About this Portfolio

I design and lead enterprise analytics programs that help organizations move from static reporting to decision intelligence.

My work focuses on building scalable analytics capabilities – combining ERP data, analytics models, automation, and stakeholder alignment – to support leadership, operational, and strategic decisions.

Profile

Senior Analytics & BI Leader with over a decade of experience designing and leading enterprise analytics programs across sales, customers, inventory, and operations. Proven ability to translate complex business challenges into scalable decision systems by combining ERP-driven data, analytics modeling, automation, and stakeholder alignment.

My work focuses on moving organizations from static reporting to decision intelligence – where data actively informs prioritization, risk management, and growth.

Analytics Leadership Perspective

Rather than treating analytics as dashboards or isolated insights, I approach it as a business capability built on three pillars:

  • Decision-first design – KPIs and models aligned to how leaders actually make decisions
  • Platform enablement – analytics that scales without increasing manual effort
  • Trust & context – ERP-aligned data, consistent metrics, and explainable logic

This approach ensures analytics remains relevant, sustainable, and actionable, even as data volume and complexity grow.

Core Analytics Programs Led

This program consists of multiple integrated analytical capabilities, working together as a single decision system:

Designed a unified performance intelligence framework enabling leadership to monitor and explain sales performance across Retail, Online, Marketplace, B2B, and Clinics.

Key outcomes:

  • Flexible period comparisons beyond fixed PY/PM/PQ models
  • Fair (adjusted) performance comparisons across unequal periods
  • Contribution-based focus (ABC analysis) to direct leadership attention
  • Reduced dependency on manual and ad-hoc reporting

Impact: Faster decision cycles, shared performance language, clearer accountability.

Built a customer-centric analytics framework to understand customer value, lifecycle behavior, churn, and growth opportunities.

Key capabilities:

  • RFM-based segmentation with actionable customer groups
  • Absolute and relative churn/retention analysis
  • Product affinity and cross-sell intelligence

Impact: Smarter targeting, improved retention focus, reduced waste in broad campaigns.

Transformed inventory analytics from reactive stock reporting into risk-aware supply chain decision intelligence.

Key capabilities:

  • Vendor fulfillment reliability analysis
  • Scenario-driven reorder point logic
  • Overstock, stockout, and working capital risk visibility
  • Expiry-aware stock allocation models to mitigate short-dated inventory risk

Impact: Better inventory utilization, reduced expiry losses, more disciplined procurement decisions.

4. Operational Intelligence & Pattern Detection

Implemented analytics capabilities focused on early signal detection, enabling teams to spot trends, anomalies, and risks before impact.

Key capabilities:

  • Multi-dimensional trend and variance analysis
  • Outlier detection and exception-based views
  • Temporal pattern analysis for operational planning

Impact: Faster issue detection, reduced reliance on intuition, improved operational awareness.

5. Data Engineering, Analytics Automation & Platform Enablement

Designed and led the analytics platform foundation to ensure scalability, reliability, and low operational overhead.

Key capabilities:

  • ERP-to-analytics integration (Dynamics AX / D365)
  • Automated data ingestion and transformation using Microsoft Fabric
  • Centralized semantic models and metric standardization
  • Significant reduction in manual data preparation and reporting

Impact: 70–90% reduction in manual effort, improved data trust, analytics ready for AI-assisted use cases.

Role & Responsibilities

Across these programs, I have consistently led initiatives end-to-end, including:

  • Business problem framing and KPI definition
  • Analytics architecture and platform design
  • Data modeling and semantic layer strategy
  • Automation and scalability planning
  • Stakeholder alignment and adoption

Technology Context

Analytics initiatives delivered using combinations of:

  • Microsoft Fabric (Lakehouse, Pipelines, Warehouses)
  • Power BI (Semantic Models, Advanced BI)
  • SQL-based data modeling
  • ERP systems (Dynamics AX / D365)

Value Proposition

I help organizations:

  • See what truly matters in their data
  • Act earlier, not later
  • Reduce operational friction in analytics delivery
  • Build analytics capabilities that scale with the business

Positioning

Best aligned to roles such as:

  • Head of Analytics / BI Strategy
  • Analytics & Decision Intelligence Lead
  • Senior BI / Data Analytics Manager
  • Data & Analytics Transformation Lead