Opportunities of Big Data Management in Libraries and Information Center: Structural-Interpretive Analysis and Finding a Solution

Darmandeh, Mozhdeh and Noruzi, Alireza and Esmaeili Givi, Mohammadreza Opportunities of Big Data Management in Libraries and Information Center: Structural-Interpretive Analysis and Finding a Solution. Iranian Journal of Information processing and Management, 2019, vol. 34, n. 2, pp. 841-870. [Journal article (Paginated)]

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English abstract

Big Data is a collection of massive and complex data sets and data volume that has characteristics such as valence, validity, value, variability, variety, velocity, veracity, visualization, volatility, and volume. These features of big data make it difficult to process and manage with a typical database, but extracting value from it can improve many organizational and non-organizational processes, although it may cost and require the use of modern ICT infrastructure and techniques. The aim of this research was to identify, determine, prioritize and analyze the opportunities of big data; to analyze structural-interpretation of it; and to suggest solutions for managing big data in libraries and information centers in Tehran. The methodology of this applied research was descriptive survey and the total number of chief managers of central libraries of public universities in Tehran was considered as its research population (35 chief managers). The data were collected through library studies and questionnaire. In fact, previous studies and the documentary research method were used to review the fundamental opportunities of big data in order to design the questionnaire based on the research model. Data was analyzed through descriptive and inferential statistical methods by the SPSS and the Smart PLS softwares. Finally, the second questionnaire which was designed based on Interpretive Structural Modeling (ISM). It was distributed among 15 experts in the field of big data and analyzed manually. The results showed that from the viewpoints of managers of libraries and information centers in Tehran, opportunities of big data management are as follows in order of importance: managerial-organizational, essential, procedural and human resources. However, according to the experts' opinions, the opportunities of big data were as follows: essential, procedural, human resources and managerial-organizational. One of the main reasons for this difference was that managers were more focused on managerial-organizational issues and they had less familiarity with big data; while experts, with a more understanding of the concept of big data focused on essential opportunities, and the managerial-organizational opportunities were less important from their viewpoint.

Item type: Journal article (Paginated)
Keywords: Big data, Big Data Management, Opportunities, Solutions, Information Center, Libraries, Tehran, Iran
Subjects: B. Information use and sociology of information > BC. Information in society.
L. Information technology and library technology > LZ. None of these, but in this section.
Depositing user: Dr. Alireza Noruzi
Date deposited: 17 Mar 2019 12:38
Last modified: 17 Mar 2019 14:30
URI: http://hdl.handle.net/10760/34221

References

Anderson, J. Q. & L. Rainie. 2012. Big data: Experts say new forms of information analysis will help people be more nimble and adaptive, but worry over humans’ capacity to understand and use these new tools well. Washington, DC: Pew Research Center.

Barca, R., L. Haas, A. Halevy, P. Miller, and R. V. Zicari. 2012. Big data for good. Operational Database Management Systems)ODBMS( Industry Watch. Retrieved from http://www.odbms.org/2012/06/big-data-for-good/

Bello-Orgaz, G., J. J. Jung, & D. Camacho. 2016. Social big data: recent achievements and new challenges. Information Fusion, 28, 45-59.

Chen, C. P., & C. Y. Zhang. 2014. Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences 275: 314-347.

Chen, M., S. Mao, & Y. Liu. 2014. Big data: A survey. Mobile Networks and Applications 19 (2), 171-209.

Data science series. 2016. Ten practical big data benefits data science stories. Retrieved from http://datascienceseries.com/stories/ten-practical-big-data-benefits

Davenport, T. 2014. Three big benefits of big data analytics. Retrieved from https://www.sas.com/en_ca/news/sascom/2014q3/Big-data-davenport.html

DeMers, J. 2016. 7 technology trends that will dominate 2017. Forbes. Retrieved from https://www.forbes.com/sites/jaysondemers/2016/11/16/7-technology-trends-that-willdominate-2017/#27dd2d1e4a51

EMC. 2012. Big data: Big opportunities to create business value. Retrieved from https://www.emc.com/microsites/cio/articles/big-data-big-opportunities/index.htm

European Data Protection Supervisor. 2015. Meeting the challenges of big data: A call for transparency, user control, data protection by design and accountability. Retrieved from https://edps.europa.eu/sites/edp/files/publication/15-11-19_big_data_en.pdf

Fan, J., F. Han, & H. Liu. 2014. Challenges of big data analysis. National science review 1 (2): 293-314.

Fister, B. 2015. Big data or big brother? data, ethics, and academic libraries. Library Issues: Briefings for Faculty and Administrators 35 (4): 293-314.

Gandomi, A., & M. Haider. 2015. Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management 35: 137-144.

Gopinath, S. 2015. 10 Reasons why big data analytics is the best career move. Retrieved from http://www.edureka.co/blog/10-reasons-why-big-data-analytics-is-the-best-career-move

HDFS Tutorial Team. 2017. 5 Big data use cases in banking and financial services. Retrieved from https://www.hdfstutorial.com/blog/big-data-use-cases-in-banking-and-financial-services/

Jagadish, H. V., J. Gehrke, A. Labrinidis, Y. Papakonstantinou, J. M. Patel, R. Ramakrishnan, & C. Shahabi. 2014. Big data and its technical challenges. Communications of the ACM 57 (7): 86-94.

Kaisler, S., F. Armour, J. A. Espinosa, & W. Money. 2013. Big data: Issues and challenges moving forward. In System sciences (HICSS), 2013 46th Hawaii International Conference on (pp. 995-1004). IEEE.

Kalra, B., S. Yadav, & D. K. Chauhan. 2014. A review of issues and challenges with big data. International Journal of Computer Science and Information Technology Research. 2 (4): 97-101.

Karr, D. 2016. What is Big Data? What Are the Benefits of Big Data? Retrieved from https://marketingtechblog.com/benefits-of-big-data/

Katal, A., M. Wazid, & R. H. Goudar. 2013. Big data: issues, challenges, tools and good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference on Noida, India (pp. 404-409). IEEE.

Khan, N., I. Yaqoob, I. A. T. Hashem, Z. Inayat, M. Ali, W. Kamaleldin, ... & A. Gani. 2014. Big data: survey, technologies, opportunities, and challenges. The Scientific World Journal. vol. 2014, Article ID 712826, 18 pages.

Kuketz, D. 2012. The 7 biggest business benefits from big data. Retrieved from http://www.utopiainc.com/insights/blog/381-7-biggest-business-benefits-from-big-data

Kwon, O., N. Lee, & B. Shin. 2014. Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management 34 (3): 387-394.

Liu, H., & G. Guo. 2016. Opportunities and challenges of big data for the social sciences: The case of genomic data. Social science research 59: 13-22.

Manyika, J., M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, & A. H. Byers. 2011. Big data: The next frontier for innovation, competition, and productivity. New York, NY: McKinsey Global Institute. Retrieved from http://bit.ly/McKinseyBigDataReport

Marvin, H. J., E. M. Janssen, Y. Bouzembrak, P. J. Hendriksen, & M. Staats. 2017. Big data in food safety: An overview. Critical Reviews in Food Science and Nutrition 57 (11): 2286-2295.

Oxford Advanced Learner's Dictionary. 2003. 7th edition. Oxford: Oxford University Press.

Pryor, G., & M. Donnelly. 2009. Skilling up to do data: whose role, whose responsibility, whose career? International Journal of Digital Curation 4 (2): 158-170.

Pulse, U. G. 2012. Big data for development: Challenges & opportunities. Naciones Unidas, Nueva York, Mayo.

Ray, T. 2017. Scopes of big data & data science in the banking & finance (FinTech) Sector. Retrieved from https://www.stoodnt.com/blog/scopes-of-big-data-data-science-in-the-banking-finance-fintech-sector/

Runion, D. A. 2015. A Study of the perceptions held by information technology professionals in relation to the maturity, value, and practical deployment of big data solutions. Doctoral dissertation, Universidad Central de Nicaragua (Nicaragua).

SAS Inc. 2013. Five big data challenges: how to overcome them with visual analytics. SAS, White paper.

Smith, M. 2012. The big deal in big data is a big opportunity. Vennata Research. Retrieved from https://blog.ventanaresearch.com/2012/12/05/the-big-deal-in-big-data-is-a-big-opportunity

Staffing, A. 2016 .6 reasons switching to big data analytics is a great career move. Retrieved from http://www.aditiconsulting.com/6-reasons-switching-to-big-data-analytics-is-a-great-career-move

TCS: TATA Consultancy Services. 21 Mar. 2013 . Big Data Study - The 10 Key Findings. Enterprise Insights, sites.tcs.com/big-data-study/big-data-study-key-findings/

Westin, G. F., A. L. Dias, & R. S. Go. 2016. Exploring big data in hematological malignancies: Challenges and opportunities. Current Hematologic Malignancy Reports 11 (4): 271-279.

Wetzels, M., G. Odekerken-Schröder, & C. Van Oppen. 2009. Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly 33(1) :177-195.

Yaqoob, I., V. Chang, A. Gani, S. Mokhtar, I. A. T. Hashem, N. B. Anuar, & S. U. Khan. 2016. Information fusion in social big data: foundations, state-of-the-art, applications, challenges, and future research directions. International Journal of Information Management. Withdrawn Article in Press.

Zicari, R. V. 2014. Big data: Challenges and opportunities. Big Data Computing. London: New York: Tylor& Francis Group. 103-129.


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