Making Data Governance an ongoing activity: a case study from Nepal

Data governance (DG) plays a vital role in the research process, enhancing accountability, data quality, and risk management to ensure sustainable research outcomes, especially in resource-limited settings like low- and middle-income countries (LMICs). However, DG is often seen as a one-time planning task rather than an ongoing process, and its effectiveness is rarely considered. This paper reviews how DG was developed for a large research programme (SafeTrip Nepal), focusing on how it was developed into an ongoing review, and how we set about formally evaluating it.

SafeTrip (2022-2026) is funded by the UK National Institute for Health and Care Research to improve road safety in Nepal. The funding explicitly included the development of DG capability in the Nepalese partners.

DG initially focused around in-person training and developing DG plans for each of the four workstreams within SafeTrip. Following the initial planning stage, the DG team began to explore how DG could be embedded into operations more actively. Accordingly, the team introduced a schedule of ongoing light-touch review, spread across four key stages: planning, data collection, analysis, and post-project activities. This proved effective at highlighting inconsistencies between plans and outcomes, early enough to implement mitigation measures.

In Autumn 2024 the team evaluated the effectiveness of DG more formally. A survey and one-on-one interviews were conducted across the SafeTrip team. A mixed-methods approach was employed to assess the plan’s impact on risk management, ethical compliance, and overall project support.

Overall, the DG plans effectively mitigated risks, ensured ethical compliance, and maintained data security. The "Five Safes" framework enhanced accountability, data quality, participant trust; flow diagrams facilitated data management. Challenges, such as the initial technical issues with data collection tools and the need for software adaptation, were resolved through flexible platform adjustments and collaborative problem solving. Respondents highlighted the need for regular training.