The digitalisation of materials quality assurance in the automotive and plastics industries
depends on the availability of structured, machine-readable data. However, a substantial
proportion of existing test and product data resides in formats that were never designed for
automated processing: PDF data sheets, proprietary Excel templates, laboratory reports in
unstructured text, and isolated database exports with heterogeneous schemas.
When organisations seek to adopt new data standards—such as VDA 231-301 or
comparable structured data models—they inevitably face the challenge of converting these
legacy data inventories into the target format. In practice, this conversion process is one of
the most resource-intensive steps in any digitalisation initiative.
Typical obstacles include:
Inconsistent document structures across suppliers, projects, and time periods
Ambiguous or incomplete descriptions of testing conditions and parameters
Implicit domain knowledge embedded in naming conventions and abbreviations
Varying granularity of recorded results, from aggregated summaries to raw
measurement values
Absence of unique identifiers linking test results to defined testing requirements
The cumulative effect is a significant bottleneck: organisations cannot leverage their
historical data assets within modern, interoperable systems without first undertaking a
costly and error-prone transformation effort. This delays the realisation of efficiency gains
that standardised data models are designed to deliver.
A widely discussed approach to legacy data conversion involves the use of Artificial
Intelligence and algorithmic methods to automatically map existing data into a target data
model. Techniques such as natural language processing, pattern recognition, and
rule-based extraction engines can identify and classify testing requirements, extract
parameter values, and propose mappings to standardised fields.
These methods offer clear advantages in terms of throughput and scalability. For large
document volumes, automated approaches can process orders of magnitude more data
than manual review. However, purely automated solutions encounter well-documented
limitations in the materials testing domain:
As a result, relying exclusively on AI or algorithmic approaches introduces risks that are
difficult to accept in quality-critical environments. The automotive industry, in particular,
demands traceability, auditability, and correctness—requirements that purely automated
pipelines cannot consistently guarantee without additional safeguards.
Brain of Materials addresses the fundamental tension between automation efficiency and
domain reliability by researching and implementing multiple complementary approaches for
the extraction and standardisation of legacy data. Rather than relying on a single method,
the platform employs a hybrid strategy that combines algorithmic processing with
structured domain validation.
The core methodology integrates:
This hybrid approach—combining algorithmic evaluation with expert-guided quality
assurance—ensures that even extensive, heterogeneous legacy data inventories can be
transformed into a structured, standardised data basis with a high degree of reliability. The
feedback mechanisms simultaneously serve to refine and optimise the underlying
algorithms, improving extraction accuracy over successive iterations.
The ability to reliably convert legacy data into standardised formats unlocks a range of
operational benefits that extend well beyond the immediate conversion task. By
transforming historically accumulated document repositories into a consistent,
interoperable data source, organisations establish the preconditions for meaningful
digitalisation of their entire materials quality assurance landscape.
Concrete applications include:
The conversion of legacy data gains particular significance in the context of industry-wide
standardisation efforts. VDA 231-301 defines a generic, machine-readable data model for
the structured description of testing requirements, testing conditions, result structures, and
references to standards and specifications. However, the value of such a standard is fully
realised only when existing data—not merely newly generated data—can be represented
within it.
Brain of Materials facilitates this integration by mapping extracted legacy data directly into
the VDA 231-301 data model and enriching it with TestIDs. The TestID provides a unique,
machine-readable identifier for each testing requirement, including its methodology,
parameterisation, and conditions. Within the VDA 231-301 framework, the TestID functions
as a BusinessKey that enhances the data model with unambiguous references—bridging
the gap between normative text and operational testing practice.
This combination of standardised data structures and unique identification transforms
legacy data from a static archive into an active, queryable, and automatable resource
within the digital supply chain.
The conversion of existing test and product data into new data standards and data models
is one of the most significant practical challenges in the digitalisation of materials quality
assurance. Purely manual conversion is prohibitively expensive at scale; purely automated
approaches lack the domain-specific reliability required in quality-critical environments.
Brain of Materials addresses this challenge by researching and implementing a hybrid
methodology that combines AI-based extraction, algorithmic mapping, and expert-validated
structuring. This enables:
Organisations that address their legacy data challenge systematically will not only
accelerate their transition to data-driven quality assurance but also unlock the full value of
their historical data assets for future digitalisation initiatives.
Would you like to understand how legacy data conversion can be concretely implemented
in your existing system and process landscape—and what efficiency potentials can be
realised through structured, standardised data exchange?
In our complimentary webinar, we will demonstrate practical applications of how Brain of
Materials can serve as an operational infrastructure for testing and material data. Together,
we will analyse typical integration scenarios, automation potentials, and specific use cases
along the supply chain.
Secure your appointment now and discuss your individual requirements directly with our
experts.