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A Guide to Data Innovation for Development – From idea to proof-of-concept

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The toolkit provides step-by-step guidance for development practitioners to leverage new sources of data. It is a result of a collaboration of UNDP and UN Global Pulse with support from UN Volunteers, led by UNDP innovation teams in Europe and Central Asia and Arab States.
The guide is structured into three sections - (I) Explore the Problem & System, (II) Assemble the Team and (III) Create the Workplan. Each of the sections comprises of a series of tools for completing the steps needed to initiate and design a data innovation project, to engage the right partners and to make sure that adequate privacy and protection mechanisms are applied.

About this resource

Features

Techniques

Country/Territory

United Nations

Date Published

2016

License

Copyrighted-All rights reserved

Formats

PDF publication

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One review for "A Guide to Data Innovation for Development – From idea to proof-of-concept"

  1. One of the biggest struggles of public policymakers is often that of finding hard data to objectively design, monitor and evaluate the social issues they are working on.
    This toolkit will be an extraordinary resource for all practitioners or program designers who need a dataset or are struggling to deal with the data they have collected.
    The toolkit comprehensively illustrates the entire agenda-setting process of the design of a policy-program based on data. Its aim, hence, is not that of providing a series of techniques or solutions. Rather, it must be considered as a single detailed plan of interlinked operations.
    The biggest strength of the toolkit is that it accompanies the user step by step, providing extensive guidelines on how to set up each phase of the research.
    Relying fully on this toolkit without being used to work with dataset and statistics, however, may be difficult. The steps suggested by the UNDP are very clear and provide substantive help on how to lead data analysis and how to use the results collected. Yet, they are not a quantitative methods lesson. In order to use it, knowledge about how to process data and use statistical software is a pre-requisite.
    Nevertheless, if someone confident with quantitative methods is included in the team using the toolkit, its use will be easy and may significantly improve the reliability of the results obtained. Also, the quantity of examples and the fact that every step includes tools to practically implement the techniques described is remarkable. These last features, in fact, significantly increase the applicability of the toolkit, extending its usefulness also to who is less familiar with data analysis.

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