Aerospace Manufacturer Transformed Data Management with Microsoft Fabric to Drive AI and Automation

Overview

An aerospace manufacturer specializing in custom alloys—used in everything from high-tech aircraft components to military defense—needed to modernize its data platform to support rapid growth, stringent security requirements, and future-focused AI initiatives. Faced with aging infrastructure and siloed, vendor-centric data systems, they were looking to design and deploy a transformative data architecture using Microsoft Fabric.

By centralizing and unifying data from disparate systems, they have dramatically streamlined data-driven decision-making, accelerated AI adoption, and reduced overall costs and time-to-value. In an industry where minutes of production time can mean millions in cost savings, this transformation has been integral to shaping the next decade of innovation and profitability.

The Challenge

Decades of proprietary systems across multiple factories had left the organization struggling to gain complete insight into operations and production. Data lived in silos: SAP handled financials and maintenance tracking, machine event logs recorded each step in the manufacturing process, and separate vendor systems managed other critical inputs. Employees relied heavily on tribal knowledge to interpret data structures, complicating on-boarding and collaboration. Meanwhile, data science teams were running Jupyter notebooks on local machines without robust security, version control, or centralized access to data. Despite the desire for AI and advanced analytics, these constraints made it nearly impossible to fully realize a data-driven culture.

Areas identified:

1. Legacy Systems & High Security:
   Multiple factories operated with strict networking protocols and extensive compliance measures, making it difficult to align on a single standard for data storage and access.

2. Siloed, Disparate Data Sources:
   Machine events, production processes, and financial transactions were tracked in isolated repositories and vendor-centric systems (SAP, local SQL databases, and more). No one had a complete, organization-wide view of data.

3. Lack of Collaboration & Governance:
   Without a unified governance framework, data definitions varied and operational tribal knowledge lived in people’s heads. It took years to learn how to query each system, leading to critical inefficiencies.

4. Slow Path to AI Readiness:
   Data science teams were running Jupyter notebooks locally, lacking security, version control, and a shared environment. Advanced analytics and AI initiatives were hampered by the complexity of accessing data securely and consistently.

The Solution

We designed a modern data platform that unified critical systems and enabled future-focused analytics. By leveraging Microsoft Fabric, the team consolidated the company’s manufacturing data—machine events, production processes, vendor feeds—into a single data lakehouse structure. Automated Fabric Pipelines ensured near real-time ingestion, while Semantic Models simplified data definitions for business users.

The solution introduced a comprehensive governance strategy, implemented through Microsoft Purview, which provided security labeling and policy enforcement that aligned with stringent government contract requirements. Data science workloads moved from local machines to secure notebooks and Spark pools within Fabric, eliminating version-control headaches and dramatically improving collaboration.

1. Unified Data Lakehouse Architecture

   • Leveraged Lakehouse and Warehouse capabilities in Fabric to centralize disparate data—machine event logs, production process data, financials, and vendor integrations—into a single, secure platform.

   • Created a modern Delta Lake Storage system with seamless integration for analytics and AI use cases.

2. Automated Data Pipelines

   • Implemented Fabric Pipelines to move data from on-premises and cloud-based systems into Microsoft Fabric, ensuring automated, near real-time updates.

   • Standardized data ingestion flows, reducing the complexity and cost of managing dozens of separate data pipelines.

3. Semantic Models for Self-Service Analytics

   • Built Semantic Models in Fabric to unify data definitions and enable business users to quickly derive insights.

   • Integrated Power BI as the default reporting layer, granting leadership a “single pane of glass” for enterprise-wide KPIs, operational metrics, and financial reporting.

4. Advanced Governance & Security

   • Utilized Microsoft Purview for data governance, security labeling, and policy enforcement, ensuring compliance with government contract requirements.

   • Introduced a formal data governance council and framework to socialize best practices, steward data ownership, and maintain consistent standards.

5. Built-in AI & Data Science Environment

   • Migrated data science workloads from locally run Jupyter notebooks to secure notebooks and Spark Pools inside Fabric.

   • Provided a centralized, controlled environment that drastically simplifies collaboration, version control, and security for data science teams.

The Results

With Microsoft Fabric as the central data platform, the aerospace manufacturer now houses all of its critical data—machine logs, production processes, vendor feeds—in one secure environment. Leadership can finally model, track, and evaluate business objectives with a consistent and accurate view of the organization. This has accelerated both day-to-day decision-making and long-term strategic planning.

Significant cost savings underscore the value of this approach. By avoiding the “stitch-together” model of traditional Azure services, the company cut modern data platform costs by about 80% and reduced implementation time to value by roughly 60%. At the same time, the architecture remains flexible enough to accommodate cutting-edge AI initiatives, from predictive maintenance to in-depth materials science research.

Top Services & Integrations Leveraged

• Microsoft Azure

• Microsoft Fabric (Lakehouse, Warehouse, Fabric Pipelines, Spark Pools)

• Semantic Models

• Power BI

• Microsoft Purview

Project Highlights and Looking Ahead

This project exemplifies how organizations can prepare for the next wave of AI by embracing a unified, end-to-end data solution. The aerospace manufacturer eliminated legacy bottlenecks and achieved a centralized hub for real-time data insights. With a robust governance framework and built-in data science tools, the company has laid the groundwork for ongoing innovation. By leveraging Microsoft Fabric, its teams now have the agility to explore AI-driven efficiency gains, cost optimizations, and even new alloy recipes, confidently knowing that the data foundation will support continued digital transformation.

If you are ready to modernize your data platform and accelerate AI initiatives, Blue Orange Digital can guide you through a customized approach that seamlessly integrates security, governance, and powerful analytics—so your organization can thrive in the age of data-driven decision-making.