Our client is one of the world’s leading aerospace manufacturers, producing engines and auxiliary power units for iconic civil and military aircraft.
Today, the company’s engines aren’t just impressive examples of physical engineering — they also produce vast quantities of performance and operational data. As engines progress through their lifecycle, that data becomes increasingly crucial to their maintenance and safe operation.
Recently, our client began a major initiative to reduce the cost of maintaining one of its mature engine lines — the F-135. The organization had access to huge quantities of valuable engine performance data, but manual processes and disconnected systems made that insight hard to surface and act on at the right time, driving costs upward.
To help its maintenance teams make the most of all the data available to them and enable proactive, data-driven maintenance for all of its F-135 engines, the global aerospace manufacturer reached out to Thoughtworks for some data engineering support.
Turning one use case into organization-wide transformation
Our client understood the impact that adopting emerging best practices for data management and analytics could have on the efficiency and cost of engine maintenance. So, we moved straight into helping the company implement a data mesh solution.
Data meshes provide a robust solution for organizations with intensive, domain-specific data use cases. In their case, engine maintenance was the ideal initial use case for a data mesh transformation — providing a great example of the wider impact that use-case-driven development could have on the organization.
We started by building a self-service data platform to facilitate the operation of the data mesh. With the foundation established, we began developing data products, establishing new federated data governance practices and upskilling their team.
The power of iterative, consultative change
Across the project, we applied a “show/shift/scale” model to gradually introduce data mesh principles and practices and build support for the data mesh without overwhelming domain teams.
Implementing a data mesh isn’t just a technological shift for most organizations — it also requires significant culture and process change. At this organization we helped evolve the team’s mindset from a waterfall approach to embrace agile methodologies, and upskilled multiple teams to fulfill their new roles and responsibilities as data mesh custodians.
We prioritized use-case-driven development to deliver tangible value quickly and iteratively improve the platform and processes over time. This approach helped everyone clearly see the value the data mesh could deliver for them, building support and driving adoption across the company’s diverse domains.
On the technical side, one of the major challenges of this project was migrating from an AWS-based platform to an Azure-based platform mid-project. Our client had an existing relationship with and preference for Microsoft Azure, so our teams migrated the new system to the Azure platform and leveraged its scalability and flexibility to ensure a smooth data mesh implementation.
“While a mid-project migration was challenging, it ended up as a big net positive for us,” said the Thoughtworks Lead. “By building core data mesh elements in the Microsoft Azure cloud, we ensured easy integration with the company’s existing estate and seamless scalability as its needs evolve.”
Cutting costs, data discrepancies and response times
With the data mesh in place, our client’s domain teams have been able to build multiple new data products, powering use cases in maintenance and beyond. So far, its teams have successfully:
Reduced time spent on parts forecasting by 2,200 hours annually
Reduced time spent on developing a key cost report (CDRL) by 80%
Enabled proactive identification of data discrepancies between ERP systems
Improved data availability and accessibility across the F-135 program
Increased the frequency of data product releases, supporting faster decision-making
For the organization’s customers, the improved efficiency and cost-effectiveness of maintenance has helped them significantly cut operational costs while increasing the readiness and performance of their aircraft.
The data mesh has generated significant value for the organization. But in many ways, this is just the beginning of a new chapter in the company’s data-driven future.
With our new data mesh foundation in place, we’re now planning to expand our implementation and start building in some new leading capabilities. Right now, our focus is on incorporating AI and machine learning to the mesh and scaling out the data mesh to support other key programs within our organization.
