Case Study: Successful Data Modeling Implementation in Aerospace Engineering with Directus

Aerospace engineering sits at the frontier of precision, safety, and continuous innovation. As aircraft and spacecraft become more software-defined and data-driven, the need for robust, scalable data management has never been more pressing. This case study examines how a leading aerospace manufacturer transformed its engineering workflows by implementing a comprehensive data modeling strategy using Directus, an open-source headless CMS and data platform. The result: a unified data architecture that reduced redundancy, improved traceability, and accelerated product development cycles.

The State of Data in Aerospace Engineering Before the Transformation

Modern aerospace design involves thousands of components, millions of simulated test runs, and hundreds of engineers working across disciplines. The company in focus, a mid-tier aerospace supplier responsible for critical flight control systems, faced a fragmented data ecosystem. Their legacy systems included:

  • A mix of on-premise SQL databases for part metadata
  • Spreadsheets for design change logs
  • Proprietary file formats from CAD and CAE tools
  • Paper-based sign-off records for certification

This fragmentation led to several operational pain points. Engineers often spent hours cross-referencing data across silos to verify part numbers or revision histories. Inconsistent naming conventions caused errors in bill-of-materials (BOM) exports. Regulatory audits required manual data collection that could take weeks. Perhaps most critically, the lack of a unified data model made it difficult to trace a design change from concept through simulation, testing, and production.

Setting Clear Objectives for the Data Modeling Initiative

The executive team chartered a six-month initiative with the following strategic goals:

  • Standardize data formats across mechanical, electrical, and software engineering teams to eliminate reconciliation work.
  • Create a scalable data model that could accommodate new product lines and evolving certification standards without requiring a full re-architecture.
  • Enable full traceability of design modifications—capturing who changed what, when, and why, in a manner compliant with AS9100 and DO-178C.
  • Integrate seamlessly with existing simulation and testing tools (ANSYS, Siemens NX, MATLAB/Simulink) to allow real-time access to master data.
  • Reduce time-to-insight for analytics by providing a clean, queryable dataset ready for dashboards and machine learning models.

The chosen platform needed to be flexible enough to model complex relationships (e.g., a part belongs to an assembly, which belongs to a system, which is validated by specific tests) while offering an intuitive interface for non-technical stakeholders. After evaluating several options, the team selected Directus for its self-hosted, open-source architecture, its ability to generate a REST and GraphQL API automatically from custom schemas, and its granular role-based access control (RBAC).

Designing the Aerospace-Specific Data Model

From Entity-Relationship Diagrams to a Normalized Schema

The implementation began with a thorough analysis of existing data structures. Data architects held workshops with lead engineers from each discipline to map out every entity involved in the lifecycle of a flight control actuator—from raw material lot numbers through final test results. They used Entity-Relationship Diagrams (ERDs) to identify redundancies, anomalies, and missing relationships.

The core data model was designed around five principal collections:

  • Parts – each physical or logical component with attributes like part number, revision, material spec, and weight.
  • Assemblies – groupings of parts, with hierarchical decomposition and quantity breakdowns.
  • Design Changes – a complete log of change requests, approvals, and implementation records (aligned with engineering change management).
  • Tests – links to simulation runs, physical test protocols, and pass/fail outcomes with environmental conditions.
  • Certifications – regulatory compliance documents linked to specific part revisions or assemblies.

Relationships were normalized to avoid duplication. For example, a single material specification (e.g., "AMS 5643") was stored in its own collection and referenced by many parts, rather than being repeated as a text field. This normalization reduced data entry errors and made updates consistent across the organization.

Leveraging Directus for Schema Customization and API Generation

Directus provided the team with a visual interface to build this model, allowing non-developers to add fields, set validation rules, and define relationships (one-to-many, many-to-many) without writing SQL. The platform’s built-in field types—including JSON, WYSIWYG, file uploads, and many-to-many junction tables—covered 90% of the aerospace requirements out of the box. For the remaining needs (e.g., encrypted storage of proprietary material data), Directus’s extension system allowed custom field types using JavaScript.

Once the schema was set, Directus automatically generated a RESTful API with full CRUD (Create, Read, Update, Delete) endpoints. This API became the backbone for integration with existing CAD, CAE, and PLM tools. Engineers could now pull the latest part revision directly into their simulation environment via a simple HTTP call, eliminating the old process of manually exporting CSV files.

Role-Based Access Control for Sensitive Engineering Data

Aerospace companies must protect intellectual property while enabling collaboration. Directus’s RBAC was configured with four permission tiers:

  • Read-only viewers – production floor staff and external auditors who need to view part information but never modify it.
  • Editors – engineers who can update technical attributes but cannot delete records or approve changes.
  • Approvers – senior engineers and certification managers who can lock revisions and sign off on change requests.
  • Administrators – a small team managing the schema and system configuration.

This granularity ensured compliance with the principle of least privilege while still promoting efficient data sharing.

Implementation Process: Phased Rollout with Continuous Feedback

Phase 1 – Data Migration and Cleansing

The first month focused on extracting data from legacy systems and cleansing it. Duplicate part records were merged, inconsistent unit formats (pounds vs. kilograms) were standardized, and orphan records (parts linked to no assembly) were flagged for review. The data team wrote Python scripts that used the Directus API to batch upload cleansed records, including audit timestamps and source system identifiers for accountability.

Phase 2 – Pilot with the Actuator Design Team

Rather than a big-bang deployment, the company chose a pilot group of 12 engineers working on a single actuator product line. This group received one week of hands-on training covering Directus’s interface, the new data model, and best practices for entering change logs. During the four-week pilot, the team identified several usability improvements:

  • The need for a dashboard showing pending design changes and their approval status
  • A request to add inline image previews for part drawings
  • The ability to bulk-import test results from simulation logs

Directus’s custom dashboard extension and file preview features addressed these quickly. The feedback loop was short because administrators could modify the schema or add custom pages directly in the admin app without waiting for a software release.

Phase 3 – Integration with CAD/CAE Tools

The most technically challenging phase involved integrating the Directus API with Siemens NX (CAD) and ANSYS (CAE). Using Directus’s webhooks, every time an engineer updated a part revision in Directus, a webhook triggered an automation that pushed the latest BOM to a dedicated shared drive and notified relevant simulation teams via Slack. Similarly, simulation results from ANSYS were posted back to Directus through a small middleware script written in Node.js, creating a bidirectional link between design and analysis.

The integration also extended to the company’s legacy PLM system, which was kept as a read-only archive for historical data. Directus’s OAuth 2.0 authentication allowed single sign-on (SSO) so engineers could seamlessly switch between systems.

Phase 4 – Full Deployment and Training

After the pilot validated the approach, the rollout expanded to 400 engineers across three departments. The training program was tiered:

  • Two half-day workshops for all users covering basic navigation and data entry
  • Four advanced sessions for data stewards covering schema maintenance and API usage
  • One-on-one coaching for teams with specialized workflows (e.g., test engineering)

A dedicated internal wiki was created with video tutorials and frequently asked questions. The company also established a monthly "data office hours" where engineers could raise issues directly with the data team.

Measurable Results and Business Impact

Operational Efficiency Gains

Six months after full deployment, the company conducted a retrospective. The quantitative results were compelling:

  • 33% reduction in time spent searching for design information (from an average of 45 minutes/day to 30 minutes/day per engineer).
  • 70% drop in data entry errors in the BOM due to automated validation and controlled drop-down lists in Directus.
  • 50% faster audit preparation – the compliance team could now generate a full traceability report for any part within hours instead of days.
  • 20% reduction in engineering change cycle time, because approvals no longer required manual routing of paper forms.

Improved Collaboration and Innovation

Beyond the metrics, the cultural shift was notable. Engineers from different disciplines began referencing the same "single source of truth" for part data. The systems engineering team used Directus’s activity logs to analyze the impact of design changes more accurately. For example, they discovered that a particular fastener type was being over-specified in low-stress areas, leading to unnecessary weight. By querying the data model across assemblies, they identified 15 similar instances and proposed a lighter alternative, ultimately reducing overall system weight by 2%.

The advanced analytics team also started running machine learning models on Directus’s exported data, predicting potential failure modes based on historical test results and material batch data. These insights fed directly into the preliminary design phase of new products.

Lessons Learned and Best Practices

Start with a Clear Governance Model

The company learned that a data model is only as good as the rules that enforce it. Early in the project, some teams tried to bypass standard fields by adding comments in free-text areas. A governance board quickly issued guidelines requiring all critical attributes to be captured in structured fields, with free-text only allowed for operational notes. The data team also created automated alerts when users deviated from agreed naming conventions.

Invest in Change Management

Resistance to new tools is inevitable. The most effective strategy was to identify "data champions" within each team—engineers who understood both the domain and the value of clean data. These champions helped peers migrate their spreadsheets to Directus and demonstrated efficiency gains in their daily work. Peer-to-peer training proved far more effective than top-down mandates.

Build Incrementally, but Plan for Scale

The phased approach allowed the team to correct course early. However, they also designed the schema from day one to accommodate future expansion. For instance, the "Part" collection included a generic "attributes" JSON field for product-line-specific properties that didn't fit the core schema. This prevented the need to add new columns every time a new product family was introduced.

Future Directions: AI, Digital Twins, and Beyond

Integrating with Digital Twin Platforms

The company is now exploring how to extend the Directus-powered data model into a full digital twin environment. By connecting the data model to IoT streams from flight tests and in-service aircraft, they plan to create a living representation of each product that updates with real-time performance data. Directus’s real-time subscriptions via WebSockets make this technically feasible.

AI-Assisted Design Validation

With a clean, relational dataset in Directus, machine learning models can be trained to flag design inconsistencies or compliance gaps before a change request reaches the approval board. The data team is piloting a simple anomaly detection model that scans new part entries for combinations of attributes that have historically led to test failures (e.g., high-strength alloy with a thin wall thickness).

Expanding to Supplier Data

Currently, the data model covers only in-house designs. The next phase includes onboarding critical suppliers to a shared Directus project. Suppliers will have a restricted view of their own parts, and the company can automatically validate incoming material certifications against their standards. This extension is expected to reduce procurement rework by 15% within the first year.

Conclusion: Data Modeling Delivers Competitive Advantage in Aerospace

This case study demonstrates that even in a highly regulated, complex industry like aerospace, an open-source platform like Directus can provide the flexibility, security, and performance needed for a successful data modeling initiative. By normalizing data, enforcing governance, and enabling seamless integration with engineering tools, the company not only reduced costs and errors but also unlocked new capabilities in analytics and predictive design.

For aerospace organizations looking to modernize their data management, the key takeaways are straightforward: invest in a flexible schema, involve users early, and choose a platform that puts data owners in control. When implemented correctly, a well-modeled dataset becomes a strategic asset—one that powers everything from day-to-day decision-making to breakthrough innovation.

For more information on building scalable data models with Directus, visit the Directus documentation and explore their community case studies.