Data-Driven Governance in GST: Leveraging Big Data for Tax Compliance Monitoring

Abstract The introduction of the Goods and Services Tax (GST) has significantly transformed India’s indirect tax administration by establishing a technology-based system for tax reporting, monitoring, and compliance. With the rapid growth of digital transactions through the Goods and Services Tax Network (GSTN), vast datasets are now generated and can be analyzed to support data-driven governance. This study examines how big data analytics can strengthen tax compliance monitoring within the GST framework. The research relies on secondary data collected from government reports, policy documents, academic studies, and statistical publications issued by institutions such as the Ministry of Finance and the Central Board of Indirect Taxes and Customs. The study highlights how digital mechanisms including e-invoicing, e-way bills, and automated return filing produce real-time transactional data that can be analyzed to detect irregularities, limit tax evasion, and improve taxpayer compliance. The findings suggest that integrating big data analytics into GST administration enhances transparency, strengthens risk-based compliance systems, and supports evidence-based decision-making. However, issues related to data privacy, cybersecurity, and technical capacity remain significant challenges. The paper concludes that effective use of big data within the GST ecosystem can substantially improve compliance monitoring and policy development, contributing to stronger digital governance and a more efficient tax administration system. Keywords: e-Governance, Big Data Analytics, GST Compliance, Tax Administration, Data-Driven Governance, Digital Taxation 1. Introduction Digital technologies have reshaped governance systems across the world. Governments are increasingly adopting digital platforms to improve administrative efficiency, transparency, and service delivery. In the field of taxation, digital transformation has enabled authorities to collect, process, and analyze large volumes of financial data, which improves monitoring and enforcement mechanisms. India’s Goods and Services Tax (GST), introduced in 2017, represents one of the most significant tax reforms in the country. It replaced multiple indirect taxes with a unified tax system and introduced a technology-driven framework for tax administration. The Goods and Services Tax Network (GSTN) functions as the central digital infrastructure that supports taxpayer registration, return filing, invoice matching, and compliance monitoring. Because GST operations occur on digital platforms, the system generates large volumes of transactional data. This data creates new opportunities for the use of big data analytics in governance. Through analytical tools, tax authorities can examine transaction patterns, detect inconsistencies, and identify potential cases of tax evasion. The integration of digital technologies such as e-invoicing and the e-way bill system has strengthened the monitoring of supply chains and commercial transactions. These tools allow tax authorities to track goods movement and verify invoice information in real time, making tax administration more transparent and efficient. As governments move toward data-driven governance, the use of big data analytics has become increasingly important in improving policy implementation and compliance monitoring. This study explores how big data analytics contributes to GST compliance monitoring and the broader development of digital tax governance in India. 2. Literature Review Scholars have widely examined the relationship between digital governance and tax administration. Research suggests that technology-based tax systems improve efficiency, transparency, and compliance. Nayyar and Singh (2018) argued that the implementation of GST increased transparency in India’s indirect tax system and reduced cascading taxation. However, they also noted that businesses initially struggled with technological adaptation and compliance costs. Over time, digital mechanisms such as e-way bills and e-invoicing improved monitoring of supply chains and reduced opportunities for tax evasion. The concept of big data analytics has also gained significant attention in governance research. Chen, Chiang, and Storey (2012) describe big data analytics as a powerful approach for analyzing complex datasets to support organizational decision-making. Similarly, Kitchin (2014) emphasizes that big data enables governments to adopt predictive analytics and evidence-based policymaking. In taxation systems, big data analytics allows authorities to analyze millions of transactions to identify suspicious patterns. Gupta and Saini (2020) found that data analytics techniques improve compliance monitoring by identifying inconsistencies between reported and actual transactions. By integrating multiple data sources such as invoices, tax returns, and payment records, authorities can detect fraudulent practices including fake invoicing and input tax credit fraud. Davenport (2014) also highlights that big data analytics strengthens organizational capabilities by enabling data-driven decision-making and improving operational efficiency. The use of advanced technologies such as artificial intelligence, machine learning, and blockchain has further expanded the potential of digital tax systems. These technologies enable governments to automate compliance monitoring processes and reduce human error. Artificial intelligence systems can analyze large datasets to identify patterns associated with tax fraud or non-compliance. According to Janssen and Kuk (2016), integrating big data technologies into government information systems enhances analytical capacity and supports more effective policy implementation. Despite these advantages, researchers also highlight several challenges. One of the most significant concerns involves the protection of taxpayer data. Digital tax systems collect sensitive financial information that must be safeguarded against unauthorized access or misuse. Zuboff (2019) warns that large-scale data systems may pose privacy risks if appropriate regulatory frameworks are not established. Another challenge involves data integration. Tax administration often requires information from multiple government databases that may not be compatible. Janssen and Kuk (2016) note that the absence of standardized data formats and integration frameworks can limit the effective use of big data in governance. In addition, small and medium enterprises (SMEs) may experience difficulties adapting to digital tax systems due to limited technological resources and digital literacy. Scholars therefore emphasize the importance of capacity-building initiatives and user-friendly digital platforms to support taxpayers during the transition to digital systems. Overall, existing literature indicates that big data analytics has significant potential to strengthen tax administration and compliance monitoring. However, successful implementation requires strong institutional frameworks, data protection policies, and technological infrastructure. 3. Methodology This research adopts a qualitative approach based on secondary data analysis. The objective is to examine how big data analytics contributes to GST compliance monitoring and digital tax governance. Data Sources Secondary data for the study was collected from multiple credible sources, including: Government reports issued by the Ministry of Finance Policy documents and circulars from the Central Board of Indirect Taxes and Customs Reports published by the Organisation for Economic Co-operation and Development (OECD) Academic journals, books, and conference papers Industry reports and statistical publications Method of Analysis The collected data was analyzed using descriptive and thematic analysis techniques. The analysis focused on identifying key themes related to: Digital tax governance Big data analytics in taxation GST compliance monitoring mechanisms Challenges and opportunities in data-driven governance The findings from different sources were synthesized to understand how big data analytics supports compliance monitoring under the GST framework. 4. Discussion The findings indicate that the implementation of GST has significantly accelerated the digitalization of tax administration in India. The GST regime introduced a unified and technology-driven system that integrates multiple digital platforms to support tax reporting, monitoring, and compliance (Garg, 2017; Nayyar OECD, 2019). One major outcome of the GST system is the creation of a comprehensive digital ecosystem for tracking business transactions. Digital tools such as e-invoicing, the e-way bill system, and automated return filing enable real-time recording of transactions across supply chains (Awasthi Kitchin, 2014). Big data analytics also enables risk-based compliance management. T