Emmanuel Bassey

Emmanuel Bassey

Revenue Intelligence & Strategic BI Analyst

Excel • Tableau • Power BI • SQL • R

GitHub Kaggle LinkedIn

About Me

Revenue Intelligence & Strategic BI Analyst

I identify billion-dollar margin recovery opportunities in clinical datasets.

When health or marketing data is poorly visualized, leadership loses the ability to act with precision.

I architect Decision Support Systems (DSS) that dismantle this friction. By building Intelligent analytic layers and dashboards for complex datasets, including Neuro-diagnostic stratification (Alzheimer’s) and Actuarial Risk Modeling (Preventative Health). I provide stakeholders with the "Sovereign Clarity" needed to drive policy and operational growth.

Core Specialization:

Currently engaging with global Health-Tech partners as a Sovereign Contractor (seamless onboarding via Deel).

Download CV

Featured Projects

🧠 Neuro-Diagnostic Decision Support System (DSS): Alzheimer’s Stratification(Flagship)

An interactive Power BI dashboard designed to analyze Alzheimer’s disease data, compare healthy vs damaged brain indicators, and support clearer interpretation of complex neurological health information.

Problem

Alzheimer’s datasets are complex and difficult for non-technical stakeholders to interpret, often limiting their usefulness in research and decision-making.

Approach

  • Cleaned and transformed health data using Power Query
  • Designed segmented views to compare healthy vs affected groups
  • Used tooltips and interactivity to reduce cognitive overload

Key Insights

  • Clear visual separation between healthy and damaged brain indicators
  • Patterns become more interpretable when data is grouped contextually
  • Interactive storytelling improves usability for non-technical audiences

Key Screenshot

Alzheimer's Dashboard Overview

Tools

Power BI · Power Query · DAX

🔗 View Live Dashboard
🔗 View GitHub Repository

Healthcare Claims & Value-Based Care Intelligence Platform

An end-to-end Business Intelligence solution simulating an executive analytics engagement for a Medicare Advantage payer, transforming healthcare claims and operational data into decision-ready intelligence for value-based care, risk surveillance, and revenue protection.

Problem

Medicare Advantage organizations manage millions of healthcare claims every year while balancing quality of care, financial sustainability, regulatory compliance, and patient outcomes. Traditional operational reporting often presents historical metrics but does not provide executives with integrated decision support across clinical risk, claims performance, value-based care adoption, and revenue exposure leading to loss of revenue. This is a huge financial problem.

Approach

  • Defined the business problem and analytical framework by identifying operational challenges related to value-based care adoption, inpatient utilization, risk adjustment, and financial performance.
  • Developed SQL queries to extract, transform, validate, and structure healthcare claims data for downstream reporting and analysis.
  • Designed a relational data model and Power BI semantic layer supporting scalable KPI reporting and executive dashboards
  • Created DAX measures calculating Inpatient deflections, VBC penetration, capital preservation, RAF opportunity, star rating risk, bonus protection index, revenue exposure, and executive performance monitoring.
  • Developed interactive Power BI dashboards supporting: Regional performance monitoring Executive KPI tracking Financial scenario analysis Healthcare risk surveillance Revenue defense Operational decision support

    • Key Insights

  • Value-Based Care Creates Significant Financial Opportunity: The analysis demonstrated that increasing Value-Based Care adoption reduced projected inpatient utilization while preserving substantial operational capital across participating markets.
  • Risk is Uneven Across Markets: Healthcare risk indicators varied significantly by provider state, enabling executives to prioritize interventions where patient complexity and projected operational burden were highest.
  • Population Complexity Drives Operational Pressure Risk stratification showed that high-frailty populations were concentrated within specific regions, suggesting targeted care management strategies could improve both clinical and financial outcomes.
  • Executive KPIs Can Predict Revenue Exposure Combining RAF Opportunity Scores, Star Rating Risk, Bonus Protection Index, and Revenue Exposure created an integrated executive monitoring framework capable of identifying markets requiring immediate strategic attention.
  • A Unified Executive Dashboard Improves Decision Making Rather than reviewing disconnected operational reports, leadership could evaluate clinical performance, financial outcomes, claims trends, and organizational risk through a single integrated reporting platform.

    • Business Impact

    The platform demonstrated how integrated claims analytics could support executive decision-making by identifying high-risk markets, estimating potential financial opportunities associated with value-based care adoption, and providing a unified framework for monitoring operational performance.

    Key Screenshot

    Healthcare Claims Dashboard Overview

    Tools

    Power BI • SQL • Power Query • CMS Medicare Data • OMOP Data • Data Modeling • DAX

    🔗 Watch Executive Dashboard Walkthrough

    🚬 Predictive Actuarial Risk & Preventative Health Architecture

    A Power BI dashboard analyzing the relationship between smoking habits and health risk indicators to support public health awareness and preventive insights.

    Problem

    Smoking-related health risks are often presented as raw statistics, making it difficult to understand risk distribution across populations.

    Approach

    • Analyzed smoking behavior alongside key health metrics
    • Segmented data by demographic and risk categories
    • Focused on clarity and interpretability over visual complexity

    Key Insights

    • Higher smoking intensity correlates with elevated health risk indicators
    • Risk patterns become clearer when grouped by demographic factors
    • Dashboards can support preventive health decision-making

    Key Screenshot

    Smoking Risk Dashboard Overview

    Tools

    Power BI · Power Query · Excel

    🔗 View Live Dashboard

    🧠 Oncology Spatial Analytics: High-Precision Clinical Mapping

    A highly interactive Power BI dashboard designed to analyze brain tumor distribution across tumor grades, anatomical regions, age groups, and diagnostic certainty to support clinical and research-level decision-making.

    Problem

    Brain tumor data is often fragmented across grades, regions, and diagnostic states, making it difficult to identify high-risk populations, dominant tumor severities, and critical anatomical patterns at a glance.

    Approach

    • Built grade-based and diagnostic-status slicers to enable scenario-driven analysis
    • Analyzed tumor distribution across key brain regions and age groups
    • Designed KPI-focused visuals to surface severity, volume, and temporal trends
    • Prioritized interactivity to support rapid clinical insight extraction

    Key Insights

    • Grade IV tumors are the most malignant, indicating advanced-stage diagnoses
    • The 60+ age group is the most affected in malignant tumors, highlighting a critical intervention window
    • The Cerebrum and Brainstem show the highest tumor incidence among anatomical regions
    • Gliomas account for the majority of malignant tumor types, reinforcing known clinical prevalence patterns
    • Confirmed vs unconfirmed diagnosis filters significantly alter trend interpretation over time

    Key Screenshot

    Brain Tumor Dashboard Overview

    Tools

    Power BI · Power Query · DAX · Excel

    🔗 View Live Dashboard

    Global CO₂ Emissions Analysis

    Interactive visualization showing CO₂ emissions trends by country and year.

    Key Insight

    Identified long-term emissions growth patterns across major economies and highlighted outlier countries.

    Tools: Tableau

    Open in Tableau Public

    Cyclistic Bike Share Analysis

    Problem: Analyze differences between casual and member riders.

    Tools: Excel, PowerPoint

    Outcome: Identified usage patterns to support marketing decisions.

    View Project

    Kaggle Data Exploration Project

    Problem: Clean and explore public datasets for insights.

    Tools: Excel, R, PowerPoint

    Outcome: Extracted patterns and visualized key metrics.

    View Project

    Skills

    • Excel: Pivot Tables, Advanced Formulas, Charts
    • Power BI: Power Query, Dashboards
    • SQL: Queries, Joins
    • Tableau: Data Visualization, Dashboards
    • Python: Learning (Pandas, Data Cleaning)
    • R: Data Cleaning, Data Visualization, Reports