Solving for unique data strategy needs

A leading aerospace organization wanted to define an enterprise data strategy to meet the unique analytics and information requirements of the business units and functions within this emerging industry sector.


guiding principles


major initiatives


data platform


A privately funded space exploration, aerospace manufacturing, and suborbital spaceflight services company was growing exponentially, and so was its use of data and analytics—leaving executives and operational teams struggling to identify data assets for analysis needs. Because there was no formal governance process for generating, storing, integrating, consuming, and archiving data, teams were developing their own manual processes, which was causing duplicated efforts and extra labor across business units and putting ongoing commercial success at risk. Cervello and Kearney were brought in to define an overall data strategy and architecture to drive organizational change toward governed self-service capabilities for data.



Cervello and Kearney used a data strategy framework to assess the company’s current state across six areas: data management, architecture and tools, insight generation, operating model, services portfolio, and strategic positioning. We conducted tactical and executive stakeholder workshops and interviews to understand the data challenges and the future needs across all business units and supporting functions. We assessed the capabilities, processes, people, and technology that fed into a maturity view against leading practices, driving our recommendations and the overall initiative road map.


Our enterprise data strategy encompassed data governance, technical architecture, and an operating model to provide the vision and guiding principles that the entire organization could advocate for. We prioritized actions and identified the short-term and long-term initiatives that could help the organization capture rapid and incremental value. We also identified opportunities to provide ready access to quality data, improve operational efficiencies, and meet urgent customer commitments.

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