As part of NRC’s broader digital transformation agenda, the Programme Department is undertaking a strategic initiative to structure and digitize its field-level implementation processes and data flows. Across Country Offices, programme teams rely on a variety of locally developed forms and workflows to manage activities, resulting in fragmented data, inconsistent formats, and limited interoperability. This lack of harmonization hinders NRC’s ability to analyze operational data globally, ensure GDPR-compliant information management, and make informed, evidence-based programmatic decisions. The IM Standardization consultancy was launched to bridge this gap between global data frameworks and country-level practices.
Building on global standardization work already initiated at Head Office—where central data objects and taxonomies were defined—this consultancy started with the collection of tools currently in use across NRC’s country operations to identify recurring workflow patterns for pre-identified activity types, extract the underlying data points, and compare them with NRC’s global data standards. As such, the process shall contribute to the validation and – possibly the expansion – of NRC’s data standards and provide the foundation for a unified library of standardized tools for data collection during service delivery.
Relief Applications pursues a hybrid approach, combining manual and AI-assisted tools and processes. A Python-based workflow was developed to systematically scan, classify, and analyze hundreds of data collection tools shared by Country Offices. AI-assisted text extraction and pattern recognition techniques are used to identify common data points, recurring form structures, and workflow phases across documents. This hybrid approach—integrating automation with expert validation—ensures both analytical rigour and efficient use of resources. Moreover, it provides a replicable and scalable framework that can be re-applied to future rounds of standardization or extended to other thematic areas and organizational units.
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