ABSTRACT
In an era shaped by data-driven methodologies, this study delves into the ever-evolving landscape of policy decision-making within local governmental structures. Focused on the influence of big data analytics, this research endeavors to elucidate the efficacy and implications of leveraging vast data resources in guiding policy formation and governance. Employing a mixed methodological approach, the study undertakes a comprehensive analysis, amalgamating both qualitative and quantitative methodologies to capture a holistic understanding of the impact of big data analytics on policy decisions at the local government level. The qualitative phase of the research involves in-depth interviews, focusing on key stakeholders and decision-makers within local governmental bodies. These interviews seek to uncover nuanced insights into the integration of big data analytics, exploring the challenges, opportunities, and perceived impact on policy-making processes. Concurrently, the quantitative arm of the study engages in statistical analyses, assessing the correlation between the adoption of big data analytics and the outcomes of policy decisions. Through the analysis of empirical data, the research aims to quantify and qualify the direct and indirect effects of big data analytics on policy efficacy within local governance structures. Preliminary findings suggest that the utilization of big data analytics in local governance significantly influences policy decision-making processes, enabling more informed, precise, and responsive policy formation. The study underscores the vital role of data-driven insights in identifying societal needs, forecasting trends, and optimizing resource allocation, thereby enhancing the quality and effectiveness of policy decisions.
Keywords: Data Analytics, Policy-Decision Making, Local Government, Mixed-Methods
INTRODUCTION
Background of the Study
The utilization of data in policymaking processes has gained unprecedented significance in recent times, evident through various illustrative instances. One notable area where data-driven approaches have proven vital is in infectious disease policymaking. The ongoing COVID-19 pandemic has witnessed a surge in the development and utilization of mathematical models. These models, exemplified by the work of James et al. (2021), have played a crucial role in tracking and projecting the disease's spread. Major policy decisions have hinged on the outcomes of these studies, underscoring the pivotal role of data in shaping public health strategies. Additionally, the creation of public data panels has emerged as a promising avenue for policymaking enhancement. The recognition of data sets as valuable assets for policymaking, public service provision, and the greater societal good has led to the integration of diverse datasets. By linking large-scale datasets across thematic areas, researchers can discern patterns and identify areas of concern within populations. Notably, Northern Ireland stands as an exception among the UK's devolved jurisdictions, lacking a public panel to address critical public data issues, including data legislation and its secondary usage (Nelson & Burns, 2022). This highlights the need for comprehensive approaches to data governance in policymaking. Furthermore, evidence-informed policymaking (STP) has garnered significant attention globally.
Policymakers across the world are increasingly focused on basing decisions on the best available research evidence. This approach aims to enhance policy efficiency and effectiveness. Notably, the integration of evidence-informed policymaking equips policymakers and policy analysts with the tools to engage effectively with researchers. By timely and effectively utilizing pertinent evidence, this approach facilitates well-informed decision-making processes, potentially leading to the formulation of more effective and efficient health policies within healthcare systems (Doshmangir, 2019). In the realm of information technology, the correlation between internet use and societal trends has come to the fore. For instance, recent research in China has revealed a significant association between internet use and rising divorce rates. This discovery underscores the need to delve into the mechanisms underlying information access through the internet and its impact on societal welfare. Consequently, it emphasizes the imperative for socially responsible policymaking that takes into account the intricate dynamics of internet usage (Liu et al., 2023). This intersection of technology and societal behavior necessitates nuanced policy considerations, underlining the importance of continually integrating data-driven insights into policymaking frameworks.
Furthermore, Big Data Analytics (BDA) serves as a crucial method in contemporary decision-making processes. It involves the meticulous examination of extensive and intricate datasets with the aim of unveiling concealed patterns, unknown correlations, market trends, and customer preferences, providing valuable business insights. This approach is fundamental because it empowers organizations to make informed, data-driven decisions rooted in concrete facts and analyses rather than mere intuition or speculation. BDA finds applicability across diverse fields, ranging from social network analysis and genomics to healthcare, energy management, and computer vision, as noted in Services (2015). Through BDA techniques like regression, classification, clustering, and dimensionality reduction, organizations can delve into the complexities of large datasets. Importantly, discussions within the realm of BDA also revolve around strategies for scaling these methods. This scalability is crucial as it allows the analysis of exceptionally large datasets, enhancing the depth and accuracy of the insights derived.
Moreover, BDA's impact extends beyond the realm of business analytics. It plays a pivotal role in the development of smart cities, smart healthcare systems, smart governance structures, and intelligent homes and buildings, as well as in advancing smart mobility, transportation, and factories. These applications underscore the transformative potential of BDA, enabling the realization of innovative, data-driven solutions in various domains (Sharma et al., 2022).
On the other hand, local governments in the Philippines have been playing an increasingly important role in addressing complex societal challenges. Recent studies have shown that local governments can exercise legislative and regulatory powers, such as planning powers, to advance broader goals of public health and wellbeing, as well as support the strengthening and expansion of healthy and sustainable food systems (Rose et al., 2022). Additionally, local governments have been required to have a strategic performance management system (SPMS) as a central element of responsible and effective governance since 2012 (Gabriel & Villaroman, 2019). However, there are also challenges that local governments face, such as opposition to mining and the need for effective communication strategies (Silvallana & Hagling, 2023). Despite these challenges, local governments in the Philippines have been trying to address public management challenges and promote community participation in local governance (Saldaen et al., 2021; Ishii, 2016).
Problem Statement
Local governments worldwide increasingly rely on data-driven approaches for policymaking, particularly in public health crises like the COVID-19 pandemic. However, in the Philippines, despite the challenges faced by local governments, there is a glaring gap in harnessing Big Data Analytics (BDA) to inform policy decisions effectively. Current research needs more depth in understanding how BDA can address specific challenges faced by local governments, hindering the development of informed, data-driven strategies. This research aims to bridge this gap by exploring the impact of BDA on policy decision-making within Philippine local governments particularly in Quezon City, enabling the formulation of proactive, data-driven policies aligned with societal needs.
see PDF attachment for more information