To thoroughly monitor and analyse progress on Sustainable Development Goals (SDGs), policy makers need data and statistics that are timely, detailed, disaggregated, and accessible. Institutions and statistical offices are working intensively on collecting and analysing proper data for this sake 1 2 , and as a result, data quality has gradually improved over time. Despite all the efforts, there are still data limitations and challenges to be overcome. For example, some statistical SDG indicators are not internationally comparable, not timely enough, or incomplete 3 .
Cooperation and data sharing between statistical bodies and private, or public organisations can help overcome some of these limitations. If statistical bodies complement their traditional data collection with modern data sources, this can result in more timely and accurate statistics. What can we learn from current data sharing initiatives and what are their implications and opportunities for monitoring SDGs?
Let’s look at an example in the agricultural sector. The agricultural sector is relevant for several SDGs, like SDG 2: “Zero hunger”, SDG 6: “Clean water and sanitation” or SDG 12: “Responsible consumption and production” 4 . Agricultural food production, waste, and the use of chemicals are currently used as the statistical indicators to monitor the achievement of the SDGs 5 .
Data sharing can also be used to increase the accuracy of the farming indicators or make the statistical process less labour-intensive. The practice example “Data sharing in the agricultural sector” highlights this by showcasing JoinData, a non-profit organisation that aims to make data sharing in the agricultural sector more efficient and transparent. Smart sensor data is used to examine e.g. ground conditions and pest developments, which offers opportunities for farm businesses and has the potential to enable sustainable innovation. Aside these promising opportunities, I think that sharing sensor data can contribute to the development or improvement of agricultural statistical indicators as shown earlier in this article. Currently, surveys are used as a source for agricultural statistical indicators. The (partial) replacement of survey data by sensor data would have advantages for both the accuracy of the indicators and the efficiency of compiling them. Some statistical offices are already experimenting with farming sensor data 6 , but a full network of data sharing farmers is not yet in place. Why not involve statistical offices in initiatives like JoinData and work together to get the most out of the data available and the network that has already been built?
The example of the agricultural sector shows that there are opportunities to improve the monitoring of the SDGs by sharing data. Of course, data sharing between institutions and companies is not straightforward, as it involves legal and technical challenges. Moreover, international comparability of statistical indicators is important to guarantee the quality of indicators, therefore extending initiatives like JoinData internationally would be beneficial. Only by close international cooperation, reliable and comparable indicators can be developed. Although sharing data is challenging, the practice holds great potential for monitoring progress towards the Sustainable Development Goals.