(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Computer Research (IJACR)

ISSN (Print):2249-7277    ISSN (Online):2277-7970
Volume-13 Issue-64 September-2023
Full-Text PDF
Paper Title : The barriers and prospects related to big data analytics implementation in public institutions: a systematic review analysis
Author Name : Matendo Didas
Abstract :

Modern society has always relied on data, which is generated by individuals, businesses, and government entities. This data serves various citizen-centric purposes, including monitoring, weather forecasting, healthcare management, and disease prediction. Technological advancements have expanded the sources of data, allowing it to be produced from any device, anywhere, and in any format. However, the challenge lies in comprehending, managing, and effectively utilizing this vast data resource. Public organizations are known for generating significant amounts of data. The question arises: can this data be integrated with technology-generated data to create societal value? Yet, accessing and integrating data can be complex for public organizations and nonprofits, especially when crossing international borders, due to legal, cultural, and political considerations. Nevertheless, big data applications are making their way into public institutions, and their cumulative impact on big data analytics (BDA) has the potential to provide a competitive advantage for improved public service delivery. Despite the recent attention garnered by BDA, many BDA projects in public institutions fall short of expectations, primarily due to substantial capital investments that make their return on investment questionable. One key reason for these failures is a lack of understanding regarding the challenges and opportunities associated with BDA in the context of public institutions. This article aims to systematically review existing literature to provide comprehensive insights into the prospects and barriers of BDA in public institutions. This review paper employs a systematic literature review analysis (SLRA) to shed light on the application of state-of-the-art BDA barriers and prospects within public institutions. It draws upon existing works that provide perspectives and theoretical constructs while identifying barriers and prospects. The review underscores that BDA holds immense potential for supporting public institutions in harnessing big data for evidence-based public service delivery. While there are numerous potential benefits, including food security, knowledge management, and informed policy-making, among others, the review also highlights critical gaps that need attention to fully realize these merits. This study delves into the use of BDA systems in public institutions, addressing both opportunities and challenges in this context. Based on these findings, recommendations are offered for future directions.

Keywords : Big data analytics (BDA), Big data, Public institutions, Preferred report items for the systematic review and meta-analysis (PRISMA), SLRA.
Cite this article : Didas M. The barriers and prospects related to big data analytics implementation in public institutions: a systematic review analysis. International Journal of Advanced Computer Research. 2023; 13(64):29-54. DOI:10.19101/IJACR.2021.1152071.
References :
[1]Rodionov AA, Fayziev RA, Gulyamov SS. Experience in using big data technology for digitalization of information. In proceedings of the 6th international conference on future networks & distributed systems 2022 (pp. 412-5).
[Crossref] [Google Scholar]
[2]Garg A, Popli R, Sarao BS. Growth of digitization and its impact on big data analytics. In IOP conference series: materials science and engineering 2021 (pp. 1-9). IOP Publishing.
[Crossref] [Google Scholar]
[3]Lampropoulos G. Artificial intelligence, big data, and machine learning in industry 4.0. In encyclopedia of data science and machine learning 2023 (pp. 2101-9). IGI Global.
[Crossref] [Google Scholar]
[4]Molčan M, Čajková A. Knowledge management and local government. In developments in information and knowledge management systems for business applications 2023 (pp. 293-312). Cham: Springer Nature Switzerland.
[Crossref] [Google Scholar]
[5]Momen MN, Ferdous J. Governance in Bangladesh: innovations in delivery of public service. Springer Nature; 2023.
[Google Scholar]
[6]Sahid NZ, Sani MK, Noordin SA, Zaini MK, Baba J. Determinants factors of intention to adopt big data analytics in Malaysian public agencies. Journal of Industrial Engineering and Management. 2021; 14(2):269-93.
[Crossref] [Google Scholar]
[7]Cronemberger F, Gil-garcia JR. Big data and analytics as strategies to generate public value in smart cities: proposing an integrative framework. Setting Foundations for the Creation of Public Value in Smart Cities. 2019:247-67.
[Crossref] [Google Scholar]
[8]Pramanik S, Bandyopadhyay SK. Analysis of big data. In encyclopedia of data science and machine learning 2023 (pp. 97-115). IGI Global.
[Crossref] [Google Scholar]
[9]Supriyadi RS, Abraham I, Giyanto B, Asropi A. Big data in the public sector: systematic literature review and bibliometric analysis. Jurnal Ilmiah Mandala Education. 2023; 9(1):1-5.
[Crossref] [Google Scholar]
[10]Chen J, Tao Y, Wang H, Chen T. Big data based fraud risk management at Alibaba. The Journal of Finance and Data Science. 2015; 1(1):1-10.
[Crossref] [Google Scholar]
[11]Fan S, Lau RY, Zhao JL. Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Research. 2015; 2(1):28-32.
[Crossref] [Google Scholar]
[12]Xiang Z, Schwartz Z, Gerdes JH, Uysal M. What can big data and text analytics tell us about hotel guest experience and satisfaction? International Journal of Hospitality Management. 2015; 44:120-30.
[Crossref] [Google Scholar]
[13]Vassakis K, Petrakis E, Kopanakis I. Big data analytics: applications, prospects and challenges. Mobile Big Data: A Roadmap from Models to Technologies. 2018:3-20.
[Crossref] [Google Scholar]
[14]Gamage P. New development: leveraging ‘big data’analytics in the public sector. Public Money & Management. 2016; 36(5):385-90.
[Crossref] [Google Scholar]
[15]Moorthy J, Lahiri R, Biswas N, Sanyal D, Ranjan J, Nanath K, et al. Big data: prospects and challenges. Vikalpa. 2015; 40(1):74-96.
[Crossref] [Google Scholar]
[16]Surya L. An exploratory study of AI and big data, and its future in the United States. International Journal of Creative Research Thoughts. 2015; 3(2):991-5.
[Google Scholar]
[17]Adusumalli HP, Pasupuleti MB. Applications and practices of big data for development. Asian Business Review. 2017; 7(3):111-6.
[Crossref] [Google Scholar]
[18]Mittal P. Big data and analytics: a data management perspective in public administration. International Journal of Big Data Management. 2020; 1(2):152-65.
[Crossref] [Google Scholar]
[19]Naeem M, Jamal T, Diaz-martinez J, Butt SA, Montesano N, Tariq MI, et al. Trends and future perspective challenges in big data. In advances in intelligent data analysis and applications: proceeding of the sixth euro-China conference on intelligent data analysis and applications 2022 (pp. 309-25). Springer Singapore.
[Crossref] [Google Scholar]
[20]Väyrynen H, Helander N, Jalonen H. Public Innovation and Digital Transformation. Taylor & Francis; 2023.
[Google Scholar]
[21]Hossain E, Khan I, Un-noor F, Sikander SS, Sunny MS. Application of big data and machine learning in smart grid, and associated security concerns: a review. IEEE Access. 2019; 7:13960-88.
[Crossref] [Google Scholar]
[22]Shi Y. Advances in big data analytics. Adv Big Data Anal. Springer Nature; 2022.
[Google Scholar]
[23]Emmanuel I, Stanier C. Defining big data. In proceedings of the international conference on big data and advanced wireless technologies 2016 (pp. 1-6). ACM.
[Google Scholar]
[24]Grover V, Chiang RH, Liang TP, Zhang D. Creating strategic business value from big data analytics: a research framework. Journal of Management Information Systems. 2018; 35(2):388-423.
[Crossref] [Google Scholar]
[25]Kalmbach P. o ‘zapft is: Tap Your Networking Algorithm‘s Big Data!. 2017.
[Google Scholar]
[26]Van DHG, Klievink AJ, Arnaboldi M, Meijer AJ. Rationality and politics of algorithms. will the promise of big data survive the dynamics of public decision making? Government Information Quarterly. 2019; 36(1):27-38.
[Crossref] [Google Scholar]
[27]Van DH, Arnaboldi M, Dew BH, Klievink B. Will the promise of big data survive the dynamics of public decision making? the need for checks and balances. In EGPA annual conference, European group for public administration 2016.
[Google Scholar]
[28]Marr B. Data strategy: how to profit from a world of big data, analytics and artificial intelligence. Kogan Page Publishers; 2021.
[Google Scholar]
[29]Talaoui Y, Kohtamäki M, Ranta M, Paroutis S. Recovering the divide: a review of the big data analytics-strategy relationship. Long Range Planning. 2023.
[Crossref] [Google Scholar]
[30]Visco G, Plattner SH, Fortini P, Sammartino M. A multivariate approach for a comparison of big data matrices. Case study: thermo-hygrometric monitoring inside the Carcer Tullianum (Rome) in the absence and in the presence of visitors. Environmental Science and Pollution Research. 2017:13990-4004.
[Crossref] [Google Scholar]
[31]Gul R, Leong K, Mubashar A, Al-faryan MA, Sung A. The empirical nexus between data-driven decision-making and productivity: evidence from Pakistan’s banking sector. Cogent Business & Management. 2023; 10(1):1-17.
[Google Scholar]
[32]Gärtner B, Hiebl MR. Issues with big data. In the Routledge companion to accounting information systems 2017 (pp. 161-72). Routledge.
[Google Scholar]
[33]Lohr SL, Raghunathan TE. Combining survey data with other data sources. 2017:293-312.
[Crossref] [Google Scholar]
[34]Hartmann T, Moawad A, Fouquet F, Nain G, Klein J, Traon YL, et al. Model-driven analytics: connecting data, domain knowledge, and learning. ArXiv preprint arXiv:1704.01320. 2017.
[Crossref] [Google Scholar]
[35]Blenk A, Kalmbach P, Kellerer W, Schmid S. Ozapft is: tap your network algorithms big data. In proceedings of the workshop on big data analytics and machine learning for data communication networks 2017 (pp. 19-24).
[Crossref] [Google Scholar]
[36]Merhi MI, Bregu K. Effective and efficient usage of big data analytics in public sector. Transforming Government: People, Process and Policy. 2020; 14(4):605-22.
[Crossref] [Google Scholar]
[37]Rogge N, Agasisti T, De WK. Big data and the measurement of public organizations’ performance and efficiency: the state-of-the-art. Public Policy and Administration. 2017; 32(4):263-81.
[Crossref] [Google Scholar]
[38]Desouza KC, Jacob B. Big data in the public sector: lessons for practitioners and scholars. Administration & Society. 2017; 49(7):1043-64.
[Crossref] [Google Scholar]
[39]Visvizi A, Troisi O, Grimaldi M. Mapping and conceptualizing big data and its value across issues and domains. In big data and decision-making: applications and uses in the public and private sector 2023 (pp. 15-25). Emerald Publishing Limited.
[Crossref] [Google Scholar]
[40]Sarkis-onofre R, Catalá-lópez F, Aromataris E, Lockwood C. How to properly use the PRISMA statement. Systematic Reviews. 2021; 10(1):1-3.
[Crossref] [Google Scholar]
[41]Nazari E, Afkanpour M, Tabesh H. Big data from A to Z. Frontiers in Health Informatics. 2019; 8(1):1-5.
[Crossref] [Google Scholar]
[42]Cartledge C. How Many VS are there in big data. 2006:1-4.
[Google Scholar]
[43]Khanna D, Jindal N, Singh H, Rana PS. Applications and challenges in healthcare big data: a strategic review. Current Medical Imaging. 2023; 19(1):27-36.
[Google Scholar]
[44]Mcdermott S, Turk MA. What are the implications of the big data paradigm shift for disability and health? Disability and Health Journal. 2015; 3(8):303-4.
[Crossref] [Google Scholar]
[45]Reinsel D, Gantz J, Rydning J. Data age 2025: the evolution of data to life-critical. From Seagate. Framingham, MA, US: International Data Corporation. 2017.
[Google Scholar]
[46]Athey S. Beyond prediction: using big data for policy problems. Science. 2017; 355(6324):483-5.
[Google Scholar]
[47]Gill SS, Chana I, Buyya R. IoT based agriculture as a cloud and big data service: the beginning of digital India. Journal of Organizational and End User Computing (JOEUC). 2017; 29(4):1-23.
[Crossref] [Google Scholar]
[48]De MA, Greco M, Grimaldi M. A formal definition of big data based on its essential features. Library Review. 2016; 65(3):122-35.
[Crossref] [Google Scholar]
[49]Galetsi P, Katsaliaki K, Kumar S. Big data analytics in health sector: theoretical framework, techniques and prospects. International Journal of Information Management. 2020; 50:206-16.
[Crossref] [Google Scholar]
[50]Mergel I, Rethemeyer K, Isett KR. What does big data mean to public affairs research? understanding the methodological and analytical challenges. Impact of Social Sciences Blog. 2016.
[Google Scholar]
[51]Khanra S, Dhir A, Islam AN, Mäntymäki M. Big data analytics in healthcare: a systematic literature review. Enterprise Information Systems. 2020; 14(7):878-912.
[Crossref] [Google Scholar]
[52]Bahri S, Zoghlami N, Abed M, Tavares JM. Big data for healthcare: a survey. IEEE Access. 2018; 7:7397-408.
[Crossref] [Google Scholar]
[53]Khan A, Tao M. Knowledge absorption capacitys efficacy to enhance innovation performance through big data analytics and digital platform capability. Journal of Innovation & Knowledge. 2022; 7(3):1-13.
[Crossref] [Google Scholar]
[54]Wang Y, Kung L, Byrd TA. Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change. 2018; 126:3-13.
[Crossref] [Google Scholar]
[55]Watson HJ. Update tutorial: big data analytics: concepts, technology, and applications. Communications of the Association for Information Systems. 2019; 44(1):1-14.
[Crossref] [Google Scholar]
[56]Statistics UB. Statistical abstract. Kampala: Uganda Bureau of Statistics. 2013.
[Google Scholar]
[57]Mishra D, Luo Z, Hazen B, Hassini E, Foropon C. Organizational capabilities that enable big data and predictive analytics diffusion and organizational performance: a resource-based perspective. Management Decision. 2019; 57(8):1734-55.
[Crossref] [Google Scholar]
[58]Galetsi P, Katsaliaki K. A review of the literature on big data analytics in healthcare. Journal of the Operational Research Society. 2020; 71(10):1511-29.
[Crossref] [Google Scholar]
[59]Kamble SS, Gunasekaran A, Goswami M, Manda J. A systematic perspective on the applications of big data analytics in healthcare management. International Journal of Healthcare Management. 2018; 12(3):226-40.
[Crossref] [Google Scholar]
[60]Boutabba MA, Lardic S. EU emissions trading scheme, competitiveness and carbon leakage: new evidence from cement and steel industries. Annals of Operations Research. 2017; 255:47-61.
[Crossref] [Google Scholar]
[61]Ozminkowski RJ, Wells TS, Hawkins K, Bhattarai GR, Martel CW, Yeh CS. Big data, little data, and care coordination for medicare beneficiaries with medigap coverage. Big Data. 2015; 3(2):114-25.
[Crossref] [Google Scholar]
[62]Amirian P, Van LF, Lang T, Thomas A, Peeling R, Basiri A, et al. Using big data analytics to extract disease surveillance information from point of care diagnostic machines. Pervasive and Mobile Computing. 2017; 42:470-86.
[Crossref] [Google Scholar]
[63]Singh RK, Agrawal S, Sahu A, Kazancoglu Y. Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda. The TQM Journal. 2023; 35(1):262-91.
[Crossref] [Google Scholar]
[64]Kalid N, Zaidan AA, Zaidan BB, Salman OH, Hashim M, Muzammil HJ. Based real time remote health monitoring systems: A review on patients prioritization and related big data using body sensors information and communication technology. Journal of Medical Systems. 2018; 42:1-30.
[Crossref] [Google Scholar]
[65]Prasser F, Spengler H, Bild R, Eicher J, Kuhn KA. Privacy-enhancing ETL-processes for biomedical data. International Journal of Medical Informatics. 2019; 126:72-81.
[Crossref] [Google Scholar]
[66]Harerimana G, Jang B, Kim JW, Park HK. Health big data analytics: a technology survey. IEEE Access. 2018; 6:65661-78.
[Crossref] [Google Scholar]
[67]Zhang R, Simon G, Yu F. Advancing Alzheimers research: a review of big data promises. International Journal of Medical Informatics. 2017; 106:48-56.
[Crossref] [Google Scholar]
[68]Alharthi A, Krotov V, Bowman M. Addressing barriers to big data. Business Horizons. 2017; 60(3):285-92.
[Crossref] [Google Scholar]
[69]Amalina F, Hashem IA, Azizul ZH, Fong AT, Firdaus A, Imran M, et al. Blending big data analytics: review on challenges and a recent study. IEEE Access. 2019; 8:3629-45.
[Crossref] [Google Scholar]
[70]Zhuang YT, Wu F, Chen C, Pan YH. Challenges and opportunities: from big data to knowledge in AI 2.0. Frontiers of Information Technology & Electronic Engineering. 2017; 18:3-14.
[Crossref] [Google Scholar]
[71]Sáez C, García-gómez JM. Kinematics of big biomedical data to characterize temporal variability and seasonality of data repositories: functional data analysis of data temporal evolution over non-parametric statistical manifolds. International Journal of Medical Informatics. 2018; 119:109-24.
[Crossref] [Google Scholar]
[72]Coyne EM, Coyne JG, Walker KB. Big Data information governance by accountants. International Journal of Accounting & Information Management. 2018; 26(1):153-70.
[Crossref] [Google Scholar]
[73]Vitale G, Cupertino S, Riccaboni A. Big data and management control systems change: the case of an agricultural SME. Journal of Management Control. 2020; 31:123-52.
[Crossref] [Google Scholar]
[74]Boell SK, Cecez-kecmanovic D. On being ‘systematic’in literature reviews in IS. Journal of Information Technology. 2015; 30(2):161-73.
[Crossref] [Google Scholar]
[75]Paré G, Kitsiou S. Methods for literature reviews. In Handbook of eHealth Evaluation: an evidence-based approach [Internet] 2017. University of Victoria.
[Google Scholar]
[76]Paul J, Barari M. Meta‐analysis and traditional systematic literature reviews-What, why, when, where, and how? Psychology & Marketing. 2022; 39(6):1099-115.
[Crossref] [Google Scholar]
[77]Selçuk AA. A guide for systematic reviews: PRISMA. Turkish Archives of Otorhinolaryngology. 2019; 57(1):57-8.
[Google Scholar]
[78]Zainal NZ, Hussin H, Nazri MN. Big data initiatives by governments--issues and challenges: a review. In 6th international conference on information and communication technology for the Muslim world 2016 (pp. 304-9). IEEE.
[Crossref] [Google Scholar]
[79]Al-sai ZA, Abdullah R. Big data impacts and challenges: a review. In Jordan international joint conference on electrical engineering and information technology 2019 (pp. 150-5). IEEE.
[Crossref] [Google Scholar]
[80]Kamilaris A, Kartakoullis A, Prenafeta-boldú FX. A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture. 2017; 143:23-37.
[Crossref] [Google Scholar]
[81]Desai PV. A survey on big data applications and challenges. In second international conference on inventive communication and computational technologies 2018 (pp. 737-40). IEEE.
[Crossref] [Google Scholar]
[82]Mureddu F, Schmeling J, Kanellou E. Research challenges for the use of big data in policy-making. Transforming Government: People, Process and Policy. 2020; 14(4):593-604.
[Crossref] [Google Scholar]
[83]Praharaj S. Development challenges for big data command and control centres for smart cities in India. Data-driven Multivalence in the Built Environment. 2020:75-90.
[Crossref] [Google Scholar]
[84]Cannataci J, Falce V, Pollicino O. Legal challenges of big data. Edward Elgar Publishing; 2020.
[Google Scholar]
[85]Tse D, Chow CK, Ly TP, Tong CY, Tam KW. The challenges of big data governance in healthcare. In 17th IEEE international conference on trust, security and privacy in computing and communications/12th IEEE international conference on big data science and engineering (TrustCom/BigDataSE) 2018 (pp. 1632-6). IEEE.
[Crossref] [Google Scholar]
[86]Pradhan P, Shakya S. Big data challenges for e-Government services in Nepal. Journal of the Institute of Engineering. 2018; 14(1):216-22.
[Crossref] [Google Scholar]
[87]Albqowr A, Alsharairi M, Alsoussi A. Big data analytics in supply chain management: a systematic literature review. VINE Journal of Information and Knowledge Management Systems. 2022.
[Crossref] [Google Scholar]
[88]Shastri A, Deshpande M. A review of big data and its applications in healthcare and public sector. Big Data Analytics in Healthcare. 2020:55-66.
[Crossref] [Google Scholar]
[89]Younas M. Research challenges of big data. Service Oriented Computing and Applications. 2019; 13:105-7.
[Google Scholar]
[90]Kondraganti A, Narayanamurthy G, Sharifi H. A systematic literature review on the use of big data analytics in humanitarian and disaster operations. Annals of Operations Research. 2022; 21:1-38.
[Google Scholar]
[91]Eachempati P, Srivastava PR. Systematic literature review of big data analytics. In proceedings of the SIGMIS conference on computers and people research 2017 (pp. 177-8). ACM.
[Google Scholar]
[92]Candra S, Joartha JJ, Moniaga JV, Hidayat MF, Jabar BA. A systematic literature review of big data analytics challenges in cloud computing. In 2nd international conference on electronic and electrical engineering and intelligent system 2022 (pp. 246-51). IEEE.
[Crossref] [Google Scholar]
[93]Pappas IO, Mikalef P, Giannakos MN, Krogstie J, Lekakos G. Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. Information Systems and e-business Management. 2018:479-91.
[Crossref] [Google Scholar]
[94]Kolajo T, Daramola O, Adebiyi A. Big data stream analysis: a systematic literature review. Journal of Big Data. 2019; 6(1):1-30.
[Crossref] [Google Scholar]
[95]Mikalef P, Pappas IO, Krogstie J, Giannakos M. Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management. 2018; 16:547-78.
[Crossref] [Google Scholar]
[96]Vanani IR, Majidian S. Literature review on big data analytics methods. Social Media and Machine Learning; 2019.
[Google Scholar]
[97]Aggarwal AK. Opportunities and challenges of big data in public sector. Web Services: Concepts, Methodologies, Tools, and Applications. 2019:1749-61.
[Crossref] [Google Scholar]
[98]Morabito V, Morabito V. Big data and analytics for government innovation. Big Data and Analytics: Strategic and Organizational Impacts. 2015:23-45.
[Crossref] [Google Scholar]
[99]Batko K, Ślęzak A. The use of big data analytics in healthcare. Journal of Big Data. 2022; 9(1):1-24.
[Crossref] [Google Scholar]
[100]Verma S. Sentiment analysis of public services for smart society: literature review and future research directions. Government Information Quarterly. 2022; 39(3):101708.
[Crossref] [Google Scholar]
[101]Rafiq F, Awan MJ, Yasin A, Nobanee H, Zain AM, Bahaj SA. Privacy prevention of big data applications: a systematic literature review. SAGE Open. 2022; 12(2).1582440221096445.
[102]Lazarevska ZB, Tocev T, Dionisijev I. How to improve performance in public sector auditing through the power of big data and data analytics? - the case of the republic of north macedonia. Journal of Accounting, Finance and Auditing Studies. 2022; 8(3):187-209.
[Crossref] [Google Scholar]
[103]Jin W. Challenges and innovative countermeasures faced by public administration in the context of big data and internet of things. Mathematical Problems in Engineering. 2022; 2022:1-10.
[Crossref] [Google Scholar]
[104]Barham H, Daim T. The use of readiness assessment for big data projects. Sustainable Cities and Society. 2020; 60:102233.
[Crossref] [Google Scholar]
[105]Gandomi AH, Chen F, Abualigah L. Machine learning technologies for big data analytics. Electronics. 2022; 11(3):421.
[Crossref] [Google Scholar]
[106]Kaufmann M. Big data management canvas: a reference model for value creation from data. Big Data and Cognitive Computing. 2019; 3(1):1-18.
[Crossref] [Google Scholar]
[107]Lee I. Big data: dimensions, evolution, impacts, and challenges. Business Horizons. 2017; 60(3):293-303.
[Crossref] [Google Scholar]
[108]Williamson B. Governing through infrastructural control: artificial intelligence and cloud computing in the data-intensive state. The SAGE Handbook of Digital Society. Thousand Oaks, CA: SAGE. 2023.
[Google Scholar]
[109]Hagsten E. ICT infrastructure in firms and online sales. Electronic Commerce Research. 2022:1-20.
[Google Scholar]
[110]Eldow A, Alsharida RA, Hammood M, Shakir M, Malik SI, Muttar AK, et al. Information communication technology infrastructure in Sudanese Governmental Universities. Recent Advances in Intelligent Systems and Smart Applications. 2021:363-75.
[Crossref] [Google Scholar]
[111]Shin DH, Choi MJ. Ecological views of big data: perspectives and issues. Telematics and Informatics. 2015; 32(2):311-20.
[Google Scholar]
[112]Anna K, Nikolay K. Survey on big data analytics in public sector of Russian federation. Procedia Computer Science. 2015; 55:905-11.
[Crossref] [Google Scholar]
[113]Özköse H, Arı ES, Gencer C. Yesterday, today and tomorrow of big data. Procedia-Social and Behavioral Sciences. 2015; 195:1042-50.
[Crossref] [Google Scholar]
[114]Ullrich A. Opportunities and challenges of big data and predictive analytics for achieving the UN’s SDGs. Pacific Asia Conference on Information Systems. 2022:1-17.
[Google Scholar]
[115]Soomro K, Bhutta MN, Khan Z, Tahir MA. Smart city big data analytics: an advanced review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2019; 9(5):e1319.
[Crossref] [Google Scholar]
[116]Raad E, Al BB, Chbeir R. Preventing sensitive relationships disclosure for better social media preservation. International Journal of Information Security. 2016; 15:173-94.
[Crossref] [Google Scholar]
[117]Malomane R, Musonda I, Okoro CS. The opportunities and challenges associated with the implementation of fourth industrial revolution technologies to manage health and safety. International Journal of Environmental Research and Public Health. 2022; 19(2):846.
[Crossref] [Google Scholar]
[118]Pandey R, Dhoundiyal M. Quantitative evaluation of big data categorical variables through R. Procedia Computer Science. 2015; 46:582-8.
[Crossref] [Google Scholar]
[119]Sureephong P, Komanee P, Trongpanyachot C. Data sharing and electronic medical record privacy protection of out-patient-department using blockchain. In 25th international symposium on wireless personal multimedia communications 2022 (pp. 303-7). IEEE.
[Crossref] [Google Scholar]
[120]Rossi R, Hirama K. Characterizing big data management. arXiv preprint arXiv:2201.05929. 2022.
[Crossref] [Google Scholar]
[121]Moreno A, Molano-pulido J, Gomez-morantes JE, Gonzalez RA. ADACOP: a big data platform for open government data. In proceedings of the 15th international conference on theory and practice of electronic governance 2022 (pp. 369-75).
[Crossref] [Google Scholar]
[122]Da STL, Magalhães RP, Brilhante IR, De MJA, Araújo D, Rego PA, et al. Big data analytics technologies and platforms: a brief review. LADaS@ VLDB. 2018:25-32.
[Google Scholar]
[123]Liu SM, Yuan Q. The evolution of information and communication technology in public administration. Public Administration and Development. 2015; 35(2):140-51.
[Google Scholar]
[124]Biswas R. Outlining big data analytics in health sector with special reference to Covid-19. Wireless Personal Communications. 2022; 124(3):2097-108.
[Crossref] [Google Scholar]
[125]Coulthart S, Riccucci R. Putting big data to work in government: the case of the United States border patrol. Public Administration Review. 2022; 82(2):280-9.
[Google Scholar]
[126]Hassenstein MJ, Vanella P. Data quality—concepts and problems. Encyclopedia. 2022; 2(1):498-510.
[Crossref] [Google Scholar]
[127]Spangenberg N, Wilke M, Franczyk B. A big data architecture for intra-surgical remaining time predictions. Procedia Computer Science. 2017; 113:310-7.
[Crossref] [Google Scholar]
[128]Manson SM. Big Data and Human-Environment Systems. Cambridge University Press; 2023.
[Google Scholar]
[129]Weibl J, Hess T. Success or failure of big data: Insights of managerial challenges from a technology assimilation perspective. Proceedings of the Multikonferenz Wirtschaftsinformatik (MKWI). 2018:12-59.
[Google Scholar]
[130]Munné R. Big data in the public sector. New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe; 2016:195-208.
[Google Scholar]
[131]Hardy K, Maurushat A. Opening up government data for Big Data analysis and public benefit. Computer Law & Security Review. 2017; 33(1):30-7.
[Crossref] [Google Scholar]
[132]Mavriki P, Karyda M. Big data analytics in e-government and e-democracy applications: privacy threats, implications and mitigation. International Journal of Electronic Governance. 2022; 14(1-2):58-82.
[Crossref] [Google Scholar]
[133]Abouelmehdi K, Beni-hessane A, Khaloufi H. Big healthcare data: preserving security and privacy. Journal of Big Data. 2018; 5(1):1-8.
[Crossref] [Google Scholar]
[134]Zulkarnain N, Anshari M, Hamdan M, Fithriyah M. Big data in business and ethical challenges. In international conference on information management and technology 2021 (pp. 298-303). IEEE.
[Crossref] [Google Scholar]
[135]Gumbus A, Grodzinsky F. Era of big data: danger of descrimination. ACM SIGCAS Computers and Society. 2016; 45(3):118-25.
[Crossref] [Google Scholar]
[136]Pavone P, Ricci P, Calogero M. New challenges for public value and accountability in the age of big data: a bibliometric analysis. Meditari Accountancy Research. 2023.
[Crossref] [Google Scholar]
[137]Kacem A, Belkaroui R, Jemal D, Ghorbel H, Faiz R, Abid IH. Towards improving e-government services using social media-based citizens profile investigation. In proceedings of the 9th international conference on theory and practice of electronic governance 2016 (pp. 187-90).
[Google Scholar]
[138]Bello-orgaz G, Jung JJ, Camacho D. Social big data: recent achievements and new challenges. Information Fusion. 2016; 28:45-59.
[139]Mohan DA. Big data analytics: recent achievements and new challenges. International Journal of Computer Applications Technology and Research. 2016; 5(7):460-4.
[Google Scholar]
[140]Lev-on A, Steinfeld N. Local engagement online: municipal Facebook pages as hubs of interaction. Government Information Quarterly. 2015; 32(3):299-307.
[Crossref] [Google Scholar]
[141]Lande D, Subach I, Puchkov A. A system for analysis of big data from social media. Information & Security. 2020; 47(1):44-61.
[Crossref] [Google Scholar]
[142]Saggi MK, Jain S. A survey towards an integration of big data analytics to big insights for value-creation. Information Processing & Management. 2018; 54(5):758-90.
[Crossref] [Google Scholar]
[143]Jordan L. The problem with big data in translational medicine. a review of where weve been and the possibilities ahead. Applied & Translational Genomics. 2015; 6:3-6.
[Crossref] [Google Scholar]
[144]Corallo A, Fortunato L, Matera M, Alessi M, Camillò A, Chetta V, et al. Sentiment analysis for government: an optimized approach. In machine learning and data mining in pattern recognition: 11th international conference, MLDM 2015, Hamburg, Germany, 2015 (pp. 98-112). Springer International Publishing.
[Crossref] [Google Scholar]
[145]Li L, Ma S, Wang R, Wang Y, Zheng Y. Citizen participation in the co-production of urban natural resource assets: analysis based on social media big data. Journal of Global Information Management. 2021; 30(6):1-21.
[Crossref] [Google Scholar]
[146]Dash S, Shakyawar SK, Sharma M, Kaushik S. Big data in healthcare: management, analysis and future prospects. Journal of Big Data. 2019; 6(1):1-25.
[Google Scholar]
[147]Meng L, Li W. Application of big data technology in the field of public service: precise demand management. In proceedings of the 5th international conference on computer science and software engineering 2022 (pp. 670-4).
[Crossref] [Google Scholar]
[148]Xu J. An accurate management method of public services based on big data and cloud computing. Journal of Cloud Computing. 2023; 12(1):80.
[Crossref] [Google Scholar]
[149]De OCDK, Da SGG, Porto RB. Big data in the brazilian public health sector: concept, characteristics, benefits, and challenges. Revista do Serviço Público. 2022; 73(4):594-621.
[Google Scholar]
[150]Keikhosrokiani P. Big data analytics for healthcare: datasets, techniques, life cycles, management, and applications. Academic Press; 2022.
[Google Scholar]
[151]Goundar S, Masilamani K, Bhardwaj A, Dhasarathan C. Big data analytics in healthcare: a developing country survey. In applications of big data in large-and small-scale systems 2021 (pp. 85-98). IGI Global.
[Google Scholar]
[152]Mishra S, Tripathy HK, Mishra BK, Sahoo S. Usage and analysis of big data in E-health domain. In research anthology on big data analytics, architectures, and applications 2022 (pp. 417-30). IGI Global.
[Crossref]
[153]Schulte T, Bohnet-joschko S. How can big data analytics support people-centred and integrated health services: a scoping review. International Journal of Integrated Care. 2022; 22(2):23.
[Google Scholar]
[154]Sazu MH, Akter JS. Impact of big data analytics on government organizations. Management & Datascience. 2022; 6(2).
[Google Scholar]
[155]Khan S. Barriers of big data analytics for smart cities development: a context of emerging economies. International Journal of Management Science and Engineering Management. 2022; 17(2):123-31.
[Crossref] [Google Scholar]
[156]Dey N, Ella HA, Bhatt C, S AA, Chandra SS. Internet of things and big data analytics toward next-generation intelligence; 2018.
[Crossref] [Google Scholar]
[157]Losurdo F, Dileo I, Siergiejczyk M, Krzykowska K, Krzykowski M. Innovation in the ICT infrastructure as a key factor in enhancing road safety: a multi-sectoral approach. In 25th international conference on systems engineering 2017 (pp. 157-62). IEEE.
[Google Scholar]
[158]Rathore MM, Ahmad A, Paul A, Rho S. Urban planning and building smart cities based on the internet of things using big data analytics. Computer Networks. 2016; 101:63-80.
[Crossref] [Google Scholar]
[159]Di VA, Hassan R, Alavoine C. Data intelligence and analytics: a bibliometric analysis of human–artificial intelligence in public sector decision-making effectiveness. Technological Forecasting and Social Change. 2022; 174:121201.
[Crossref] [Google Scholar]
[160]Wibowo S, Sandikapura T. Improving data security, interoperability, and veracity using blockchain for one data governance, case study of local tax big data. In international conference on ICT for smart society 2019 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[161]Leonelli S. Data governance is key to interpretation: reconceptualizing data in data science. Harvard Data Science Review. 2019;1(1):1-9.
[Crossref] [Google Scholar]
[162]Moutselos K, Kyriazis D, Maglogiannis I. A web based modular environment for assisting health policy making utilizing big data analytics. In 9th international conference on information, intelligence, systems and applications 2018 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[163]Saunders GH, Christensen JH, Gutenberg J, Pontoppidan NH, Smith A, Spanoudakis G, et al. Application of big data to support evidence-based public health policy decision-making for hearing. Ear and Hearing. 2020; 41(5):1057-63.
[Crossref] [Google Scholar]
[164]Rahmanto F, Pribadi U, Priyanto A. Big data: what are the implications for public sector policy in society 5.0 era. In IOP conference series: earth and environmental science 2021 (pp. 1-7). IOP Publishing.
[Crossref] [Google Scholar]
[165]Amankwah-amoah J. Safety or no safety in numbers? Governments, big data and public policy formulation. Industrial Management & Data Systems. 2015; 115(9):1596-603.
[Crossref] [Google Scholar]
[166]El MIG, Mardiyanta A, Suryono A, Rizkika HL. Who owns big data? examine the policies of rural government in Indonesia. Promoting Adaptive System to The Current Turbulence Within Crisis Environments. 2023:298.
[Google Scholar]
[167]Panori A, Kakderi C, Komninos N. Transformation of smart city public services through AI and big data analytics: towards universal cross-sector solutions. Chapters. 2023:292-307.
[Google Scholar]
[168]Barkham R, Bokhari S, Saiz A. Urban big data: city management and real estate markets. Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities: Designing for Sustainability. 2022:177-209.
[Crossref] [Google Scholar]
[169]Liu X, Chang G. Construction of a public service cloud platform for disabled people based on the big data management model of the internet of things. Computational Intelligence and Neuroscience. 2023; 2023:1-11.
[Crossref] [Google Scholar]
[170]Ohemeng FL, Ofosu-adarkwa K. One way traffic: the open data initiative project and the need for an effective demand side initiative in Ghana. Government Information Quarterly. 2015; 32(4):419-28.
[Crossref] [Google Scholar]
[171]Huang T, Lan L, Fang X, An P, Min J, Wang F. Promises and challenges of big data computing in health sciences. Big Data Research. 2015; 2(1):2-11.
[Crossref] [Google Scholar]