Data

NQCP and Quantum Foundry Copenhagen are joining forces to establish a best-in-class Data Team to build a scalable platform that encompasses data from all the other teams and enables data exploration and analysis across the whole programme.

The ambitious goal to build a unified data platform plays a central role in the entire program. Change management is the primary tool to establish data acquisition standards, centralize data storage, and develop data analytics capabilities at a programme level through the implementation of a robust data platform.

The Team

The Data Team consists of data experts with academic and industry experience, which benefits best data practices from both fields. The team is led by Head of Data, Juste Stumbryte and is currently in the process of shaping and growing. The team culture places a significant emphasis on project-centric organization and agile project management. This approach aims to efficiently support other parts of the program ensuring data structure requirements and optimal use of the data platform services.

Our focus

Our focus encompasses several key pillars to ensure integrity, security, and availability, thereby promoting a data-driven research approach. These include:

Data governance: we are establishing dedicated databases for different data classes, ensuring standardized data collection across teams. This improves consistency and supports uniform data management across the program.

Data security: to protect our data assets and prevent unauthorized access, we implement strict access controls and security protocols, ensuring only authorized personnel can handle sensitive data.

Data platform: our data strategy includes developing a robust platform that integrates these established databases, enabling comprehensive analysis and supporting informed decision-making across the program.

Data collection process automation: we are streamlining and automating data collection processes to enhance efficiency, standardize methods, and ensure the production of high-quality datasets.

Data quality: implementing rigorous quality controls at key integration points guarantees that our analyses are based on reliable, accurate, and trustworthy data.

Software coordination and governance: by coordinating efforts across teams and setting guidelines for best practices in code development (e.g. Python Cafe), we ensure effective knowledge management and collaboration within the program.