Building a Scalable AI and Data Foundation for a Global Aviation Services Leader

A leading global aviation services company had already made meaningful investments in data and analytics, including the rollout of Databricks as a central platform. But while the organization had taken important steps toward modernization, the foundations needed to turn that investment into enterprise-wide value were not yet in place.

The Databricks environment had been established, but its configuration and access patterns created friction for the teams that needed to use it. Important platform capabilities, including serverless compute, were unavailable because of the way the environment had been set up, and users struggled to access data in ways that supported scalable analytics. As a result, the platform was not yet functioning as a trusted, easy-to-use foundation for reporting, analytics, and future AI use cases.

At the same time, much of the data being brought into the environment remained close to its raw source-system form. Data was fragmented across multiple systems, early-stage data layers limited reuse, and the organization lacked a robust data quality framework to build confidence in analytics outputs. Where data had been modeled, it was not modeled in a way that enabled business users to confidently answer questions through self-service analytics. This created inconsistent reporting, reduced trust in results, and increased reliance on technical experts to interpret and prepare data for decision-making.

The organization also recognized that technology alone would not solve the problem. Ownership was unclear, governance was limited, and the capabilities required to scale data, analytics, machine learning, and AI were not yet fully defined. Heavy reliance on third-party providers made it harder to build internal momentum, while the absence of a clear operating model left leaders without a practical view of how teams should organize, govern, and deliver against strategic priorities.

For senior leadership, the implications were significant. Without stronger data foundations, better modeled and trusted data products, and a clearer operating model, the organization risked slower decision-making, duplicated effort, rising delivery costs, and a longer path to AI adoption. What was needed was not just a technical review, but a practical transformation plan that connected platform modernization, data modeling, governance, and organizational capability to measurable business outcomes.  

Aimpoint Digital approached the four-week engagement as both a strategic and operational assessment. We conducted interviews with 56 stakeholders across business and technology functions to understand the organization’s current pain points, priorities, and long-term ambitions. In parallel, we completed a technical review of the existing data environment, including Databricks platform configuration, pipeline design, feature usage, maturity across core capabilities, and opportunities to align the platform more closely with industry best practices.

From there, Aimpoint translated those findings into a future-state vision designed to help the client move from fragmented reporting and ad hoc analytics toward a centralized, governed, and scalable data ecosystem. We recommended a Databricks-centered architecture built around stronger technical foundations, clearer integration patterns, and a more deliberate medallion-based data model. We also defined a target operating model that clarified ownership across data engineering, business intelligence, governance, and AI, while recommending a hub-and-spoke structure to balance enterprise standards with business-unit agility.

Just as importantly, the engagement produced a phased roadmap that gave leadership a practical path forward. The roadmap prioritized foundational platform fixes, modernization of orchestration and access patterns, a targeted build-out of the medallion architecture aligned to business priorities, rollout of governance and data quality capabilities, and a sequenced plan for delivering analytics and AI use cases that could demonstrate value quickly while building toward long-term scale.