Pioneering Data Agility in Aerospace Manufacturing

A global aerospace manufacturer known for advanced composites was running its financial and operational reporting on a legacy, on-premises data warehouse — a setup that took up to 24 hours to refresh a single financial model. The manufacturer partnered with Redapt to rebuild its data foundation on Microsoft Azure. The result: a governed Analytics Data Hub that refreshes in about an hour, and a foundation ready to support what comes next.

Turning Challenges Into Opportunities

When Legacy Data Slows the Business Down

For a company competing on precision and speed to market, a data platform that takes a full day to refresh financial numbers is a liability, not a back-office detail. Every delayed report was a decision made on stale information, in an industry with little room for guesswork. Across manufacturing, the gap between having data and being able to trust it in the moment is what separates companies that can pilot AI safely from those still debating whether their data is ready to try.

The Problem

The manufacturer's finance, operations, and sales data lived in an on-premises Dynamics AX system, moved through SQL Server Integration Services into a traditional data warehouse. Building or refreshing a financial model took 12 to 24 hours — a manual, error-prone process that left leadership working from information that was already a day old by the time it reached them, and made governed, safe experimentation with generative AI difficult to even scope.

The Solution

Redapt worked alongside the manufacturer's team to design and stand up a governed Analytics Data Hub on Microsoft Azure — a Bronze/Silver/Gold medallion architecture backed by a reusable, parameterized ELT framework and a secure landing zone. Redapt then built an Azure DevOps CI/CD pipeline across development, test, and production environments, replacing manual releases with an auditable, repeatable deployment process, and piloted a scoped Azure OpenAI proof-of-concept to test governed, natural-language access to the curated data.

The Outcome

Data refresh time dropped from a 12-to-24-hour batch cycle to just over an hour end to end, turning a day-old report into a same-day decision. Deployments across environments became repeatable and auditable instead of manual. And with a working generative AI proof-of-concept already piloted against governed data, leadership has a concrete answer about what its data foundation can support next.

SOLUTION DEEP DIVE

Building a Governed Foundation for AI-Ready Data

Redapt rebuilt the manufacturer's data foundation from the ground up, replacing a slow, manual warehouse with a governed, automated Azure platform designed for what comes next.

Assessing the Legacy Foundation

Evaluated the existing on-premises SSIS-based ETL and data warehouse architecture from end to end.
Mapped current-state data flows against the target Azure Synapse design before any migration began.

Building the Medallion Data Hub

Designed and deployed a Bronze/Silver/Gold medallion architecture in Azure Synapse and Azure Data Lake Storage.
Built a reusable, parameterized ELT framework with centralized control tables governing every data load.

Migrating and Consolidating Data

Migrated legacy on-premises data into the new Azure Data Lake alongside data from the company's modernized ERP environment.
Consolidated finance, operations, and sales data into a single governed source instead of scattered extracts.

Hardening the Delivery Pipeline

Stood up Azure DevOps repositories and release pipelines, replacing manual deployments with automated, auditable ones.
Added audit logging across the framework so every data load can be traced, timed, and verified.

Enabling What's Next

Piloted a scoped Azure OpenAI proof-of-concept for natural-language Q&A against curated, governed data.
Delivered documentation and hands-on knowledge transfer so the manufacturer's team can extend the platform independently.

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THE OUTCOMES

Faster Data, Fewer Manual Steps

The rebuilt platform turned a day-long reporting cycle into a same-day one — and gave leadership a governed foundation to build on.

Reporting Speed
Data hub refresh time cut from a 12-to-24-hour cycle to just over an hour.

Deployment Reliability
Manual releases replaced with an automated, auditable CI/CD pipeline across development, test, and production.

AI Readiness
A piloted Azure OpenAI proof-of-concept showed what governed, natural-language access to the data could do.

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CONCLUSION

A Platform Built for What's Next

What started as a fix for a slow, manual reporting cycle became a governed data foundation the manufacturer can build on — including a first, working look at generative AI over its own data. If your team is already fielding questions about what AI could do for the business, and you're not confident about the data underneath, it is trustworthy enough to answer that safely. That's exactly the gap this closes.

Request a Data Readiness Workshop to find out what your data can support today. AI only performs as well as the data it's built on, and the fastest path to a reliable answer starts with getting that foundation right first.

PRIVACY / CONFIDENTIALITY STATEMENT

Your trust and confidentiality are our top priorities. Redapt prioritizes and protects our customers' privacy, so we don't publicly disclose account names or other identifiable information. We are eager to share our successes and are happy to provide specific referrals or case studies upon request.

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