Data Collection

Publié: 17 janvier 2022
Dernière modification: 17 janvier 2022

Several types of data were collected throughout the process of this project. In the Horizon Scanning phase, socio-economic-environmental secondary data using SDGs as a framework were collected along with data from social media trends. This phase was mostly done by SDG Move. These data were processed and challenging social, economic and environmental issues were selected and made each of them into a 1-page report. Each region contextualized the 1-page reports and selected or added regional specific issues. In the Delphi method phase (regional level), expert opinions (assessing and commenting on the 1-page report in the first round and prioritizing the challenging issues in the second round) were collected and processed to identify regional priorities. The priorities were then utilized in the regional foresight workshops, in which Backcasting method was used in a multi-stakeholder consultation for obtaining the regional aspirations involving the regional priorities and strategic directions to achieve those aspirations. Outcomes of all regional processes were synthesized. A list of more than 10,000 research projects was analyzed against SDGs and the synthesized regional outcomes to obtain research gaps.

Classifications

Category
Partenariat
Scale of implementation
National

Enabling factors

The expertise of the regional teams made the contextualization of challenging issues possible with little effort. Their social capital, with personal connections to stakeholders from several sectors in the region, helped identify the stakeholders with experienced and engagement with the existing movements, enabling us to obtain grounded and current perspectives of these challenging issues. 

A well-planned data collection process and regular and open consultation between SDG Move and the regional teams were also crucial for in-time data collection.

 

Lessons learned

Academic and Civil Society Organization experts are the second-based source to fill the data gap. This is possible because the data are not used for complex statistical methods but to understand the situation of the challenging issues. So the quantitative data was only one piece of the puzzle. 

Clear objectives, timeline, and deliverables for each regional team help their planning. The timeline should account for delays and unexpected regional or local limitations. Regular check-in was important to update the project status and obstacles. The earlier the obstacles are identified, the better. 

SDG Move as a coordination team has to be open-minded and listen to the voice and concerns of the regional teams since our plan is not perfect and might not fit regional and cultural contexts. The morale of the regional teams also needs to be observed and boosted when needed. The project progress and prospects, and a compliment from the TSRI office were good morale boosts.

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