• Logo
  • HamaraJournals

Knowledge, Attitude, Challenges of Big Data Analytics based on IT Staffs Point of View in a Developing Country

Elham Nazari, Zahra Ebnehoseini, Zhila Agharezaei, Hamed Tabesh
162

Views


Abstract

Introduction: The skilled IT staff about big data analytics can motivate organizations to adopt the big data analytics. The aim of the current study is to present the knowledge, attitude, and challenges of the big data analytics based on IT staff’ viewpoints in a developing country.

Material and Methods: A self-administered semi-structured questionnaire was developed based on a literature review. Content validity and face validity were measured using Delphi technique. The questionnaire comprised of three parts including knowledge, attitude, and challenges. Descriptive statistics were used to summarize the results. The chi-square test was applied to identify associations between knowledge and attitude of participants with the demographic characteristics.

Results: Out of a total of 250 IT staffs, 120 participated in the study. Knowledge levels were low, moderate, and high in 35.0%, 33.3%, and 31.7 % of the participants, respectively. The two most affecting factors on the knowledge level of participants were age groups and sex. IT staffs hold a positive attitude toward big data analytics. The most of IT staffs believed that big data management is necessary for the country and they agreed that big data analyzes can provide many advantages to organization managers. As well, 35 challenges of the big data analytics were identified.

Conclusions: Our results showed that the big data analytics face with many problems in following issues: awareness and education, recruiting skilled specialists, presentation big data analytics benefits to IT managers and policy-makers, conducting research projects, developing a strategic plan at national and local levels.


References

Nambiar R, Bhardwaj R, Sethi A, Vargheese R. A look at challenges and opportunities of big data analytics in healthcare. International Conference on Big Data. IEEE; 2013.

Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst. 2014; 2(1): 3. PMID: 25825667 DOI: 10.1186/2047-2501-2-3

Olayinka O, Kekeh M, Sheth-Chandra M, Akpinar-Elci M. Big Data knowledge in global health education. Ann Glob Health. 2017; 83(3-4): 676-81. PMID: 29221544 DOI: 10.1016/j.aogh.2017.09.005

Emani CK, Cullot N, Nicolle C. Understandable big data: A survey. Computer Science Review. 2015; 17: 70-81.

Hariri RH, Fredericks EM, Bowers KM. Uncertainty in big data analytics: Survey, opportunities, and challenges. Journal of Big Data. 2019; 6(1): 44.

Häyrinen K, Saranto K, Nykänen P. Definition, structure, content, use and impacts of electronic health records: A review of the research literature. Int J Med Inform. 2008; 77(5): 291-304. PMID: 17951106 DOI: 10.1016/j.ijmedinf.2007.09.001

Doi K. Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Comput Med Imaging Graph. 2007; 31(4-5): 198–211. PMID: 17349778 DOI: 10.1016/j.compmedimag.2007.02.002

Big Data Value Association, TF7 Healthcare Subgroup. Big data technologies in healthcare: Needs, opportunities and challenges. BDV publication; 2016.

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.

DeMauro A, Greco M, Grimaldi M. What is big data? A consensual definition and a review of key research topics. AIP Conference Proceedings. 2015; 1644(1): 97.

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

Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA. 2013; 309(13): 1351-2. PMID: 23549579 DOI: 10.1001/jama.2013.393

Bossé É, Roy J, Wark S. Concepts, models, and tools for information fusion. Artech House; 2007.

Jin X, Wah BW, Cheng X, Wang Y. Significance and challenges of big data research. Big Data Research. 2015; 2(2): 59-64.

Archenaa J, Anita EM. A survey of big data analytics in healthcare and government. Procedia Computer Science. 2015; 50: 408-13.

Purkayastha S, Braa J. Big data analytics for developing countries–using the cloud for operational BI in health. The Electronic Journal of Information Systems in Developing Countries. 2013; 59(1): 1-7.

Sagiroglu S, Sinanc D. Big data: A review. International Conference on Collaboration Technologies and Systems. IEEE; 2013.

Hermon R, Williams PA. Big data in healthcare: What is it used for? Australian eHealth Informatics and Security Conference. Edith Cowan University; 2014.

Bossé É, Solaiman B. Information fusion and analytics for big data and IoT. Artech House; 2016.

Gunay O, Toreyin BU, Kose K, Cetin AE. Entropy-functional-based online adaptive decision fusion framework with application to wildfire detection in video. IEEE Trans Image Process. 2012; 21(5): 2853-65. PMID: 22249709 DOI: 10.1109/TIP.2012.2183141

Pugna IB, Duțescu A, Stănilă OG. Corporate attitudes towards big data and its impact on performance management: A qualitative study. Sustainability. 2019; 11(3): 684.

Minou J, Routsis F, Gallos P, Mantas J. Health informatics scientists' perception about big data technology. Studies in Health Technology and Informatics. 2017; 238: 144-6.

Shah N, Irani Z, Sharif AM. Big data in an HR context: Exploring organizational change readiness, employee attitudes and behaviors. Journal of Business Research. 2017; 70: 366-78.

Raguseo E. Big data technologies: An empirical investigation on their adoption, benefits and risks for companies. International Journal of Information Management. 2018; 38(1): 187-95.




DOI: http://dx.doi.org/10.30699/fhi.v9i1.225

Refbacks

  • There are currently no refbacks.