Critical Data Challenges in Measuring the Performance of Sustainable Development Goals: Solutions and the Role of Big-Data Analytics
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Abstract
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 As poor-quality, outdated, and incomplete data lead to poor decisions, the critical lack of data to track progress across countries and over time presents a critical challenge.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.
Keywords: sustainable development goals, big data analytics, data quality, data challenges, SWOT, performance
1. Introduction
Sustainability is a critical global issue (Miller, 2021; Milosevic et al., 2023), and its pursuit has been a major policy objective for the international community for the last three decades. In 2015, 193 Member States of the United Nations (UN) adopted a 15-year sustainable plan—the 2030 Agenda—for achieving the Sustainable Development Goals (SDGs) by 2030 (UN, 2022). The 2030 Agenda was founded on the principle of “leaving no one behind” to achieve sustainable development. In it, it is no longer acceptable to treat sustainable development as a three-pillared issue (Chen et al., 2023), but a balance and synergy between broader social, environmental, and economic issues is encouraged (Furtado et al., 2023). For the people and the planet, the agenda is a shared road map to peace and prosperity both now and in the future.
The 2030 Agenda comprises 17 SDGs that encapsulate 169 targets. These 17 goals are intended to address global challenges linked to environmental degradation, climate change, inequality, poverty, peace, and justice. The 2030 Agenda is global and universal in scope (Kuc-Czarnecka et al., 2023), respecting national realities, levels of development, and policies and priorities. By 2020, 231 unique indicators were categorized based on the proposed deployment approach and referring to data assessment; of these indicators, 123 are grouped in Tier 1, 106 in Tier 2, and 2 in multiple tiers (IAEG-SDGs, 2023).
The SDGs are a set of specific, measurable, and time-sensitive goals for national development plans. Effective impact measurement and data collection are critical to the success of SDGs (Fraisl et al., 2022). When it comes to pushing and accounting for change, tracking progress toward achieving the SDGs is essential, and countries must develop pathways to national targets to meet a shared agenda; this is only possible through stronger governance practices. The United Nations Statistics (UNStats) Commission thus approved a global framework of indicators known as UN SDGs indicators to track progress toward the SDGs (Grossi & Trunova, 2021; Mugellini et al., 2021). The indicators for the UN SDGs are evaluated by the international community based on their data availability and methodological development.
The SDGs can be properly implemented and tracked if decision-makers have access to the necessary data and analytical tools. Data analytics tools and analysis methods can uncover useful patterns in a rich data source. These patterns can assist decision-makers in appropriate decision-making for achieving sustainable growth. Because poor-quality, outdated, and incomplete data will lead to inaccurate SDG findings, insufficient data are a significant obstacle in monitoring the progress of SDGs, especially for comparing data over time and across countries. To achieve substantial advancement on the agenda, it is crucial to have reliable and comparable data and statistics. The absence of regularly updated and publicly available indicators further complicates the tracking of SDGs.
There exist both international- and national-level criteria for data quality evaluation; however, 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 many of the SDGs’ indicators remain beyond the financial and technical capabilities of many countries’ statistical organizations and units. In 2020, more than half of the targets in the SDGs could not be measured in the Asia-Pacific region owing to data scarcity; according to UNEP (2019), data are missing for 68% of the environment-related indicators, while data are only available for 33% of the 104 SDG indicators related to gender in Organization for Economic Co-operation and Development (OECD) nations (OECD, 2020). Thus, incomplete data sets can significantly impact the accuracy of evaluating SDG performance in many dimensions and lead to inaccurate predictions of such performance. Only effective strategies can address this issue. Solving the data incompleteness problem is crucial for evaluating countries’ progress and monitoring SDG indicators. Alternative solutions should be explored, such as big data analytics, to alleviate data incompleteness and assess SDG progress effectively.
Seven years have passed since world leaders committed to achieving the SDGs in their respective countries by 2030. There are 9 years left to reach the targets. The value of data and data quality is well known (Meng, 2021b; Van Eupen et al., 2021). Without data, especially high-quality data, sustainable development is doomed to fail—an issue seldom addressed in previous research. We thus critically investigate the data challenges in measuring the performance of sustainable development goals. First, we investigate the use of big data as a cost-effective solution; second, we adopt the strengths, weaknesses, opportunities, and threats (SWOT) approach for a systematic analysis of the use of big data in the assessment of SDG performance; third, we demonstrate that big data and big data analytics solutions have the potential to alleviate data incompleteness in assessing SDGs and can be effective in the successful implementation of SDGs. Based on insights from related research, we present strategies and methodologies to expand our discussion. We believe this study can contribute positively to refining the SDG indicators and advancing their monitoring.
The remainder of the article is organized as follows. In Section 2, we review SDGs and their measurements, the importance of data quality, and the role of big data analytics in SDGs. In Section 3, we conduct a SWOT analysis of the use of big data in SDGs. In Section 4, we discuss the implementation of big data in SDGs. In Section 5, we conclude this study.
2. Background
In this section, we discuss SDGs and their measurements, the importance of data quality, and the role of big data analytics in SDGs.
2.1. SDGs and Measuring the Goals Performance
SDGs, adopted by the UN in 2015, serve as a road map for achieving a better and more sustainable future for all. They guide communities to reach a consentient vision of sustainable development (UN, 2022). Readers can refer to (UN, 2022) for more information about the SDG framework, and the goals and indicators in the framework.
SDGs shed light on human development while emphasizing human rights, providing solutions to injustice and poverty, and addressing the problems of exclusion and inequality. SDGs have a broad scope that covers several environmental, social, and economic aspects, including energy preservation, sustainable cities, peace of communities, green infrastructure, and responsible consumption of resources. They aim to reach both developed and developing communities and allow the engagement of diverse partners in the development and evaluation processes. Referring to the UN, the indicators of the SDGs should be perceived as evenly significant. SDGs entail distinct, time-dependent, and measurable goals that should not be separated from the local development goals within each country (Chopra et al., 2022).
The IAEG-SDGs has utilized a tier system to monitor the progression of the performance and the data availability with international coordination. The robustness of the SDGs is reflected by the deployment of quantitative targets and indicators to represent the relations, rearrange the global and local resources, influence the utilization of resources, and develop narratives that frame the decision-making (Fisher & Fukuda-Parr, 2019). The evaluation of SDG performance is also influenced by previous experiences with the Millennium Development Goals (MDGs) (Woodbridge, 2015), with a more aspiring transformative scheme that aims to bridge the gap toward constructional change. The SDGs preserve the MDGs’ focus on eradicating poverty, but they also take a broader approach to global development and preserving human life on Earth (Woodbridge, 2015). Still, relying on quantitative-based indicators might impact the essence of human development and present major concerns about the measurement approaches.
The UN has encouraged the deployment of scientific approaches in both the development and the evaluation of the SDGs and within the decision-making process. In this context, technology facilitation has been promoted through the engagement of several stakeholders and partners to utilize the innovations and produce knowledge-based plans to address the diverse sustainability problems and requirements. The UN stresses upon the need for accessible high-quality data from reliable resources to guarantee effective plans of action. The measurement of the progression of the SDGs requires a robust and inclusive evaluation of the goals and targets that entail the monitoring of the achievement and identification of SDG obstacles. This process solely depends on data availability and quality. Besides the significance of the data in SDG deployment, the International Science Council report (Griggs et al., 2017) also stresses upon the need to measure the performance and assess the dynamic interactions between different goals and indicators through cross-disciplinary scientific methodologies. SDGs’ broad scope requires huge efforts to measure their performance, in which the availability of big data can provide a valuable asset for organizations in this context. In fact, the measurement of the SDGs indicators requires a careful disaggregation of the indicators into measurable parameters that need the provision of granular data.
2.2. Data Quality
Data is one of the most valuable organizational assets. Information is not restricted by organizational boundaries in today’s decision-making environments. The presence of large amounts of data and a diverse range of decision-making responsibilities complicates the decision-making process in these contexts. Because they can access data anytime and from any location, decision-makers are forced to become more responsive to changing circumstances. It is critical in such situations for decision makers to ensure the accuracy of the data they use. In the context of data science (Meng, 2019, 2021a), the lack of high-quality data is a common pitfall (Meng, 2021b). Organizations are increasingly concerned about data quality, which is now recognized as an important performance issue within decision-making processes (Batini et al., 2009; Chengalur-Smith et al., 1999). Data-quality issues have an impact on an organization’s information system regardless of its size (Nord et al., 2005). Further, the quality of data has a vital impact on the research outputs and their generalizability, researchers’ credibility, organizational performance, and governmental policies. Data quality is a significant factor in any analytic tool’s performance and prediction model.
There are many factors that impact the quality of data (Peltier et al., 2013; Wang et al., 2023). Missing value has been one of these critical issues for data quality (Cahsai et al., 2015). Missing data is a common problem in organizational research (Fichman & Cummings, 2003) that has harmful consequences for data analysis (Morshedizadeh et al., 2018) and, consequently, effective decision-making. Accordingly, missing values in data sets need to be addressed before processing the data to maintain the consistency of data inputs (Ryu et al., 2020).
Data strategies have long been the center of arguments in the research community (Valencia-Parra et al., 2021). The quality of data is defined as the level to which the data meets its defined goals (McCord et al., 2022). Data quality is represented by its accessibility as well as its representational, contextual, and intrinsic dimensions (Omri et al., 2021). Thus, the multidimensional nature of data makes measuring their quality challenging.
Data quality is often categorized based on the volume, accuracy, and completeness of the data. The volume refers to whether the data size meets its goal, the accuracy refers to the level of representativeness of the data to the real problem, and the completeness of data represents the ratio of missing values (Omri et al., 2021). Other researchers classify data quality into intrinsic and contextual features (Lee et al., 2002). Intrinsic features are objective in nature and natively linked to the data, while contextual features depend on the context of the data’s usage and collection (Ghasemaghaei & Calic, 2019). Contextual features include data reputation, accessibility, believability, quantity, added value, and relevance. In general, the quality of the data has been evaluated based on several factors that include the coherence, timeliness, accuracy, completeness, and relevance of data (McCord et al., 2022). This technique has been used before (Zmud, 1978) as a mechanism for measuring the management information systems’ performance. Management information systems encompass both the expanded scope of the business's information processing tasks and the increased focus on computer systems (King, 2003). (Ballou & Pazer, 1985) described and evaluated data quality using the aforementioned dimensions. (Wang & Strong, 1996) conducted a thorough investigation of the nature of data-quality attributes, while (Gelman, 2010) demonstrated the devastating effect of data errors on the accuracy of decision-making.
Because measuring the SDGs heavily relies on the availability of the data for their indicators, the quality of data is critical in measuring SDG performance, and data-driven decision-making plays an important role in this assessment. Data-driven decision-making can assure resources are set aside to ensure that the SDGs are used effectively and efficiently. The policymakers in organizations that are responsible for such assessment can identify areas that need attention, track progress toward goals, and make informed decisions about which interventions to prioritize by analyzing data. For tracking progress toward the 17 goals and 169 targets, accurate data collection, analysis, and reporting are essential. In fact, poor data quality can lead to incorrect conclusions about the efficacy of policies and programs aimed at achieving the SDGs, whereas high-quality data can help identify areas that require additional attention and resources. In addition, for the evaluation of SDG performance, missing values can be a significant problem because they can lead to biased estimates of progress made toward the SDGs. Thus, we must consider all missing values when evaluating SDG performance for an accurate estimation of each goal’s progress. For optimal decision-making, we must also assure the quality of all data at all stages of data management—from data collection to the final analytical stages. Consequently, to accurately track the progress toward achieving the SDGs, it is critical to invest in improving data quality–control measures to enable informed decision-making based on reliable data.
In summary, in the context of SDGs, the above-mentioned issues raise an important question that needs to be answered before the effective deployment of the data: Do the data meet the required quality criteria in measuring SDGs?
2.3. Big-Data Analytics and Its Role in SDGs
The term ‘big data’ refers to several data features, including size and volume, velocity, and variety. This term is also strictly linked with other terms such as predictive analytics and data science (Hofmann, 2017; Waller & Fawcett, 2013). The volume of data reflects the increasing data amount that resulted from the high-digitization shift (Acciarini et al., 2023; Newell & Marabelli, 2015). A variety of data represents the diverse resources and formats of data that entail unstructured, semistructured, and structured data (Li et al., 2008). Velocity reflects the amount of time in which the data is generated in real time (Ghasemaghaei et al., 2018). Big data allows scholars to locate insights from the data without predetermined hypotheses (Lycett, 2013). This direction of scientific research goes beyond the dependence on the known to a focus on the unknown. This direction adopts the ignorance-based view instead of the knowledge-based view, in which relying on the existing knowledge cannot alone meet the increasing demands of the world (Sammut & Sartawi, 2012). This shift has been induced by the availability of big data and the huge development of big data and predictive analysis approaches (Ali et al., 2021; Chauhan et al., 2022; Yadegaridehkordi et al., 2020). This perspective has been especially popular in recent research, wherein there is growing emphasis on the availability of big social data and user-generated content to guide the researcher’s approach (Nilashi et al., 2022; Zibarzani et al., 2022). Still, the massive utilization of big data in practice and research has been combined with arguments about the quality and the validation of the data.
The literature contends that poor data quality negatively influences the organization’s ability to generate insightful outcomes from the data (Ghasemaghaei & Calic, 2019). However, data with high levels of quality has an impact on data diagnosticity and the organizations’ decision-making quality (Ghasemaghaei & Calic, 2019). The concept of data diagnosticity identifies the approach in which in-depth and refined insights are inferred from the data to be utilized in several application domains (Grange et al., 2019). This approach can be achieved through a deep analysis of the data aiming to gain beneficial outcomes within a specific context or regarding a particular phenomenon (Ghasemaghaei & Calic, 2019). The organization inspects the data to estimate its current position compared with its competitors in order to identify the factors that impact its performance as well as to predict potential performance scenarios. Data diagnosticity deployed on big data can be analyzed to enhance the performance of the organization and address the obstacles it faces, which brings tangible and intangible added value to the organization (Erevelles et al., 2016).
The level of accuracy and correctness of the decision reflects the quality of the decision in the organization. According to (Phillipps, 2013), the utilization of big data can enhance the quality of the decision based on 49% of the surveyed organizations. The analysis of big data has been deployed to enhance the quality of the decision, with an insufficient concentration on the topic in previous literature (Janssen et al., 2017). Thus, many organizations have begun to process big data to enhance the quality of their decisions. The big-data analytics capabilities help organizations improve the decision quality, but also support organizations with conviction about the reached decision (Tan et al., 2017). The big data that are integrated from internal and external sources provide the organizations with insights that are inferred from the hidden patterns of the data and help them reach better decisions. The advanced innovations and their capabilities can aid in analyzing large volumes of data within a short time. They can help organizations to grasp the cause–effect relationship and analyze the drivers of a specific event, which allows decision-makers to predict future scenarios and design practical courses of action (Ghasemaghaei et al., 2018).
Big data can play a significant role in data enrichment, and thus help improve data quality by filling in the gaps in the data. Data enrichment entails the integration of new data sources to supplement the current sources of data. This process can significantly help when the current data cannot be fully helpful to reach a firm conclusion or solution to a problem. Data enrichment using big data in structured and unstructured data can be utilized to improve the data quality and enhance the decision quality by incorporating supplementary and context-based data. The supplementary sources of data share common features with the original data sources, and aim to complement them with evidence-based data while maintaining the original data set.
For the decision-making process within SDGs, data enrichment allows us to integrate diverse sources into one merged, unified, more useful, and more valuable source; it allows for more in-depth granular analysis of the data and overcomes the shortcomings of the raw data. This way, data enrichment adds value to the SDGs by supporting decision-makers in UN agencies and national governments, who gain a better understanding of the goals, targets, and indicators of SDGs in the context of a certain place and time. The data enrichment process is not restricted to a specific source of supplementary data and diverse sources have been utilized to enrich the data, such as social media data. Other popular sources of data enrichment include geographic and demographic data sources. The integrated sources can be internally or externally collected. The data can be scraped from available public data sets, for example, decision-makers can utilize the user-generated content that is represented by the online reviews to get a deep understanding of the current situation on the specific targets’ indicators of SDGs. The main advantage of big social data as a source for the data enrichment process is the incremental update process of the data and increasing growth in size. In the SDGs, data enrichment can add value to the initial data through many aspects. The data enrichment process can be utilized to improve data quality and accuracy, while also addressing the missing value problem of the targets’ indicators of SDGs. Overall, data enrichment of databases with missing values, with the aid of the data scraped from the appropriate sources, enhances the quality of the data and improves its shareability.
3. SWOT Analysis of Use of Big Data in SDGs
The 2030 Agenda presents a scheme of action for environmental, social, and economic development (Guo et al., 2022). Research on the performance of the SDGs has been carried out in a scattered manner with organizational and individual efforts and less focus on the simultaneous performance of the goals and their integrated essence (Keynejad et al., 2021). Meeting SDGs is a challenging task, and there is a need for collaboration among researchers, economists, social scientists, engineers, and decision-makers to present effective implementation plans (Matson, 2022). In this context, it is important to mention that the evaluation of the SDG performance is executed by local governments, and the data gathering, monitoring, and assessment processes are performed by National Statistical Offices (NSOs) (Allen, Smith, et al., 2021). However, other nonofficial parties such as academic institutions have a significant role in identifying and quantifying the indicators, performing data gathering and integration, and designing scales and approaches for assessing the indicators (Allen, Metternicht, et al., 2021). However, there are common obstacles that face researchers and decision-makers in the deployment and evaluation of the performance of the SDGs, which need to be fully explored through the SWOT analysis.
SWOT analysis refers to the evaluation of the strengths, weaknesses, opportunities, and threats, focusing on a specific topic and aiming to get an in-depth understanding of its current situation. It aims to explore a topic referring to an organized, accurate, and fully covered approach with an emphasis on the inner and outer factors (Wang & Wang, 2020). This analysis will help decision-makers frame a strategic plan and design effective policies (Jasiulewicz-Kaczmarek, 2016). A deep understanding of the topic will also help locate both the negative and positive dimensions through exploring opportunities and obstacles. Strengths and weaknesses represent the inner factors, while opportunities and threats represent the outer factors (Akçaba & Eminer, 2022). In this context, we divide the factors that impact the deployment of big data to meet the SDGs into internal and external types. The internal factors will be represented by strengths and weaknesses that impact the SDG performance and evaluation in terms of big-data deployment within the country itself. For the external factors that impact the performance of the SDGs in terms of big-data adoption, we focus on the factors that are outside the country and are related to global dimensions.
The normative transformation from the MDGs to the SDGs aimed to include both the local and global contexts and obstacles that are encountered to worldwide sustainability. Although international standards have been leading the evaluation of SDG performance, the specific context of each country imposes a need for deep knowledge of the policies needed at the local dimension. It is necessary to localize the set of measures set forth by the UN in order to achieve the 2030 Agenda. While the SDGs have an international reach, their capacity to be carried out is contingent upon the degree of importance given by local organizations and the degree of funding rivalry within those organizations (Sánchez-Rivero et al., 2023). Hence, we found the SWOT analysis very suitable in the context of this study, because it aims to explore both the internal and external factors that impact the evaluation and the progression of the SDGs. A summary of the SWOT analysis is presented in Figure 1.
3.1. Strengths
To assess the strengths of the deployment of big data, we explore the internal factors within the country. As big data is a valuable source of information with increasing significance, such data can be provided or collected by decision-makers, managers, governments, third parties, and private agencies from a variety of sources. The available big data and the diverse sources of data, when combined with suitable technologies, have vital impacts on human well-being and worldwide wealth. Research on big data reveals robust findings in terms of exploring value, assessing impacts, locating the drivers of the results, and studying the cause–effect relationships. The literature also confirms the impact of big data predictive analytics (BDPA) on the environmental, economic, and social dimensions of sustainability (Raut et al., 2021) in both developed and developing countries, although there are more barriers to its adoption in developing countries (Moktadir et al., 2019).
Nevertheless, BDPA in the context of sustainable development and within the limits of big-data availability has gained much attention (Bag et al., 2022; Hazen et al., 2016; Singh & El-Kassar, 2019). BDPA has been shown to promote performance in sustainable manufacturing (Ma et al., 2022; Tayal et al., 2020), sustainable tourism (Agrawal et al., 2022), human well-being (Weerakkody et al., 2021), renewable energy (Ifaei et al., 2017), sustainable agriculture (Ifaei et al., 2017), sustainable supply chain (Kusi-Sarpong et al., 2021; Peng et al., 2022), and food waste (Ciccullo et al., 2022); it has been employed for smart cities development, especially in contexts of management strategies of different sources of data (Zhang et al., 2022). Regarding small- and medium-sized enterprises, BDPA is anticipated to provide enterprises with a sustainable competitive advantage (Behl et al., 2022) or sustainable value creation (Tamym et al., 2022). BDPA has also been employed in food waste management through a shift in linear to circular supply chains (Ciccullo et al., 2022).
We thus argue that BDPA can be utilized effectively to further SDGs and meet the goals within the target deadline (UN, 2015). It can be utilized to investigate the huge volumes of data that are gathered using phones, sensors, platforms, and satellites in order to analyze financial patterns, gender gaps, social welfare, and inequality (Arfanuzzaman, 2021); for the collection, tracking, monitoring, and evaluation of the SDGs; to leverage insights from both structured and unstructured data, including business data, consumption data, financial transactions, weather data, maintenance records, manuals, and machine sensor data through data processing that allows decision-making without the risk of human manipulation; and for employing big data as rich sources of demographic data, community services, governmental strategies, and natural resources. The availability of such sources allows for countries to take well-guided actions. This is especially relevant when predicting future trends in climate change, population growth, and economic development through the application of BDPA. Accordingly, BDPA can aid policymakers in proactively handling potential and future challenges and possibilities. It enables governments to choose their priorities and improve their resource allocation strategies.
3.2. Weaknesses
Despite the expected benefits of big data deployment for SDGs, several weak points should be carefully addressed within each country’s plan (Kusi-Sarpong et al., 2021). Big data are usually gathered from different sources and within different formats, which makes data integration a complex affair. Data collection efforts may become dispersed owing to the many methods of data collection by different local institutions, making data comparisons across nations and regions—which are crucial for determining how well the SDGs are being achieved—very difficult. The data are usually stored in different mediums, and the storage approach usually impacts quality. Organizations need large-scale infrastructure to evaluate the quality of the data. Yet, the available innovations in the BDPA might not be compatible with the organizations’ infrastructure. BDPA adoption requires appropriate financial resources to be deployed in the organization in terms of the tools and the infrastructure.
The skills of the employees within the organization should be compatible with the BDPA deployment and management. In general, there is a lack of required skills for BDPA among the employees, which might lead to wrong assumptions about the data. Assigning suitable employees to the appropriate task within the context of the BDPA functionalities might take time and impact the organization’s performance. The adoption of the BDPA is also affected by the individual’s willingness to accept it and the government’s support through appropriate policies and strategies. The lack of regulations in data copyrights within organizations is another important obstacle. There is also a need to adapt to the dynamic demands of the market, wherein the need for data depends on the fluctuating requirements of the market.
The lack of a clear evaluation plan and sufficient data has restricted the progress of SDGs. Inaccurate collection and evaluation procedures of the data might impact the evaluation of the performance of these SDGs as well. Besides, the inaccurate documentation of the procedures of the data collection and assessment makes it complex to reproduce the outcomes.
Data validation is a vital obstacle confronting custodian organizations and governments in the performance and evaluation of SDGs (Gennari & Navarro, 2019). The UN Statistical Commission has promoted custodian organizations to generate SDG indicators referring to data from local statistical systems (Gennari & Navarro, 2019). The role of these organizations is to control SDG performance, which might require the amendment of local data to assure they meet international statistical standards, confirm their comparability ability, and assure their correspondence with local and international estimations. These organizations might need to provide estimations for SDG indicators when there are missing data, incomplete data, or data with poor quality. However, they should refer to decision-makers in the targeted countries when there is a need to provide an estimation of the data to confirm the validity of estimation models (MacFeely, 2018). The lack of coordination among the custodian organizations and the governments, as well as the need for standard directions, has identified in the deployment of the SDGs. To address this gap, the IAEG has devised some directions by clarifying the obstacles in the data validation for SDG indicators and the coordination problems of the global statistical systems, among which are the broad constraints of the capacity that restricts the compilation process (Gennari & Navarro, 2019).
3.3. Opportunities
There are many opportunities in big data deployment for assessing the evaluation and performance of SDGs. First, BDPA provides strong capabilities to analyze data from international parties, which, in turn, enables the evaluation of SDG performance. In this context, there is a need to establish high standards of credibility and impartiality for BDPA approaches. The methods, sources of data, classifications of data, and conceptual definitions in this process, and the progress, when clearly identified, will provide robust sources for international statistics.
BDPAs leverage varied databases from many different sources and handle more thorough and in-depth data than conventional data collection techniques. These data sets can include information from social media, mobile devices, satellite photography, and other sources, offering a wealth of data for monitoring SDG development. Besides, real-time monitoring of SDG progress can be provided by BDPA, which can help pinpoint problem areas and promote quick decision-making.
Second, the innovations in big-data analysis can help to frame clear policies for the management of data in which the data that is subject to confidentiality rules are appropriately treated and protected with legalizations. The data collection approaches should be appropriately correlated with quality standards, meet timeliness and cost-efficiency criteria, and reduce data providers’ reporting charges. Third, big-data analysis provides a unique opportunity for global coordination and training of data management methods to meet the SDGs. There is a need for global coordination of data analysis programs to assure the quality of the data, coherence, and control of global statistics, and avoid duplicated data. This opportunity has been endorsed by the initiative carried out by the UN Statistics Division and the United Nations Institute for Training and Research (UNITAR), in which StaTact was launched to enhance the accessibility to data, data usage, and data literacy. StaTact is an innovative instrument that enables nations to address measurement gaps that impede the monitoring of local strategies (StaTact, 2023). Utilizing the potential of Agenda 2030 and the SDGs, it strategically supports the resolution of issues and difficulties (StaTact, 2023). To effectively utilize StaTact, several workshops were performed under the title of “Governance of Data Ecosystems for SDGs” in 2018, followed by national pilot pieces of training in 2019. Other training workshops were carried out under the title of “Data for the 2030 Agenda” to control the progress on SDGs (UNITAR, 2019).
Big data has opened new routes for collecting and coordinating high-quality official statistics (UN, 2014). The report, which was released by the Secretary-General’s Independent Expert Advisory Group on a Data Revolution for Sustainable Development (UN, 2014), presented directions about the deployment of emerging technologies that include data sharing, big-data analytics, internet connectivity, and new data sources. The report indicated the need to develop worldwide collaboration routes for the collection of sustainable development indicators that include both private organizations and community members. It also stressed the need to promote the development of a “Network of Data Innovation Networks” by the UN that allows several ‘buddies’ to be part of the data collection. The report concentrated on the significance of identifying and monitoring new data sources and indicated the need to locate the missing data and their linked indicators. The need to invest in data has also been stressed in the report. Besides, the need to enlarge the capacity of NSOs and their participation in unofficial data management approaches, to foster the generation, collection, and utilization of data, has been stressed. Big data–analysis techniques enabled the analysis of multi-source data from various infrastructures and with diverse forms and provided solutions to address the lack of data and monitor the achievement of the SDGs.
3.4. Threats
Focusing on the practical deployment of the SDGs, the completeness of the data has gained great attention from decision makers. The shortage of data has been indicated as a basic flaw in the evaluation of the deployment of SDGs (Guo et al., 2022). As indicated by the UN, for five goals, and fewer than half of the 193 UN Member States, there is a shortage of data on several folds, of timelines, regions, and evaluation approaches (Sachs et al., 2021). For example, there is a shortage in data for half of the goals in Asia (Guo et al., 2022). In terms of the evaluation approaches, among 230 indicators of the SDGs, only 56% have well-established evaluation approaches with global standards, while 42% suffer from data shortage (Guo et al., 2022). This issue raises the need for allocating new sources of data in the validation process such as big data (United Nations Statistics Commission, 2018).
To achieve the SDGs, clear monitoring of the overall performance and regular evaluation of the schemes of each goal are required. International organizations in charge of data collecting and analysis may not be coordinated well, which could result in wasted time and inefficiency. It may also result in coverage and accuracy shortages in the data. The lack of a regular evaluation process also has a significant impact on meeting the SDGs. Regular evaluation will aid in gaining insights and locating the gaps in the development's progress. It also helps in identifying the gap between achieved and desired outcomes. The evaluation process should be carefully identified with a time frame and by assigning the evaluation task to a credible agency, while allocating the needed resources; it should be standardized and open for corrections in the plans and strategies in order to enable the learning and improvement of the data collection and analysis processes. The standardization of the evaluation process is an important factor in assuring that the results are consistent, transparent, and credible. Standardization should be assured on the national level and accordingly on the international level (Srivastava, 2018).
Figure 1.Summary of the SWOT analysis of the SDGs and big data. SWOT = strengths, weaknesses, opportunities, and threats; SDGs = Sustainable Development Goals.
4. Implementation of Big Data in SDGs
The 169 goals, along with their interrelated 230 indicators, face a critical challenge in terms of the evaluation of the progression on the local and global degrees. The research on the SDGs highlights a research gap in the availability and accessibility of the data that is required for the deployment and the assessment, with MDGs being no exception to this shortage. Hence, several schemes of plans have been proposed to ensure the progression of the goals, and several revisions were introduced.
The goals and indicators of the SDGs are treated as equally important and a systematic approach is deployed by the IAEG-SDGs under international monitoring to assess the data availability and accessibility. Apart from the MDGs, SDGs are a more collaborative and participatory scheme that includes small and low-income countries playing a major role in the development process. Besides, the SDGs’ proposed agenda goes beyond the community’s development to cover the protection of the environment, which usually takes different routes than the traditional political alignments. Hence, the SDGs offer more diversity in the goals and a more valuable agenda than the MDGs (Dang & Serajuddin, 2020).
Similar to other contexts, the use of big data for SDGs is influenced by several factors related to human, organizational, environmental, and technological dimensions. As we argued previously, big data analytics approaches have an influential impact on SDG achievements. Hence, the factors that impact the adoption of big data analytics will influence SDG achievement in this context. For the use of big data for SDGs, it is important to know how these factors will impact the selection of the appropriate analytics technique that can aid organizations to reach the right decisions.
Human factors impact the performance of SDGs, with an emphasis on the individual’s attitudes and behaviors toward big data analytics. Big data analytics techniques rely on human factors because they require highly skilled individuals to analyze the data. Several job titles have been linked recently with the analysis of big data such as data scientists, data engineers, and data developers (De Mauro et al., 2018). Organizational factors, however, have diverse and influential impacts on the deployment of SDGs. Many organizations now are aware of their role in SDGs’ achievement and reflect this in their sustainability assessments and combined reports. Many businesses highlight their environmental and social sustainability initiatives using SDGs’ icons. This makes sense given that many organizations view their sustainability initiatives as crucial to their continued existence and expansion and that many of them had already adopted such initiatives prior to the SDGs (Nishitani et al., 2021). These organizations will be more tolerant toward the adoption of big data analytics than organizations that focus on short-term performance. Hence, it is important that organizations embrace big-data analytics approaches in their strategies, business models, and regulations. Besides, organizations have a vital role in facilitating the usage of big data analytics techniques and promoting the shift beyond traditional analysis approaches. They have a strong link with the human factor, as they are responsible for the presence of skilled experts in the area of big data in their human capital—either by upgrading the current human resources they have or by the acquisition of new talented ones (Brynjolfsson et al., 2011).
Environmental factors represent external forces that can affect an organization’s decision to embrace big-data analytics (Nilashi et al., 2023; Xu et al., 2017) in order to meet SDGs. It is crucial to develop supporting environmental policies and management practices (Lutfi et al., 2023) to encourage the application of big-data analytics to advance SDGs. This may entail making investments in systems for data collecting and management, offering instruction and technical assistance for data analysis, and encouraging the rise of environmentally friendly technology and behaviors. Technology factors have an impact on the adoption of big data analytics with several related factors that have been explored in the literature, such as data volume, relative advantage (Lutfi et al., 2023), complexity, compatibility, and observability (Maroufkhani et al., 2020). Big data analytics for sustainable development can be influenced by the ease of access to information and communication technologies (ICTs). For instance, poor access to ICT infrastructure and a lack of technical skills may exist in many low-income countries, which may limit the potential of big data analytics to promote progress toward SDGs. An organization would be far less inclined to employ and implement big data analytics than it would be if it did not believe that the adoption of BDA is difficult and inconsistent with current IT systems and infrastructure maintenance procedures (El-Haddadeh et al., 2021).
Thus, the technology factor is the core of big data analytics techniques, as it allows the deployment of robust, fast, and cheap innovations to acquire, store, transfer, and process the data to reach the most appropriate decision for the organization. Following Moore’s law, the capacity of storage has been enhanced with the advancements of integrated circuits in the past 5 decades, with transistors’ density being doubled every 2 years (Moore, 2006). The processing of data is becoming faster and more available day by day, referring to the advancements in distributed computing and the huge growth in networking capabilities. Such an example of a related innovation to big data is Apache Hadoop Software, which represents a new open-source scheme that enables segments of dispersed machines to collaborate to present high performance along with parallel computing (De Mauro et al., 2018). Apache Hadoop enables the usage of simple programming models to distribute the processing of massive data sets among computer clusters (Apache Software Foundation, 2023). The advancement in cloud storage and cloud computing is another important innovation that facilitates big data analytics techniques. Cloud computing can aid organizations to control the expenses of big data analytics and monitor the utilization of resources carefully.
The adoption of big-data analytics is further affected by several other environmental factors, such as external pressure, external support, privacy and security aspects, and many others (Yadegaridehkordi et al., 2020). External pressure is reflected by the power imposed by the outer environment that goes beyond the organizations’ capital. External pressure can be formed by other competitors in the market or other partners in the market (Sun et al., 2018). The support from the government for the shift toward big data analytics has an impact on the adoption of such innovation. Support from other vendors also has an impact on the adoption of big data analytics (Maduku et al., 2016). Security and privacy are important factors in the utilization of big data analytics. Big-data analytics approaches entail security and privacy risks that need to be addressed to facilitate the adoption process (Salleh & Janczewski, 2019); their deployment for SDGs may manifest differently depending on these considerations. Strong governance and policy frameworks can prevent data misuse, privacy invasion, and other harmful effects on society and the environment, but poor governance and policy structures can negatively influence credibility and confidence in the utilization of big data analytics.
In summary, effective deployment of SDGs entails different factors, including human, organizational, technological, and environmental dimensions (see Figure 2). Along with these factors, data play an important role in the assessment of the performance of SDGs. Yet, in the context of SDGs, making sure that the available data meets the required level of quality will aid governments in appraising their performance, meeting the requirements of inter-organizational partnerships, and planning more focused strategies (Mugellini et al., 2021). Evaluating the quality of data has been indicated as a critical process in the contexts of social science and human behaviors (Jager et al., 2022). The nature of the data in these areas, being complex and pretty inferential, brings vital issues in terms of the reliability and validity of the data and makes it difficult to design measurable indicators (Drost, 2011). Because many of the SDGs’ indicators reflect social phenomena that are complicated to define and assess, evaluation of the SDGs suffers from the same issues considering the quality of data.
Figure 2. Stages and factors related to big data and SDGs. SDGs = Sustainable Development Goals.
Based on the preceding discussion, we propose a set of guidelines that decision makers could utilize to incorporate big data in all phases of the evaluation of SDG performance, from data allocation to the final evaluation measures. To do this, we divide the SDG deployment and assessment process into several stages, starting with the identification of data sources. Knowing the sources of the data and whether they will come from people or organizations is crucial. The mechanisms for gathering the data should be defined in the next phase. This entails outlining the methods and equipment that will be used to gather the data. The following phase involves data storage. This is a crucial stage, and any flaws in the data that have been gathered must be fixed. To ensure the accuracy and dependability of the results, it is essential to choose the best analytical technique. Finally, assessing the data is crucial for establishing reliability.
4.1. Allocating Data Sources
The availability of big data has enabled emerging and diverse sources of data to be accessible in interpreting insights for decision makers in real time. These insights can include behavioral patterns and trends that are of great importance to SDG performance. Emerging sources of big data present a valuable chance for countries if considered carefully, to ease all other stages of the collection, management, and evaluation of data. However, diverse data sources that are utilized for the evaluation of SDG performance pose a significant challenge for decision makers, as the integration of data from several sources might present different outcomes. This issue can be explained by the different approaches that are utilized by these organizations to manipulate the data. For example, these organizations might use different forecasting approaches of the economic time series, in which models’ estimations rely on revision schedules. Besides, the data collected from two surveys by the same national organization might present different outcomes, in which both developed and developing countries face this issue (see the discussion in (Dang et al., 2014). (Deaton, 2005) also studied this issue and indicated that the development ratio of consumption based on local accounts is larger than the ratio based on household surveys for many countries. (Dang & Serajuddin, 2020) more recent study also supports this finding, where they confirmed a difference in consumption development degrees between household surveys and national accounts. They further argued that their conclusions can be applied to the whole distribution of consumption, in which the risk of inconsistencies should be carefully considered.
Research centers can be utilized as resources for the data in the SDGs evaluation, particularly aiming to facilitate the usage of big data. An example of these centers is the International Research Center of Big Data for Sustainable Development Goals (CBAS) (Huadong, 2020), which was developed for global-based research. The center presents a great opportunity for collaboration among diverse parties and enables the development of inclusive strategies incorporating big data. This center is recognized as being the first research center that incorporates capacity building, talent fostering, data service, big data, and technological innovations to meet SDGs (Guo et al., 2021). Another example is the CASEarth program of the Chinese Academy of Sciences, which is developing digital earth sciences and big earth data cloud service portals. The program is also developing a sequence of SDG satellites for controlling and assessing the implementation of the SDGs.
Another important source of data in this context is social data. Organizations might use social networking sites as a source for big data analytics, and perhaps utilize social media listening techniques to follow and examine online discussions pertaining to particular SDGs (Franco-Riquelme & Rubalcaba, 2021). Organizations can use diverse technologies to gather and analyze data focusing on a variety of variables, including sentiments, places, demographics, and other important variables.
4.2. Data Collection
The monitoring of the progress of SDGs is contingent on huge volumes of data. These data will be utilized in the decision-making and planning of strategic frameworks. The collected data holds significant value and should be adequate, apropos, timely, insightful, and valuable. This indicates the need for the involvement of collaborators and data creators at different levels of involvement. Data collection will involve innovative technologies that can ease this process (Chopra et al., 2022). The huge advancement of big data has enabled new data collection approaches to be considered. With this significant progress in big data concepts and methods, there is still tremendous potential for systematic implementation of these concepts in policy and decision-making systems. Hence, data collection approaches should integrate the concepts and applications of big data and allow emerging technological-edge solutions toward addressing global challenges (Guo et al., 2021). Artificial intelligence techniques, along with the growth in the volumes of big data, allow intelligent tools and applications to be integrated into our lives in different fields such as smart cities, health care, and citizen science. Data engineering has been also integrated, for example, into the medical field, allowing the collection of big significant volumes of data. Crowdsourcing involves gathering tasks, data, or reviews from a huge slot of individuals through social media portals and other tools. Along with citizen data, crowdsourcing has been used to enhance vital resilience approaches, including disaster-related applications, warning tools, and community resilience infrastructure (Guo et al., 2021). The advancement in artificial intelligence approaches has also participated in the advancement of big data mining techniques through advanced tools and applications.
4.3. Data Storage
The age of digitalization, along with the increasing growth of big data, has made it necessary to utilize appropriate storage systems (Roy et al., 2022). The current storage systems are limited by their inability to store huge volumes of big data. The report generated by the International Data Corporation (IDC) indicates that the volume of data that is produced over the world will reach 44 zettabytes by 2020 and will be duplicated every 2 years; it stresses that this growth will exceed the available storage capacity (IDC, 2014). Older systems, such as relational database management systems, are usually deployed for storing structured data (Ahmed & Thomo, 2017). Still, these systems suffer from limitations in handling large volumes of data. Focusing on SDGs and big data, we find a need for a reliable and scalable storage scheme that can maintain high quality standards and guarantee data accessibility and availability. Accordingly, many storage systems have gained the interest of researchers and developers, such as OpenStack Swift, the Hadoop Distributed File System (Shvachko et al., 2010), and Google File System (Ghemawat et al., 2003). These systems support scalability, distribution, and virtualization to effectively handle huge volumes of data.
Nevertheless, there is a need to investigate storage technologies and deploy those with longer lifetimes, larger throughputs, and higher storage densities to overcome the data-capacity gap. This issue is particularly important in the storage of the big data that are utilized in the SDGs evaluation.
4.4. Data Evaluation
The evaluation of data for SDGs entails data sources, collection, and management approaches. Although big data present rich sources for decision-makers, there exist credibility risks. There is a need to establish standards for the evaluation of the credibility of the partners involved in the data collection and the sources of the data. The collected data should also be evaluated in terms of their relevance to SDG performance. NSOs thus emphasize the quality of the data in terms of sources, storage, documentation, standardization, reliability, bias and error rates, uncertainty, accuracy, and fidelity (Kitchin, 2015). The utilization of big data in SDG evaluation should be critically investigated regarding the different conceptual natures of big data. Big data are usually gathered as a by-product rather than through traditional well-designed approaches (Allen, Smith, et al., 2021). Such an example of big data is data from social media platforms collected without a predesigned approach or quality measure or targeted population. The unstructured nature of big data indicates more complexity in reaching meaningful insights from the data; hence, a careful assessment of the analysis approach is required. In fact, data-quality issues and potential solutions can be more easily identified and pursued with the help of a quality assurance framework. This will help government agencies establish and sustain a culture of data quality in measuring SDG performance.
4.5. Data Analysis
Following data collection, storage, cleaning, and mining, a data analysis stage is required. However, big data are usually not easy to interpret and entail noise, heterogeneity, unreliability, and dynamicity. The features of big data distinguish it from traditional databases, which evolve around relational databases (Shah et al., 2015). Although conventional data warehousing schemes resemble big data in the main goal, they differ in the appropriate analysis technique and the structure of the data (Kune et al., 2016). Outdated analysis approaches cannot be applied to big data, and there is a need for huge capabilities of data storage, data capacity, and computing power (Barbierato et al., 2014). There is also a need to act wisely in terms of choosing the appropriate analysis approach for big data to gain the ultimate impact and the desired value. Choosing the appropriate analytic technique is, however, affected by several factors, including the availability of skilled people (Phillips-Wren & Hoskisson, 2015). The advancement of online computing techniques that allow fast reach, integration, analysis, and interpretation of data brings a new challenge in choosing the best analysis approach (Sivarajah et al., 2017). Besides, the quality of data has a critical impact on the chosen analytic technique.
At this point, it is important to discuss how missing data can impact the assessment of SDG deployment. The interpretation of the data will be affected by the data’s availability over years and not over restricted, sparse time slots. Some types of analysis require the availability of data over a sequence of time slots in order to discover useful, accurate patterns from the data; such data are harder to interpret when the time slots thereof differ among regions. This predicament highlights the impacts that missing data can have on interpreting the SDG progress. One vital shortcoming of the data that might restrict the choice of the BDPA technique is the lack of regularly produced data on about one-half of SDG indicators. This shortcoming is enlarged when considering indicators of the environment wherein there is a clear gap in the available data worldwide (Allen, Smith, et al., 2021).
5. Concluding Remarks
This study presents several contributions that decision makers may use, particularly considering the deployment of big data and innovations in the progression and evaluation of SDGs. First, decision makers need to understand the nature of big data and BDPA techniques for their efficient utilization toward SDG deployment and evaluation. While big data are considered a valuable asset, they should not replace conventional data sources. BDPA should serve as an addition to conventional and official statistical techniques that aid organizations in drawing sustainable frameworks. Thus, big data represent a data revolution that runs complementary to traditional data analysis techniques—reinforcing and supporting previous methodologies—to provide evidence-based outcomes.
BDPA techniques can, if deployed carefully, provide a solution to addressing the gap in missing data and provide cost-effective evaluation techniques. As an essential part of the data ecosystem, big data are represented by several factors that include the nature, generation approach, sharing approach, and, most importantly, analysis approach of the data. These factors integrate several stakeholders, including individual actors and organizations. Besides, the big data ecosystem entails diverse tasks that entail discursive, prescriptive, predictive, and descriptive functions. Hence, a deep understanding of all these aspects is critical to assure the effective deployment of big data in achieving the SDGs.
Second, big data are represented by their velocity, variety, and volume; they serve as a valuable option for the holistic evaluation of SDGs, enabling decision-makers to overcome the obstacles presented by traditional sources of data. The literature presents various evidences to show that big data can provide solutions to environmental (Xu & Zhang, 2022), social (Foresti et al., 2020), and economic (Modgil et al., 2021; Stekelorum et al., 2021; Wu, 2022) issues—the three pillars of sustainability. These solutions have overcome the shortcomings of traditional statistical techniques and yielded high performance.
Third, the deployment of BDPA runs smoothly with the SDGs agenda, as it allows real-time management of data. It enables the analysis of human behaviors at the micro and macro scales, and allows stakeholders to investigate the included measures and their relations. BDPA techniques have presented multidimensional and innovative approaches to analyze the data and evaluate performance, which presents a great opportunity for improvements in SDG analysis.
Fourth, organizations should draw a course of action that allows the integration of several sources of data through efficient management of the data ecosystem. The deployed strategies within the organizations should incorporate the diversity of the involved parties in order to meet SDGs and the variety of requirements among different sectors. Moreover, traditional paradigms of data management have presented governments as the main sources, managers, and keepers of data. However, big data, particularly social data, are created mainly by community members and private sectors. This shift from a governmental-based to a public-based scheme requires careful management of data. The integration of data from diverse sources requires the collaboration of the stakeholders to form the optimal framework for the access, storage, management, security, and analysis of the data.
Finally, in sustainable development, prediction technologies using artificial intelligence must be developed in conjunction with the development of big data technology in order to accelerate the adoption of the latter. However, these cutting-edge technologies are only possible through the work of experts and data scientists. Citizen data scientists must be able to easily access big data and analysis tools at a low cost, as they stand to benefit more through the use of (affordable) advanced analytics. To gain insights, citizen data scientists should develop and enhance predictive models, as well as deploy more models to supplement existing ones. A critical task for the government agencies involved in SDGs is the investment and development of tools for analyzing large amounts of data and building predictive and decision-making models with the assistance of easy-to-use analytics systems for non-experts. This can help government agencies comprehensively involve big data analytics to meet all SDGs in their respective countries.
Disclosure Statement
Mehrbakhsh Nilashi, Ooi Keng Boon, Garry Tan Wei Han, Binshan Lin, and Rabab Abumalloh have no financial or non-financial disclosures to share for this article.
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