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Enterprise Cloud Modernization

Rebuilding an analytics platform people trusted again.

Promethean's analytics ecosystem had become difficult to trust. Legacy Cognos reports, inconsistent KPI definitions, and an aging on-premises platform slowed decision making.

I led the end-to-end modernization to Microsoft Azure and later Microsoft Fabric, rebuilding data pipelines, semantic models, and Power BI experiences that restored confidence across Finance, Sales, Operations and Executive leadership.

Executive Summary

Technology wasn't the biggest problem. Trust was.

Promethean's analytics platform had evolved over many years, resulting in inconsistent KPI definitions, aging on-premises infrastructure, and declining confidence in enterprise reporting. Business users frequently questioned Cognos reports because different teams interpreted the same metrics differently.

Rather than simply migrating existing reports, I partnered with stakeholders across Finance, Sales, Operations, and Executive Leadership to redefine business metrics, modernize the analytics architecture, and establish a governed semantic layer that aligned reporting with real business processes.

I led the complete cloud modernization journey—from Microsoft SQL Server, SSIS, and Cognos to Microsoft Azure, and later to Microsoft Fabric—designing and implementing the platform architecture, data pipelines, semantic models, security, Power BI reporting, and deployment strategy from the ground up.

The outcome was more than a successful cloud migration. It became a trusted analytics ecosystem that provided every department with a single source of truth and empowered leaders to make decisions with confidence.

Business Challenge

Modernizing technology was only half the challenge. Restoring confidence in data was the real mission.

Before

Legacy Analytics Platform

SQL Server
SSIS Packages
IBM Cognos Reports
Outdated KPI Logic
Low Business Trust

After

Modern Analytics Platform

Microsoft Azure
Microsoft Fabric
Enterprise Semantic Models
Power BI
Single Source of Truth

While cloud migration was the technical objective, the business success of the project depended on rebuilding confidence in enterprise reporting. By partnering directly with business stakeholders, redefining KPI logic, and designing semantic models around real business processes, the platform evolved from a collection of disconnected reports into a trusted decision-making system.

Modernization Journey

From legacy infrastructure to a modern cloud analytics platform.

1

On-Premises

Legacy Platform

Microsoft SQL Server
SSIS
IBM Cognos
Disconnected KPI Logic
2

Microsoft Azure

Cloud Modernization

Azure Data Factory
Azure Data Lake
Azure SQL
Power BI
3

Microsoft Fabric

Platform Evolution

Lakehouse
Warehouse
Semantic Models
Power BI

The modernization was completed in two strategic phases. We first established a scalable Azure-based analytics foundation to replace the legacy on-premises platform. As Microsoft's data ecosystem evolved, we transitioned to Microsoft Fabric, adopting a unified architecture built around Lakehouse, Warehouse, semantic models, and Power BI while preserving consistent business logic across the organization.

Azure Architecture

Building a cloud-native analytics platform from the ground up.

I designed and implemented the complete Azure analytics architecture—from ingestion through reporting—creating a scalable, governed, and secure platform capable of supporting enterprise analytics across multiple business functions.

ERP

CRM

Sales

Finance

Azure Data Factory

Azure Data Lake

Azure SQL

Semantic Models

Power BI

Supporting Services

• Azure Blob Storage
• Azure Key Vault
• Azure Functions
• Azure Monitor
• Power BI Gateway

Engineering Ownership

• Architecture Design
• Azure Infrastructure
• GitHub Version Control
• Capacity Planning
• Licensing Strategy

Platform Evolution

Evolving from Azure into the Microsoft Fabric ecosystem.

As Microsoft's unified analytics platform matured, the organization decided to adopt Microsoft Fabric. Rather than performing a simple migration, I redesigned the analytics architecture to take advantage of Fabric's integrated data engineering, warehousing, semantic modeling and Power BI capabilities.

Why Fabric

• Unified analytics platform
• Simplified architecture
• Better collaboration
• Reduced operational complexity
• Native Power BI integration

My Responsibilities

• Redesigned platform architecture
• Rebuilt ingestion pipelines
• Implemented Lakehouse strategy
• Created enterprise semantic models
• Migrated Power BI reporting

Microsoft Fabric

Lakehouse

Warehouse

Data Pipelines

Semantic Models

Power BI

Because I had already designed the Azure platform, I was able to transition the organization into Microsoft Fabric while preserving trusted business logic, minimizing user disruption, and ensuring reporting consistency across Finance, Sales, Operations, and Executive Leadership.

Enterprise Data Modeling

Building a semantic foundation for consistent business decisions.

Rather than replicating legacy database structures, I redesigned the analytical layer around how business teams actually consume information. Every model, relationship, and KPI was intentionally built to improve consistency, performance, and long-term governance.

Star Schema Design

Designed subject-oriented data models that simplified analytics while improving performance and consistency.

Fact & Dimension Modeling

Created reusable enterprise fact tables and conformed dimensions to establish a governed reporting foundation.

Slowly Changing Dimensions

Implemented SCD strategies to accurately preserve historical business context and reporting accuracy.

Incremental Processing

Built efficient incremental loading strategies that reduced processing time while supporting near real-time analytics.

Data Quality

Introduced validation checkpoints throughout the pipeline to improve reliability before data reached business users.

Governed Semantic Layer

Standardized KPI definitions and business terminology through semantic models shared across Finance, Sales, Operations and Executive reporting.

The Biggest Win

The semantic layer became the organization's shared business language. Instead of every department calculating KPIs differently, Finance, Sales, Operations and Executive Leadership all relied on the same governed definitions, creating a true single source of truth across the enterprise.

Enterprise Reporting

Modern BI Experience

Promethean standardized executive reporting across Power BI and Tableau, enabling governed dashboards alongside interactive visual exploration.

P

Power BI Desktop

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Promethean Analytics

Executive Dashboard

Executive Performance Overview

Last Refresh • 5 mins ago

Revenue

$148.2M

+12.4%vs Previous Period

Gross Margin

38.7%

+3.1%vs Previous Period

Orders

82,419

+8.6%vs Previous Period

Inventory

99.4%

Healthyvs Previous Period

Revenue Trend

Product Mix

Total

100%

Product Categories

Interactive Displays92%
Software74%
Accessories56%
Services44%

Manufacturing Operations

Technology Stack

Enterprise Analytics Platform

Azure Data Factory
Azure SQL
Azure Data Lake
Azure Blob Storage
Azure Functions
Azure Key Vault
Azure Monitor
Microsoft Fabric
Power BI
Tableau
SQL
GitHub

Business Outcomes

Lasting Business Impact

Single Source of Truth

Unified legacy reporting into governed semantic models trusted across departments.

Business Trust Restored

Partnered with business stakeholders to redefine KPI logic and eliminate reporting inconsistencies.

Cloud Modernization

Led the transition from an on-premises BI platform to Azure and Microsoft Fabric.

Enterprise Adoption

Delivered executive dashboards that became the preferred reporting experience across Finance, Sales, Manufacturing, and Operations.