Data Foundations for Aerospace: From 7 Disconnected Systems to Predictive Quality
A Tier 2 aerospace structures and precision components manufacturer with $85M in revenue and 340 employees had data trapped in 7 disconnected systems — Siemens Teamcenter, Epicor ERP, Zeiss CMM quality inspection, Hexagon MES, Access databases, Excel scorecards, and scanned paper travelers. They'd already spent $180K on an AI vendor who delivered a proof of concept that never made it to production. We built the data platform that got their first predictive quality model into production within 60 days of go-live.
Data Quality (was 68%)
To First AI Model
Scrap Reduction
Foundation to Production
The Challenge
The VP of Operations wanted predictive models for CNC machining quality — the business case was strong with $1.8M in annual scrap. But they'd already failed once. An AI vendor spent 8 months and $180K delivering a POC that worked on curated data but couldn't survive production conditions. The post-mortem blamed "data readiness issues." The real problems: data in 7 systems with no integration, 68% critical field accuracy, no data dictionary (the word "defect" had 4 different definitions across departments), ITAR-controlled data commingled with general business data, and 1.5 FTEs doing data work part-time. Every report required manual reconciliation. Nobody trusted the numbers. They'd been told by two vendors their data wasn't ready — but nobody offered to actually fix it.
The Solution
We ran a 3-week assessment interviewing 14 people across operations, quality, engineering, and finance. Then we built an Azure data platform on Microsoft Fabric with 23 automated pipelines connecting all 7 source systems, a 1,200-term data dictionary with resolved ownership, ITAR/CUI data classification with role-based access controls, 340+ automated data quality checks, and AS9100-integrated governance. We migrated 12 TB of historical data, decommissioned 3 legacy databases, built 14 trusted Power BI reports, and trained 6 team members to operate the platform independently. The client owns 100% of the IP.
How We Did It
Assessment & Architecture (3 Weeks)
Three-week deep dive across the data landscape — interviewed 14 people from CNC operators to the CFO, inventoried every system, mapped every data flow. Designed target architecture on Microsoft Fabric with Azure Government Cloud for ITAR workloads. Created a 1,200-term data dictionary, resolving 47 conflicting definitions across departments. Built 340+ automated data quality rules.
Platform Build (6 Weeks)
Deployed Microsoft Fabric OneLake with medallion architecture. Built 23 data pipelines via Azure Data Factory — API integration for Teamcenter, Epicor, and MES; CDC for CMM quality data; OCR extraction for scanned paper travelers; automated import for Excel scorecards. Implemented ITAR/CUI role-based access controls and real-time data quality monitoring with 15-minute alerting.
Migration & Integration (5 Weeks)
Migrated 12 TB of historical data — 8 years of quality inspection records, production logs, and ERP transactions. Validated every dataset against source systems with zero-tolerance reconciliation. Integrated near-real-time feeds from MES and CMM systems on 5-minute refresh cycles. Decommissioned 3 legacy reporting databases. Built 14 production Power BI dashboards.
Handoff & Enablement (2 Weeks)
Full documentation: architecture diagrams, runbooks, troubleshooting guides. 24 hours of hands-on training — not lectures, working sessions building real reports and investigating real data quality issues. 30-day hypercare period. Defined the AI/ML roadmap they originally came to us for, now with a platform that could actually support it.
"We spent $180K learning that our data wasn't ready. Then Ryshe spent 16 weeks actually making it ready. The predictive quality model everyone kept telling us was impossible with our data was in production two months later. I wish we'd started here."— VP of Operations
What We Learned
The Foundation Is the Project
The predictive quality model took 6 weeks to build. The data foundation took 16 weeks. That ratio isn't a failure of planning — it's the reality of enterprise data work in regulated industries. The foundation is where the value is created. The AI model just harvests it.
Governance Isn't Optional in Aerospace
Without agreed definitions, ownership, and classification, every downstream project becomes a debate. In an ITAR environment, that debate has compliance implications. We resolved 47 conflicting term definitions in Phase 1. Every one of those would have been a project-stalling argument later.
Compliance and Data Architecture Are the Same Project
Separating "build the data platform" from "fix the compliance gaps" would have doubled the timeline and cost. CUI classification, access controls, and audit trails aren't add-ons — they're architectural requirements that shape every design decision.
Built With
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