In 2015, 193 member states of the 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 As poor quality, outdated, and incomplete data lead to poor decisions, the critical lack of data to track progress across countries and over time presents a critical challenge.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 a 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.

Big data and predictive analytics (BDPA) techniques have been deployed in several areas of research to enhance individuals’ quality of living and business performance. The emergence of big data has made recycling and waste management easier and more efficient. The growth in worldwide food waste has led to vital economic, social, and environmental effects, and has gained the interest of researchers. Although previous studies have explored the influence of big data on industrial performance, this issue has not been explored in the context of recycling and waste management in the food industry. In addition, no studies have explored the influence of BDPA on the performance and competitive advantage of the food waste and the recycling industry. Specifically, the impact of big data on environmental and economic performance has received little attention. This research develops a new model based on the resource-based view, technology-organization-environment, and human organization technology theories to address the gap in this research area. Partial least squares structural equation modeling is used to analyze the data. The findings reveal that both the human factor, represented by employee knowledge, and environmental factor, represented by competitive pressure, are essential drivers for evaluating the BDPA adoption by waste and recycling organizations. In addition, the impact of BDPA adoption on competitive advantage, environmental performance, and economic performance are significant. The results indicate that BDPA capability enhances an organization’s competitive advantage by enhancing its environmental and economic performance. This study presents decision-makers with important insights into the imperative factors that influence the competitive advantage of food waste and recycling organizations within the market.

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.