Learnings from the multidimensional digital inclusiveness index: implications of data quality on evaluations of digital inclusiveness

Nisansa, Vinuri; Barzan, Amna; de Alwis, Osal; Martins, Carolina Iglesias; Garcia Andarcia, Mariangel

Learnings from the multidimensional digital inclusiveness index: implications of data quality on evaluations of digital inclusiveness

Autores

Nisansa, Vinuri

Barzan, Amna

de Alwis, Osal

Martins, Carolina Iglesias

Garcia Andarcia, Mariangel

Editor

International Water Management Institute

Descripción

High quality data is essential for evaluating whether digital tools in agrifood systems are inclusive, equitable, and usable for diverse user groups. This brief synthesizes insights from the Multidimensional Digital Inclusiveness Index (MDII), a scientific framework that assesses digital agri-system tools across seven dimensions, including accessibility, usage efficacy, beneficial impact, risks and harms, supportive ecosystem, ethical and responsible innovation, and co-creation and governance. Through pilot applications of MDII to eight FAO WaPOR based tools across Ethiopia, Jordan, Mozambique, Pakistan, and Tunisia, the study shows that data quality strongly influences the reliability and interpretability of digital inclusiveness assessments. Findings reveal that missing information, uneven participation among user groups, inconsistent records, and misaligned data structures reduce clarity and limit meaningful comparison across tools and contexts. These constraints usually arise from institutional processes, limited connectivity, distributed responsibilities, or inconsistent documentation practices rather than shortcomings in the tools themselves. Such limitations travel through the MDII scoring pipeline and create uncertainty in results, making it difficult to identify genuine usability issues or inclusion gaps. The report demonstrates that structured data quality practices, supported by FAIR and ALCOA+ principles, strengthen the evidence base by improving provenance, consistency, and traceability. Embedded validation checks, harmonized artefacts, and iterative improvement cycles enhance scoring stability and support more credible diagnostics. Pilot studies further highlight recurring challenges such as respondent fatigue, gaps in understanding MDII concepts, and technology-related barriers, along with practical solutions to improve data completeness and reliability.