Data & Analytics

Data and analytics are increasingly recognized as fundamental elements in achieving the Sustainable Development Goals (SDGs). These 17 goals, adopted by the United Nations in 2015, aim to address global challenges such as poverty, inequality, climate change, environmental degradation, peace, and justice. Each goal is interconnected, requiring a holistic approach to achieve sustainable development by 2030. Within this framework, SDG 17, "Partnerships for the Goals," is particularly crucial as it highlights the need for high-quality, timely, and reliable data to drive progress across all goals.

The importance of data and analytics in realizing the SDGs cannot be overstated. Accurate and insightful data is necessary for several key aspects: assessing current progress, identifying existing gaps, informing policy-making, and guiding the allocation of resources. For instance, in addressing SDG 1, "No Poverty," data helps in understanding the demographics of poverty, allowing for targeted interventions. Similarly, for SDG 3, "Good Health and Well-being," data analytics play a crucial role in tracking disease outbreaks, understanding health trends, and improving healthcare delivery.

In the education sector, under SDG 4, "Quality Education," data can inform about areas where educational resources are lacking or where dropout rates are high, guiding efforts to enhance education systems. Additionally, for SDG 13, "Climate Action," data is indispensable for understanding climate patterns, predicting future scenarios, and formulating strategies to mitigate and adapt to climate change.

Advancements in data collection and analytics methods have opened up new possibilities. Mobile technology, for example, has revolutionized data collection, enabling real-time gathering and dissemination of information even in remote areas. Remote sensing technologies, such as satellite imagery, provide critical data on environmental changes, agricultural patterns, and urban development. These methods not only expand the scope of data collection but also enhance its accuracy and timeliness.

However, challenges remain in harnessing the full potential of data for the SDGs. These include issues related to data availability, quality, accessibility, and interoperability. In many parts of the world, especially in developing countries, there is a significant data deficit. This gap hinders the ability to make informed decisions and effectively address the SDGs. Moreover, data collected must be reliable and relevant to be useful in policy formulation and implementation.

To overcome these challenges, partnerships between governments, private sector, academia, and civil society are vital. These collaborations can foster innovation in data collection and analytics, ensure data sharing, and build capacities for data analysis. Furthermore, there is a need for a global framework to standardize data collection and reporting methods, which will facilitate comparison and aggregation of data across regions and countries.

Background: Across countries and disciplines, studies show male researchers receive more research funding than their female peers. Because most studies have been observational, it is unclear whether imbalances stem from evaluations of female research investigators or of their proposed research. In 2014, the Canadian Institutes of Health Research created a natural experiment by dividing investigator-initiated funding applications into two new grant programmes: one with and one without an explicit review focus on the calibre of the principal investigator.
Improving the career progression of women and ethnic minorities in public health universities has been a longstanding challenge, which we believe might be addressed by including staff diversity data in university rankings. We present findings from a mixed methods investigation of gender-related and ethnicity-related differences in career progression at the 15 highest ranked social sciences and public health universities in the world, including an analysis of the intersection between sex and ethnicity.
Characterising microplastics based on spectroscopic measurements is one key step of many studies that analyse the fate of microplastics in the environment. Over the years, many potential sources of error were identified, which can be seen by the implementation of anti-contamination protocols, measuring laboratory blanks or using less aggressive chemicals for sample purification. However, the identification process itself in the meaning of a traceable and transparent documentation is hard to find in many research studies.
Elsevier, TrAC - Trends in Analytical Chemistry, Volume 111, February 2019
The quantification of micro- and nanoplastics in environmental matrices is an analytical challenge and pushes to the use of unrealistic high exposure concentrations in laboratory studies which can lead to manifestations of ecotoxicological effects and risks estimation that are transient under natural conditions.
This chapter content advances SDG 3 and 5 by explaining how diethylstilbestrol has been used in the past by obstetricians, gynecologists, and family physicians to treat pregnant women with the intent to prevent miscarriage, and the antimiscarriage use of this drug had side effects that became tragically clear soon after the commercialization showing the failure of adequate preclinical testing.

United Nations University, February 2019.

Directly relevant to SDGs 8 (Decent Work and Economic Growth) and 16 (Peace, Justice and Strong Institutions), this piece explores an innovative methodology for modelling the risk of modern slavery.
This analysis of 160 cases of artificial intelligence (AI) being used for social good touches on all 17 of the SDGs, with Goal 3, good health and wellbeing, being particularly well documented in terms of AI for good.