AI-Powered Enterprise Data Modernization with Microsoft Fabric

A leading U.S. healthcare software provider needed to modernize its fragmented enterprise data ecosystem and establish a unified analytics platform across finance, operations, customer, and ERP systems. CES applied its Engineering.AI framework across data architecture, engineering, governance, analytics, and reporting to accelerate data-driven decision-making and build a scalable enterprise data foundation on Microsoft Fabric.

Scroll down for the whole story

The Challenge

15+ systems created fragmented data silos

8 business units lacked unified visibility.

400+ reporting hours/month spent on manual effort.

the client

Healthcare Software Provider

United States

Technology Stack

Microsoft Fabric
Fabric Lakehouse
Dataflow Gen2
Delta Lake
PySpark
Power BI
Dynamics 365 ERP
Finance & Operations
Dataverse
SharePoint

Solution Area

Enterprise Data Modernization & Analytics Transformation

the impact

8

business units unified through a centralized enterprise data platform

75%

reduction in report preparation and delivery time

40%

improvement in data accuracy and consistency

400+

manual hours eliminated monthly

how we did it

DATA UNIFICATION WAS ENGINEERING-LED. THE RESULT?

ENTERPRISE-WIDE VISIBILITY AT SCALE.

The Need & The Challenges

The CES Solution

Results & Business Impact

The Need

The organization operated across 8 business units and multiple acquisitions, with critical data spread across Dynamics 365 ERP, Finance & Operations, Dataverse, SharePoint, customer platforms, and 15+ other systems. This fragmented landscape limited enterprise-wide visibility, created inconsistent business metrics, and required reporting teams to spend over 400 hours per month gathering, reconciling, and validating data. Leadership needed a scalable analytics foundation that could unify enterprise data, deliver trusted insights, enable self-service reporting, and support future growth and acquisitions.

Challenges

Legacy Data Silos & Fragmented Information: Customer, operational, ERP, and financial data existed across 15+ disconnected systems, making it difficult to establish a unified enterprise view. Different business units maintained separate reporting processes, resulting in inconsistent data definitions and limited cross-functional visibility.
Reporting Inefficiencies & Manual Preparation: Business users depended heavily on manual data extraction, reconciliation, and report creation activities. Reports often required 3-5 days to prepare and validate, delaying decision-making and consuming valuable operational resources.
Data Governance, Scalability & Enterprise Visibility: As the organization expanded through acquisitions, data complexity increased. The enterprise required a scalable analytics architecture capable of processing 12+ TB of enterprise data, standardizing information, improving quality, enforcing governance, and supporting growing reporting demands.

CES implemented an Engineering.AI-led Microsoft Fabric modernization strategy to transform fragmented enterprise data into a centralized, intelligent analytics ecosystem.

1. Enterprise Data Discovery & Architecture Planning

Conducted enterprise-wide assessments across ERP, Finance & Operations, Dataverse, SharePoint, customer platforms, and legacy applications.
Mapped dependencies across 8 business units and 15+ source systems.
Defined a scalable modernization roadmap aligned with growth and acquisition objectives.
Established governance standards for enterprise-wide data quality and consistency.

2. Unified Data Architecture with Microsoft Fabric

Designed and implemented a Microsoft Fabric-based enterprise analytics platform.
Consolidated data from Dynamics 365 ERP, Finance & Operations, Dataverse, SharePoint, and customer applications into a centralized architecture.
Implemented Microsoft Fabric Lakehouse with Delta Lake as the unified enterprise storage layer, creating a single source of truth for structured and semi-structured data.
Centralized and governed 12+ TB of enterprise data within a scalable analytics environment.
Created an extensible architecture capable of supporting future acquisitions, expansion initiatives, and additional enterprise data sources.

3. Data Engineering & Intelligent Transformation

Implemented Fabric Dataflow Gen2 and Microsoft Fabric Pipelines to automate enterprise data ingestion, orchestration, scheduling, and monitoring across 15+ source systems.
Leveraged Microsoft Fabric Lakehouse and Delta Lake architecture as the centralized data engineering foundation for scalable storage, versioning, and high-performance analytics.
Developed PySpark-based transformation frameworks for data cleansing, standardization, validation, deduplication, enrichment, and business rule enforcement.
Automated processing of 20M+ records monthly, eliminating manual integration and reconciliation efforts.
Established standardized business definitions and common enterprise data models across financial, operational, and customer domains.
Reduced data preparation effort by 70%, significantly improving reporting readiness and data reliability.

4. Enterprise Analytics & Reporting Enablement

Built governed Power BI Semantic Models to provide trusted, reusable datasets and enable secure self-service analytics.
Delivered 25+ interactive Power BI dashboards supporting executive reporting, operational performance management, and business-unit-level analytics.
Enabled near real-time visibility into financial, operational, and customer performance metrics.
Reduced report preparation and generation time by 75%.
Eliminated repetitive manual reporting activities, allowing business users to focus on analysis and strategic decision-making.

5. Governance, Scalability & Operational Excellence

Established data governance controls to improve consistency, reliability, and trust in enterprise reporting.
Standardized data management practices across acquired business entities and operational teams.
Implemented scalable Microsoft Fabric architecture patterns capable of supporting 12+ TB of enterprise data and future growth requirements.
Created a future-ready analytics foundation supporting advanced reporting, AI, predictive analytics, and continuous modernization initiatives.

Single Source of Truth: A centralized Microsoft Fabric platform unified data from 15+ enterprise systems and 8 business units, providing a trusted enterprise-wide view of operational, customer, and financial performance.
Improved Reporting Efficiency: Automated ingestion, transformation, and reporting workflows reduced report preparation and delivery cycles by 75%, enabling stakeholders to access insights in hours rather than days.
Enhanced Data Accuracy & Consistency: Standardized business rules xgoverned semantic models, and PySpark-driven data quality processes improved data accuracy by 40% while ensuring consistency across business units and acquired entities.
Increased Enterprise Visibility: Leadership gained near real-time visibility into enterprise performance through centralized analytics and interactive Power BI dashboards spanning financial, operational, and customer metrics.
Reduced Manual Effort: Automation across ingestion, transformation, and reporting processes eliminated more than 400 manual hours per month, improving operational efficiency and reducing reporting bottlenecks.
Future-Ready Analytics Foundation: The organization established a Microsoft Fabric-powered analytics ecosystem capable of managing 12+ TB of enterprise data and processing 20M+ records monthly, positioning the business to support future acquisitions, enterprise growth, advanced analytics, AI initiatives, and evolving reporting requirements.

view all case studies

Data silos eliminated. Enterprise insights accelerated.
Engineering.AI modernized reporting and analytics with Microsoft Fabric to create a scalable, trusted data foundation.