Araştırma Makalesi
BibTex RIS Kaynak Göster

Assessing the Awareness Levels and Usage of Big Data Technologies by Companies in Turkey

Yıl 2020, Sayı: 18, 728 - 737, 15.04.2020
https://doi.org/10.31590/ejosat.675247

Öz

The need to understand, analyze, produce quick results, and develop new and better tools that will facilitate data utilization, continues to grow with technological and scientific developments. The effective use of large sources of information provides new unforeseen opportunities in resource utilization and decision making. The analysis of large data provides serious savings and new possibilities by taking the information-based decision support to a higher level in many critical areas. This study aimed at determining the maturity levels of using big data technologies in companies operating in Turkey. For this purpose, relevant firms and their representatives were determined and big data usage and maturity indexes of companies from different industries were established. In this framework, a survey on big data usage and awareness was designed. 101 individuals from different firms were interviewed and data were collected through the survey. In light of this dataset, an index measuring the usage of big data technologies in Turkey has been established and the companies’ preparation to big data paradigm of and their use of big data technologies has been assessed using the index. Thus, the opportunities and awareness of big data technologies in sectoral terms has been investigated.

Kaynakça

  • Ahalt, S., & Kelly, K. (2013). The big data talent gap. UNC Kenan-Flagler Business School White Paper, 1-15.
  • Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics, 24(1), 1-7. 3351.
  • Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45-59.
  • Bughin, J. (2016). Big data, Big bang?. Journal of Big Data, 3(1), 2.
  • Che, D., Safran, M., & Peng, Z. (2013). From big data to big data mining: challenges, issues, and opportunities. In International Conference on Database Systems for Advanced Applications (pp. 1-15). Springer, Berlin, Heidelberg.
  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 1165-1188.
  • Davenport, T. H., & Kudyba, S. (2016). Designing and developing analytics-based data products. MIT Sloan Management Review, 58(1), 83.
  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
  • Günther, W. A., Mehrizi, M. H. R., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems, 26(3), 191-209.
  • Halper, F., & Krishnan, K. (2013). TDWI big data maturity model guide interpreting your assessment score. TDWI Benchmark Guide, 2014, 2013.
  • IBM. (2012), What is big data: Bring big data to the enterprise, http://www01.ibm.com/software/data/bigdata/, IBM. Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), 387-394.
  • Kim, G. H., Trimi, S., & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85.
  • Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Ali, M., Kamaleldin, W., ... & Gani, A. (2014). Big data: survey, technologies, opportunities, and challenges. The Scientific World Journal, 2014.
  • Labrinidis, A., & Jagadish, H. V. (2012). Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 5(12), 2032-2033.
  • Moro, S., Rita, P., & Vala, B. (2016). Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach. Journal of Business Research, 69(9), 3341
  • Raguseo, E. (2018). Big data technologies: An empirical investigation on their adoption, benefits and risks for companies. International Journal of Information Management, 38(1), 187-195.
  • Ransbotham, S., Kiron, D., & Prentice, P. K. (2016). Beyond the hype: the hard work behind analytics success. MIT Sloan Management Review, 57(3).
  • Snijders, C., Matzat, U., & Reips, U. D. (2012). "Big Data": big gaps of knowledge in the field of internet science. International Journal of Internet Science, 7(1), 1-5.
  • Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.

Türkiye’de Firmaların Büyük Veri Teknolojileri Bilinirliği ve Kullanımı Analizi

Yıl 2020, Sayı: 18, 728 - 737, 15.04.2020
https://doi.org/10.31590/ejosat.675247

Öz

Doğru kararları almak için toplanan büyük boyutlardaki verileri anlama, analiz etme, hızlı sonuçlar üretme, veri kullanımını kolaylaştıracak yeni ve daha iyi araçlar geliştirme ihtiyacı teknolojik ve bilimsel gelişmeler ile birlikte artarak devam etmektedir. Geniş bilgi kaynaklarının etkin bir şekilde kullanımı, kaynak kullanımında ve karar vermede öngörülemeyen yeni fırsatlar sağlamaktadır. Büyük verilerin analizinin birçok kritik alanda bilişimin karar desteğini bir üst boyuta taşıyarak ciddi tasarruflar ve yeni olanaklar sağlamaktadır. Bu çalışmada, büyük veri uygulamalarının Türkiye’de faaliyet göstermekte olan firmalarda kullanım olgunluğunu belirlemek amaçlanmıştır. Bu amaçla ilgili firmalar ve temsilcileri belirlenmiş olup, farklı sanayi kollarından şirketlerin özellikle büyük veri kullanım ve olgunluk indeksleri oluşturulmuştur. Bu çerçevede büyük veri kullanımı ve bilinirliği üzerine bir anket tasarlanmıştır. 101 tekil firma yetkilisi ile görüşülmüş olup, anket aracılığıyla veri toplanmıştır. Bu veriler ışığında büyük veri teknolojilerinin Türkiye’de kullanım indeksi oluşturulup firmaların büyük veri paradigmasına hazırlıkları ve kullanım dereceleri ölçülmüştür. Böylece sektörel anlamda büyük veri teknolojilerinin ve kazanımlarının bilinirliği ortaya çıkarılmıştır.

Kaynakça

  • Ahalt, S., & Kelly, K. (2013). The big data talent gap. UNC Kenan-Flagler Business School White Paper, 1-15.
  • Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics, 24(1), 1-7. 3351.
  • Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45-59.
  • Bughin, J. (2016). Big data, Big bang?. Journal of Big Data, 3(1), 2.
  • Che, D., Safran, M., & Peng, Z. (2013). From big data to big data mining: challenges, issues, and opportunities. In International Conference on Database Systems for Advanced Applications (pp. 1-15). Springer, Berlin, Heidelberg.
  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 1165-1188.
  • Davenport, T. H., & Kudyba, S. (2016). Designing and developing analytics-based data products. MIT Sloan Management Review, 58(1), 83.
  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
  • Günther, W. A., Mehrizi, M. H. R., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems, 26(3), 191-209.
  • Halper, F., & Krishnan, K. (2013). TDWI big data maturity model guide interpreting your assessment score. TDWI Benchmark Guide, 2014, 2013.
  • IBM. (2012), What is big data: Bring big data to the enterprise, http://www01.ibm.com/software/data/bigdata/, IBM. Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), 387-394.
  • Kim, G. H., Trimi, S., & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85.
  • Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Ali, M., Kamaleldin, W., ... & Gani, A. (2014). Big data: survey, technologies, opportunities, and challenges. The Scientific World Journal, 2014.
  • Labrinidis, A., & Jagadish, H. V. (2012). Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 5(12), 2032-2033.
  • Moro, S., Rita, P., & Vala, B. (2016). Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach. Journal of Business Research, 69(9), 3341
  • Raguseo, E. (2018). Big data technologies: An empirical investigation on their adoption, benefits and risks for companies. International Journal of Information Management, 38(1), 187-195.
  • Ransbotham, S., Kiron, D., & Prentice, P. K. (2016). Beyond the hype: the hard work behind analytics success. MIT Sloan Management Review, 57(3).
  • Snijders, C., Matzat, U., & Reips, U. D. (2012). "Big Data": big gaps of knowledge in the field of internet science. International Journal of Internet Science, 7(1), 1-5.
  • Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.
Toplam 19 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Serkan Ayvaz 0000-0003-2016-4443

Yücel Batu Salman 0000-0001-5038-1612

Yayımlanma Tarihi 15 Nisan 2020
Yayımlandığı Sayı Yıl 2020 Sayı: 18

Kaynak Göster

APA Ayvaz, S., & Salman, Y. B. (2020). Türkiye’de Firmaların Büyük Veri Teknolojileri Bilinirliği ve Kullanımı Analizi. Avrupa Bilim Ve Teknoloji Dergisi(18), 728-737. https://doi.org/10.31590/ejosat.675247