Decades of data, finally readable
- 40 Years of engineering records unlocked
- Zero Data movement
- 100% On premises
Decades of knowledge, now an active asset
A global aerospace and defense organization had 40 years of engineering records stored inside a single Teradata environment—circuit design blueprints, maintenance imagery, and technical documentation spanning four decades. The data was well governed and intact. But almost none of it was usable.
Scanned documents, PDFs, and images held serial numbers, part identifiers, and manufacturing dates that the platform couldn’t read. Critical detail for failure analysis and predictive maintenance sat buried, accessible only to human analysts working through a slow, inconsistent manual process. As the data estate grew, so did the gap between what the organization held and what it could act on.
Unstructured data at a scale no manual process could match
The conventional fix—extracting files, running optical character recognition (OCR) externally, and piping results back in—carried trade-offs the organization couldn’t accept. Data movement, external service dependencies, a separate pipeline to maintain, and a wider security perimeter are manageable risks in many industries. In defense, they’re not.
The organization needed a way to extract structured insight from decades of unstructured content without moving data outside its secure environment, without building a parallel processing infrastructure, and at a scale that made a serial, analyst-driven approach simply untenable.
In-database OCR built natively on Teradata
Teradata applied its Bring Your Own Analytics capability to build an in-database OCR pipeline that runs entirely inside Teradata—on-premises, with no external services and no data movement. The pipeline combines Java-based User Defined Functions with Python-driven OCR processing, executed natively within the platform.
Because it runs on Teradata, the pipeline exploits the platform’s massively parallel processing architecture. Rather than processing one document at a time, the full 40-year archive is processed in parallel. A workload that would take months running serially against an external service is completed at a fraction of the time—making what was previously intractable, tractable.
40 years of archive turned into operational intelligence
With a single SQL query or Python call, the organization can now extract structured information—serial numbers, part identifiers, and manufacturing dates—from 40 years of unstructured content, automatically and at scale. Data that once required a human analyst to surface is available in seconds, feeding live predictive maintenance models, engineering decisions, and operational intelligence across the business.
The architecture is also extensible: the same in-database approach applies to any unstructured content, meaning the foundation built for this use case is already in place for whatever comes next.
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