A QUALITATIVE RESEARCH ON EVALUATING PUBLIC AND PRIVATE INSTITUTIONS’ EXISTING STATUS AND EXPECTATIONS ON BIG DATA AREA IN TURKEY
- Big Data Big Data and Education
- Büyük Veri, Büyük Veri ve Eğitim
How to Cite
Copyright (c) 2020
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Big data is defined as “Datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze (Manyika et al., 2011). Big data’s 5V characteristics are volume, velocity, variety, veracity, and value (Gahi, Guennoun & Mouftah, 2016).
- RESEARCH SUBJECT
In 2015, there were more than 2 million 350 thousand job postings for data science and analytics jobs in the USA. It is estimated that there will be close to 2 million 720 thousand job postings by 2020 (Miller & Hughes, 2017). Nearly 60 universities have data science programs at the undergraduate level in the USA (DataScience, 2020). In Turkey’s Strategy and Budget Presidency’s Strategic Plan (2019-2023), “Increasing the capacity of expert/ data scientists experienced in big data analysis and strengthening the infrastructure” is defined as a requirement (Strateji Ve Bütçe Başkanlığı, 2018). Accordingly, in this study, the expectations of public and private institutions from universities and students regarding the big data field were evaluated.
- RESEARCH PURPOSE AND IMPORTANCE
This study aims to examine the use of big data analytics in Turkey's public and private institutions and determine the institutions' expectations. Within the scope, the most preferred big data tools and the expectations the field of big data have towards students and universities in Turkey were determined. This study is essential in terms of being a resource for determining the expectations of institutions from universities and students in big data.
- CONTRIBUTION of the ARTICLE to the LITERATURE
The evaluation of public and private institutions’ expectations from universities and students in Turkey is expected to contribute to immense data analytics literature.
- DESIGN AND METHOD
- RESEARCH TYPE
This study is an applied research and was carried out through exploratory research.
- RESEARCH PROBLEMS
In this study, the following research questions are defined.
What are the big data tools and technologies preferred by public and private institutions that offer big data solutions?
What are the expectations of public and private institutions that provide big data solutions from students studying in computer science and related departments and universities in the field of big data?
- DATA COLLECTION METHOD
The Snowball sampling method was chosen in the interviews. The criterion in sample selection is that the institution offers big data solutions. Meetings were held with the general managers or prominent data project managers of ten institutions. Considering the adoption of big data by sectors, institutions in different sectors have preferred the research. In this study, interviews were conducted with the prominent data department managers of two public and eight private institutions that offer big data analytics solutions in Ankara.
- QUANTITATIVE / QUALITATIVE ANALYSIS
Qualitative research methods, namely the grounded theory approach, was used to analyze the data. The transcription of the voice recordings was collected to analyze the interview data. ter the interview transcripts were examined, they were subjected to the coding process using NVivo 12 software.
- RESEARCH MODEL
A qualitative research method was used in this study. The data obtained from the interviews were generalized inductively step by step. In this study, data collection and analysis were carried out together. After the data was collected, the concepts and processes that emerged with the analysis were included in the next data collection stage.
- FINDINGS AND DISCUSSION
- FINDINGS as a RESULT of ANALYSIS
The findings obtained with the data collected from the institutions were stated under three main parts: the current situation of the big data analytics of the institutions, their expectations from the universities and students, and their suggestions. The current status of the big data analytics category of the organizations covered the positions preferred by the big data team, the number of employees in these teams, the adequacy of this number, and the technologies they use. The category of expectation included the undergraduate programs preferred in big data and the abilities and skills expected from the graduates. The suggestion section included suggestions for students considering working in this field and for universities.
It was defined that a wide variety of position names were used, and it was revealed that the most preferred position name was a software engineer. It has been observed that most of the institutions use Hadoop Ecosystem. Spark, Cassandra, Hive, and Kafka are among the most preferred tools. The most preferred departments by institutions for recruitment are Statistics, Mathematics, and Computer Science. Among the skills expected from graduates, awareness and analytical thinking were the most expressed as the interviewed institutions' technical skills expectations are the database skills (SQL) and necessary programming skills. Among the suggestions of the interviewees to the students are to have awareness, to make an extra effort other than what is taught at school, to work part-time or internship, to have continuity of learning and to be eager to learning, to participate in online courses, to participate in seminars, courses, and events. The interviewees' recommendations for universities include computer science, statistics, mathematics, and management information systems to renew the curriculum, long-term internships, and open more graduate programs.
- DISCUSSING the FINDINGS with the LITERATURE
The U.S. Bureau of Labor Statistics states that positions covering data science and analytics do not have a clear definition. It is stated that positions such as data scientists and data engineering in data science and analytics can not be followed (Miller & Hughes, 2017). It was revealed that the institutions interviewed can not make a clear distinction between positions such as data scientists, data engineers, and software engineers. It was concluded that the tools used by the institutions and the tools used around the world (HDFS, Apache Hadoop, HBase, Cassandra, Hive, Oracle Big Data ve R) (Bhadani ve Jothimani, 2016) are similar. The institutions' recommendations are to be aware of the development of big data and analytics and follow innovations. The recommendations of the institutions for universities are to renew the curriculum and to open more graduate programs. Lawler and Molluzzo (2015) stated that with a good curriculum design, students studying in computer science and information system departments could contribute to big data analytics.
- CONCLUSION, RECOMMENDATION, AND LIMITATIONS
- RESULTS of the ARTICLE
It has been concluded that data scientists, data engineers, and data analysts could not be made clearly by institutions, yet when determining employees' positions in big data projects. It has been concluded that they generally prefer the position as a software engineer. Apache Hadoop, Spark, Hive, Kafka, and Cassandra were the most used tools by the interviewed institutions. It has been revealed that the institutions prefer the personnel who work in this field mostly from computer science, then mathematics, statistics, and management information systems. It is concluded that the institutions have suggested having awareness about the big data field for students and curriculum renewal for universities.
- SUGGESTIONS BASED on RESULTS
It is recommended to open more graduate programs and undergraduate programs in interdisciplinary fields such as data science and redesign the relevant departments' curriculum to include big data analytics. The renewal of the curriculum will help institutions make more precise decisions in determining positions and recruitment. Another suggestion is to have more course alternatives in the field of data science in related departments. It is also recommended for universities to add long-term internships to relevant departments.
- LIMITATIONS of the ARTICLE
In this research, interviews were done with the prominent data project team leaders of public and private institutions that offer big data solutions in Ankara. One of the limitations of this study is the limited number of institutions that produce solutions in big data. Another limitation is to find participants in the criteria determined due to the workload of the institutions.
- Analytics Center & Kariyer.net. (2016). Türkiye Analitik Yetenek ve Yetkinlik Araştırması. İstanbul: Smartcom.
- Bhadani, A. K., & Jothimani, D. (2016). Big data: challenges, opportunities, and realities. In Effective Big Data management and opportunities for implementation (pp. 1-24). IGI Global.
- Corbin, J., & Strauss A. (2015). Baiscs of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. Sage Publications. Fourth Edition.
- Cukier, K. (2010). Data, data everywhere. The Economist. URL: https://www.economist.com/special-report/2010/02/27/data-data-everywhere Son Erişim Tarihi: 08.05.2019
- DataScience. (2020). Data Science Colleges and Universities. URL: http://datascience.community/colleges Son Erişim Tarihi: 14.03.2020
- Davis, K., & Patterson, D. (2012). Ethics of Big Data: Balancing risk and innovation. O'Reilly Media.
- Demchenko, Y., Grosso, P., De Laat, C., & Membrey, P. (2013, May). Addressing big data issues in scientific data infrastructure. In 2013 International Conference on Collaboration Technologies and Systems (CTS) (pp. 48-55). IEEE.
- DiFranza, A. (2019, 11 29). The Biggest Data Analytics Challenge Of 2020. URL: https://www.northeastern.edu/graduate/blog/data-analytics-challenges/ Son Erişim Tarihi: 30.05.2020
- Dontha, R. (2017). Who came up with the name Big Data. Data Science Central, 13.
- Gahi, Y., Guennoun, M., & Mouftah, H. T. (2016, June). Big data analytics: Security and privacy challenges. In 2016 IEEE Symposium on Computers and Communication (ISCC) (pp. 952-957). IEEE.
- Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144.
- Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: strategies for qualitative research [pdf]. Zugriff unter http://www. sxf. uevora. pt/wp-content/uploads/2013/03/Glaser_1967. pdf.
- Gökşen, Y., & Aşan H. (2015). Veri büyüklüklerinin veritabanı yönetim sistemlerinde meydana getirdiği değişim: NOSQL. International Journal of Informatics Technologies, 8(3), 125.
- Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information systems, 47, 98-115.
- Jyothirmayee, A. V. N. S., Reddy, D. G., & Akbar, K. (2014). Understanding Big Data & DV2 law. International Journal of Emerging Technology and Advanced Engineering, 4(7).
- Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META group research note, 6(70), 1.
- Lawler, J., & Molluzzo, J. C. (2015). A proposed concentration curriculum design for big data analytics for information systems students. Information Systems Education Journal, 13(1), 45.
- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Byers, A. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
- Miller, S., & Hughes, D. (2017). The quant crunch: How the demand for data science skills is disrupting the job market. Burning Glass Technologies.
- Marzullo, K. (2016). Administration Issues Strategic Plan For Big Data Research and Developement. URL: https://obamawhitehouse.archives.gov/blog/2016/05/23/administration-issues-strategic-plan-big-data-research-and-development Son Erişim Tarihi: 08.06.2019
- Mayring, P. (2011). Nitel Sosyal Araştırmaya Giriş. Ankara: BilgeSu Yayıncılık.
- Mellody, M. (2014). Training Students to Extract Value from Big Data: Summary of a Workshop. National Academies Press. 500 Fifth Street NW, Washington, DC 20001.
- National Science Foundation [NSF], (2019, 2 13). Big Data Science and Engineering Program. URL: https://www.nsf.gov/pubs/2019/nsf19039/nsf19039.jsp Son Erişim Tarihi: 27.06.2020
- Merriam, S. B. (2013). Nitel araştırma desen ve uygulama için bir rehber (S. Turan, Çev.) Ankara: Nobel Yayın Dağıtım.
- Rydning, D. R. J. G. J. (2018). The digitization of the world from edge to core. Framingham: International Data Corporation.
- Singh, D. S., & Singh, G. (2017). Big Data-A Review. International Research Journal of Engineering and Technology, 4(04), 2395-0056.
- Strateji Ve Bütçe Başkanlığı. (2018). Stratejik Plan (2019-2023). Strateji Ve Bütçe Başkanlığı.
- Swanstrom, R. (2016, 11 28). Data Scientists, Data Engineers, Software Engineers: The Difference According to LinkedIn. DataScience 101.
- Trifu, M. R., & Ivan, M. L. (2014). Big Data: present and future. Database Systems Journal, 5(1), 32-41.
- TÜBİTAK. (2016). Bulut Bilişim Ve Büyük Veri Çaliştayi 2015 Faaliyet Raporu. Kocaeli: TÜBİTAK Bilgem.
- Warden, P. (2011). Big Data Glossary . Beijing • Cambridge • Farnham • Köln • Sebastopol • Tokyo : O’Reilly Media.
- Wormer, P. V. (2014, 11 11). A sense of urgency: Excecutives rush to adobt Big Data analytics. URL: http://info.totaltraxinc.com/blog/a-sense-of-urgency-executives-rush-to-adopt-big-data-analytics Son Erişim Tarihi: 15.02.2021
- Yıldırım, A., & Şimşek, H. (2013). Sosyal Bilimlerde Nitel Araştırma Yöntemleri. Seçkin Yayıncılık.
- YÖK Atlas. (2020). Yapay Zeka Mühendisliği Programı Bulunan Tüm Üniversiteler. URL: https://yokatlas.yok.gov.tr/lisans-bolum.php?b=554009 Son Erişim Tarihi: 10.11.2020
- Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media.