your sources, assessing quality and completeness, scoring taxonomy and governance maturity, and identifying gaps between data available and data needed. A Data Quality Scorecard is produced with a prioritized remediation plan that scopes data infrastructure work alongside the analytics programme.

For each prioritized use case, churn prediction, demand forecasting, customer segmentation, sentiment analysis, fraud detection, feature engineering pipelines and appropriate modelling approaches selected across classical ML (XGBoost, Random Forest, GBMs), transformer-based NLP for Arabic and English, unsupervised clustering, RFM segmentation, and causal inference.

Model outputs translated into executive-facing dashboards designed around specific decision workflows, not generic BI reports. Decision Intelligence framework maps each dashboard to the decision it supports, the action it should trigger, and the KPIs that measure whether the action worked. Validated with actual decision-makers before finalization.

Models and dashboards deployed with monitoring infrastructure tracking model accuracy, data pipeline health, and usage patterns. Monthly Insight Packs, curated summaries of significant findings with recommended actions, produced for leadership. Forecast outputs carry confidence intervals so decision-makers understand the certainty envelope, not just the point estimate.