Accelerating Enrollment Growth with Modern Data Engineering & Analytics
Project Overview
Accelerating Enrollment Growth with Modern Data Engineering & Analytics
A premier global sports education institution—offering elite boarding programs, training camps, and professional athlete development—faced declining enrollment and outdated analytics infrastructure. Compounded by merger-driven cloud migration deadlines, their fragmented data systems hindered predictive capabilities and operational efficiency. The project focused on modernizing the complete data landscape, migrating from Azure to AWS, upgrading BI systems, and enabling AI/ML-powered insights to strengthen enrollment and revenue outcomes.
The Challenge
The organization faced several hurdles in delivering efficient support:
Declining Enrollment
Enrollment rates dropped from 6% to 4%, directly impacting revenue goals and long-term growth.
Mandatory Cloud Migration
M&A activity required a rapid shift from Azure to AWS within strict contract deadlines.
Legacy Reporting Limitations
Excel-based reports restricted visibility, slowed decisions, and lacked predictive insights for Sales & Marketing teams.
Limited AI & ML Readiness
The existing data architecture couldn't support advanced analytics, ML models, or GenAI opportunities.
Our Solution
Transformed legacy Excel reporting into dynamic Tableau dashboards for Sales & Marketing intelligence.
Risk-Controlled Cloud Migration
Executed a full-scale migration of pipelines, Data Warehouse, BI systems, and ML models from Azure to AWS within 16 weeks.
BI Modernization
Rebuilt the entire Sales & Marketing reporting ecosystem in Tableau with new data models, semantic layers, KPIs, and interactive dashboards.
Predictive Modeling
Developed Propensity-to-Enroll and Price Elasticity ML models using XGBoost to refine targeting and pricing strategies.
Data Architecture Transformation
Reengineered Azure Databricks pipelines into PySpark/EMR workflows and replaced Excel-based reporting with automated dashboards.
Implementation Process
Cloud Migration Workflow
Transferred Snowflake datasets, rebuilt ETL pipelines in AWS EMR + dbt, and redeployed ML models with SageMaker.
Data Model Redesign
Created new dimensional models and reporting layers aligned to Sales & Marketing processes.
Dashboard Reengineering
Converted static Excel reports into dynamic Tableau dashboards with actionable KPIs.
Predictive Solution Deployment
Integrated scoring pipelines for enrollment propensity and pricing elasticity into operational workflows.
Results
Migration Completed Early
Project executed 30 days ahead of schedule, reducing migration risk and business disruption.
Enhanced Analytics Ecosystem
A unified, scalable AWS-based data architecture supporting ML and GenAI initiatives.
Improved Enrollment Conversion
AI-powered insights optimized targeting, resulting in higher conversion efficiencies.
Better Revenue Forecasting
Advanced dashboards strengthened prediction accuracy and accelerated insight-to-action cycles.
Technology Used
AWS, Snowflake, Tableau, SageMaker, Airflow, PySpark, EMR, DBT, MWAA, Python
