From big data to big performance – exploring the potential of big data for enhancing public organizations’ performance : a systematic literature review

This article examines the possibilities for increasing organizational performance in the public sector using Big Data by conducting a systematic literature review. It includes the results of 36 scientific articles published between January 2012 and July 2019. The results show a tendency to explain the relationship between big data and organizational performance through the Resource-Based View of the Firm or the Dynamic Capabilities View, arguing that perfor-mance improvement in an organization stems from unique capabilities. In addition, the results show that Big Data performance improvement is influenced by better organizational decision making. Finally, it identifies three dimensions that seem to play a role in this process: the human dimension, the organizational dimension, and the data dimension. From these findings, implications for both practice and theory are derived

Preferensi Pemilih Muslim Milenial pada Pemilihan Presiden-Wakil Presiden 2019

This paper discusses the issue of the tendency to vote of millennial Muslim voters in presidential-vice presidential elections 2019. This issue is answered through three questions. Applying mix-methods of quantitative and qualitative, the data was obtained through surveys and interviews as well as literature studies. The results were analyzed using a descriptive-analytical analysis. These results of this research show three important things. First, millennial Muslim voters in the presidential-vice presidential election 2019 tend to choose civilians as potential leaders. Civilians are considered capable of overcoming problems and can advance Indonesia in the future. Second, there are variations in the reasons for voters voting their candidates. In addition to performance factors, firmness and competency factors are the drivers for voters to make choices regarding contending candidates. Third, differences in political choices have an impact on polarization among Muslim communities. Different choices lead to new groupings in Muslim societies and even tend to emerge hates and dislikes from each other

UNLOCKING THE POWER OF BIG DATA: DIGITAL TRANSFORMATION OF PUBLIC POLICY IN DPRD DKI JAKARTA

This research delves into the prospects and obstacles associated with utilizing large-scale data in developing public policies within the Indonesian context. Integrating big data technology holds promise as a tool for government agencies aiming to refine the public policy formulation process, ultimately providing enhanced services to the populace. Despite its inherent complexity and costliness, incorporating big data offers the government a means to furnish the most up-to-date, precise, and granular information pertinent to developmental issues. For instance, in the agricultural sector, big data can offer an intricate understanding of the diverse requirements of farmers in distinct regions, such as the differentiation between rice varieties sought by farmers in Kalimantan compared to those in Java. Furthermore, the expansive reservoirs of geophysical and meteorological big data hold the capacity to significantly bolster the government's initiatives concerning natural disaster mitigation policies. Nonetheless, the practical integration of big data still needs to be improved by a dearth of comprehensive regulations governing its application. Additionally, the perils of recurrent data breaches in the Indonesian context pose a formidable challenge. This comprehensive analysis concludes that using big data in policy formulation within Indonesia encounters substantial hurdles that threaten to overshadow the potential advantages this technology could offer in enhancing public policy crafting

Ciencia de datos en la evaluación del impacto de las políticas públicas: Una revisión de la literatura

Ensuring that public policies deliver results that improve people's quality of life has always been a central concern of public decision-makers, especially with budgetary restrictions such as the ones most countries are going through or will go through in the post-pandemic context. On the other hand, data science, artificial intelligence, open data, and, generally, technologies driven by large amounts of data are gaining ground in public management. This article aims to determine the current state of research on the interaction between these two disciplines, studying the application of data science to the impact assessment of public policies and identifying research gaps. A systematic literature review revealed that the proposed object of study has not been at the center of academic research. What has been investigated is the complete cycle of policy development or public management in general. The study also verified that the interaction between public affairs and data science is still an emerging field, and, in the opinion of many academics, what is lacking is research with a holistic vision that sees beyond the eminently technical.Lograr que las políticas públicas realmente logren resultados que mejoren la calidad de vida de las personas es un tema que siempre preocupa a los decisores públicos; más aún en un contexto de restricciones presupuestales como el que la mayoría de países atraviesa o atravesará en la situación de pospandemia. Por otro lado, la ciencia de datos, la inteligencia artificial, los datos abiertos y, en general, las tecnologías impulsadas por las grandes cantidades de datos cada día van ganando terreno en el ámbito de la gestión pública. El presente artículo tiene por objetivo conocer el estado actual de las investigaciones acerca de la interacción entre estos dos ámbitos, tomando como objeto de estudio la aplicación de la ciencia de datos a la evaluación del impacto de las políticas públicas, a fin de identificar los vacíos en la investigación existente. Luego de una revisión sistemática de la literatura, se encontró que el objeto de estudio propuesto no ha estado en el centro de la investigación académica. Lo que se ha investigado es el ciclo completo de desarrollo de políticas o la gestión pública en general. Se verificó también que esta interacción entre la cosa pública y la ciencia de datos aún es un campo emergente y, en opinión de muchos académicos, hacen falta investigaciones con una visión holística y con una mirada que vaya más allá de lo eminentemente técnico

Research themes in big data analytics for policymaking:Insights from a mixed-methods systematic literature review

The use of big data and data analytics are slowly emerging in public policy-making, and there are calls for systematic reviews and research agendas focusing on the impacts that big data and analytics have on policy processes. This paper examines the nascent field of big data and data analytics in public policy by reviewing the literature with bibliometric and qualitative analyses. The study encompassed scientific publications gathered from SCOPUS (N = 538). Nine bibliographically coupled clusters were identified, with the three largest clusters being big data's impact on the policy cycle, data-based decision-making, and productivity. Through the qualitative coding of the literature, our study highlights the core of the discussions and proposes a research agenda for further studies.publishedVersionPeer reviewe

Managing Algorithms for Public Value

Public organisations increasingly rely on machine learning algorithms in performing many of their core activities. It is therefore important to consider how algorithms are transforming the public sector. This article aims to clarify this by assessing algorithms from a public value perspective. Based on a discussion of the literature, it is demonstrated that algorithms are generally expected to strengthen organisational performance on a first cluster of values related to the ability to be effective and efficient (sigma values). At the same time, the use of algorithms is linked to negatively affect a second cluster of values that involves fairness and transparency (theta values). In the current academic debate, little attention is given to an important third cluster of values: the ability of organisations to be adaptive and robust (lambda values). This discussion highlights that algorithms invoke public value opportunities, but also public value risks and trade-offs. This article therefore presents five principles for managing algorithms from a public value perspective

Data science as knowledge creation a framework for synergies between data analysts and domain professionals

The road from data generation to data use is commonly approached as a data-driven, functional process in which domain expertise is integrated as an afterthought. In this contribution we complement this functional view with an institutional view, that takes data analysis and domain professionalism as complementary (yet fallible) knowledge sources. We developed a framework that identifies and amplifies synergies between data analysts and domain professionals instead of taking one of them (i.e. data analytics) at the centre of the analytical process. The framework combines the often-cited CRISP-DM framework with a knowledge creation framework. The resulting framework is used in a data science project at a Dutch inspectorate that seeks to use data for risk-based inspection. The findings show first support of our framework. They also show that whereas more complex models have a higher predictive power, simpler models are sometimes preferred as they have the potential to create more synergies between inspectors and data analyst. Another issue driven by the integrated framework is about who of the involved actors should own the predictive model: data analysts or inspectors

Legitimacy of Algorithmic Decision-Making: Six Threats and the Need for a Calibrated Institutional Response

Algorithmic decision-making in government has emerged rapidly in recent years, leading to a surge in attention for this topic by scholars from various fields, including public administration. Recent studies provide crucial yet fragmented insights on how the use of algorithms to support or fully automate decisions is transforming government. This article ties together these insights by applying the theoretical lenses of government legitimacy and institutional design. We identify how algorithmic decision-making challenges three types of legitimacy—input, throughput, and output—and identify institutional arrangements that can mitigate these threats. We argue that there is no silver bullet to maintain legitimacy of algorithmic government and that a multiplicity of different institutional mechanisms is required, ranging from legal structures and civic participation to closer monitoring of algorithmic systems. We conclude with a framework to guide future research to better understand the implications of institutional design for the legitimacy of algorithmic government

Rationality and politics of algorithms. Will the promise of big data survive the dynamics of public decision making?

Big data promises to transform public decision-making for the better by making it more responsive to actual needs and policy effects. However, much recent work on big data in public decision-making assumes a rational view of decision-making, which has been much criticized in the public administration debate. In this paper, we apply this view, and a more political one, to the context of big data and offer a qualitative study. We question the impact of big data on decision-making, realizing that big data – including its new methods and functions – must inevitably encounter existing political and managerial institutions. By studying two illustrative cases of big data use processes, we explore how these two worlds meet. Specifically, we look at the interaction between data analysts and decision makers. In this we distinguish between a rational view and a political view, and between an information logic and a decision logic. We find that big data provides ample opportunities for both analysts and decision makers to do a better job, but this doesn't necessarily imply better decision-making, because big data also provides opportunities for actors to pursue their own interests. Big data enables both data analysts and decision makers to act as autonomous agents rather than as links in a functional chain. Therefore, big data's impact cannot be interpreted only in terms of its functional promise; it must also be acknowledged as a phenomenon set to impact our policymaking institutions, including their legitimacy

Rationality and politics of algorithms. Will the promise of big data survive the dynamics of public decision making?

Big data promises to transform public decision-making for the better by making it more responsive to actual needs and policy effects. However, much recent work on big data in public decision-making assumes a rational view of decision-making, which has been much criticized in the public administration debate. In this paper, we apply this view, and a more political one, to the context of big data and offer a qualitative study. We question the impact of big data on decision-making, realizing that big data – including its new methods and functions – must inevitably encounter existing political and managerial institutions. By studying two illustrative cases of big data use processes, we explore how these two worlds meet. Specifically, we look at the interaction between data analysts and decision makers. In this we distinguish between a rational view and a political view, and between an information logic and a decision logic. We find that big data provides ample opportunities for both analysts and decision makers to do a better job, but this doesn't necessarily imply better decision-making, because big data also provides opportunities for actors to pursue their own interests. Big data enables both data analysts and decision makers to act as autonomous agents rather than as links in a functional chain. Therefore, big data's impact cannot be interpreted only in terms of its functional promise; it must also be acknowledged as a phenomenon set to impact our policymaking institutions, including their legitimacy