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  1. Discussion Paper: Innovations in Monitoring and Evaluation

    Discussion paper

    This discussion paper produced by the United Nations Development Programme discusses various innovations that are occurring in M&E, and the advantages and disadvantages of these methods.

  2. Big Data for Development: Challenges & Opportunities

    Discussion paper

    This white paper by UN Global Pulse examines the use of Big Data in development contexts. Using a number of examples, it highlights how this data type can be leveraged to provide early warnings of disruptions and crises, and can give real-time awareness and feedback of situations and interventions. It also delves into a conversation about the implications of Big Data use.

  3. Global Innovations in Measurement and Evaluation


    This report by NPC highlights their research into the latest developments in theory and practice in measurement and evaluation. The authors found that new thinking, techniques, and technology are influencing and improving practice. This report highlights eight developments that the authors think have the greatest potential to improve evaluation and programme design, and the careful collection and use of data. 

  4. Gender equality and big data: Making gender data visible

    Discussion paper

    The report from UN Women, with support from UN Global Pulse, outlines the value of big data for monitoring the Sustainable Development Goals (SDGs) in relation to women. It presents the benefits of big data (for example, real time data), risks (for example, elite capture and privacy), and policy implications (for example, how it can be incorporated in project cycles from planning to evaluation). It ends with a compendium of gender-related big data projects and their relevance to the SDGs.

  5. CEDIL Methods Brief- Using big data for impact evaluations


    This paper provides detailed guidance on using big data to fill data gaps in impact evaluations. Data gaps can arise due to the inaccessibility of target populations, inadequate aggregation of data, data collection lag times, and data being missing in some contexts, like pandemics, conflicts, and humanitarian emergency situations. The paper includes a number of specific examples and additional references.

    One issue to note when using the paper is that it uses the terms 'control group' and 'comparison group' inconsistently. The examples provided mostly refer to the use of comparison groups and quasi-experimental designs, not to randomly assigned control groups

  6. Measuring results and impact in the age of big data

    Discussion paper

    This paper explores the nexus of data science and evaluation, probing the issues and challenges of incorporating big data into evaluation practice.