Critical Data Challenges in Measuring the Performance of Sustainable Development Goals: Solutions and the Role of Big-Data Analytics

In 2015, 193 member states of the United Nations (UN) adopted a 15-year sustainable plan-the 2030 Agenda-to work toward achieving the Sustainable Development Goals (SDGs) by 2030. The SDGs are a set of specific, measurable, and time-sensitive goals for national development. The international community evaluates the SDGs using indicators based on available data and various methodological developments. Thus, effective impact measurement and data collection are critical to the success of SDGs. As poor-quality, outdated, and incomplete data lead to poor decisions, the critical lack of data to track progress across countries and over time presents a critical challenge. Quality data and statistics must be available and comparable over time to make significant progress on the agenda. Improving the quality of existing data is critical to assisting countries to make evidence-based strategic decisions. While both international- and national-level criteria for data quality evaluation exist, their practical implementation in the evaluation of specific SDG indicators is still in its early stages. The Inter-Agency Expert Group on SDGs (IAEG-SDGs) has made incremental improvements in the methodologies and data availability of SDGs, but various SDG indicators are beyond the financial and technical capabilities of many countries' statistical organizations and units. As such, the lack of data may be a major issue in the progress of assessing the performance of SDGs. Incomplete data also affect data quality, which hinders the accurate measurement of SDG performance and organizational decision-making. When data are incomplete, any method of analysis may fail to accurately predict SDG performance as well. These predicaments raise an argument about whether the lack of available data impacts the assessment of countries' performance. Without data-especially high-quality data-sustainable development is doomed to falter. We propose a contention about the deployment of big-data analytics-wherein sophisticated methods and tools are employed to analyze data and make huge and complicated data collections intelligible-as a viable alternative to solve the data-quality issues to better monitor progress on SDG indicators. We have identified lacunae in the literature pertaining to these issues, and thus critically investigate the data challenges in measuring the performance of SDGs. First, we investigate the use of big data as a cost-effective solution; second, we adopt a strengths, weaknesses, opportunities, and threats approach for a systematic analysis of the use of big data in assessing SDG performance; third, based on insights from the literature, we present strategies and methodologies to expand our discussion. We believe that this study makes a positive contribution to refining SDG indicators and advancing their monitoring.