Enlightening the Repercussion of Dark Data Management towards Malaysian SMEs Sustainability

Ahmad Fuzi Md Ajis; Jafalizan Md Jali; Isma Ishak; Qamarul Nazrin Harun

doi:10.21834/e-bpj.v8iSI15.5070

Authors

Ahmad Fuzi Md Ajis School of Information Science, College of Computing, Informatics and Media, Universiti Teknologi MARA Cawangan Johor, Segamat, Malaysia
Jafalizan Md Jali School of Information Science, College of Computing, Informatics and Media, Universiti Teknologi MARA Cawangan Selangor, Shah Alam, Malaysia
Isma Ishak School of Information Science, College of Computing, Informatics and Media, Universiti Teknologi MARA Cawangan Johor, Segamat, Malaysia
Qamarul Nazrin Harun School of Information Science, College of Computing, Informatics and Media, Universiti Teknologi MARA Cawangan Johor, Segamat, Malaysia

DOI:

https://doi.org/10.21834/e-bpj.v8iSI15.5070

Keywords:

Dark Data Management, Malaysian SMEs, Dark Data Repercussion Model, Business Sustainability

Abstract

The sheer volume of dark data impacts the costs for searching and producing appropriate information and imposes a wasted storage cost in operating budgets. Therefore, a grounded theory research was conducted to investigate the dark data phenomenon towards SMEs in Malaysia. Straussian Grounded Theory Methodology was deployed to analyze collected qualitative data to investigate the repercussions of dark data management towards sustainability of Small & Medium enterprises in Malaysia. Consequently, the study found that dark data is a precious asset to leverage and maintain sustainable business, and a model on the repercussions of dark data management was proposed.

References

Ajis, A. F. M. & Baharin, S. H. (2019) “Dark data management as frontier of information governance,” in Proc. 9th IEEE Symp. Comput. Appl. Ind. Electron., pp. 34-37, doi: 10.1109/ISCAIE.2019.8743915. DOI: https://doi.org/10.1109/ISCAIE.2019.8743915

Akbar, L. S, Al-Mutahr, K. & Nazeh, M. (2018). Aligning IS/IT with Business Allows Organizations to Utilize Dark Data. International Journal of Innovative Technology and Exploring Engineering. 8(2), 80 - 85.

Almeida, C. A. et al. (2021). Excavating FAIR Data: the Case of the Multicenter Animal Spinal Cord Injury Study (MASCIS), Blood Pressure, and Neuro-Recovery. Neuroinformatics. doi: 10.1007/s12021-021-09512-z. DOI: https://doi.org/10.1007/s12021-021-09512-z

Berghel, H., Hoelzer, D., & Sthultz, M. (2008). Chapter 1 Data Hiding Tactics for Windows and Unix File Systems. Advances in Computers. 74, pp. 1-17, doi: 10.1016/s0065-2458(08)00601-3 DOI: https://doi.org/10.1016/S0065-2458(08)00601-3

Commvault (2014). 5 Ways to Illuminate your dark data. US: Commvault Systems.

Corallo, A., Crespino A. M., Vecchio, V. D., Lazoi, M. & Marra, M. (2021). Understanding and Defining Dark Data for the Manufacturing Industry. IEEE Transactions on Engineering Management, vol. 70, no. 2, pp. 700-712, Feb. 2023, doi: 10.1109/TEM.2021.3051981. DOI: https://doi.org/10.1109/TEM.2021.3051981

Dimitrov, W., Siarova, S. & Petkova, L. (2018). Types of dark data and hidden cybersecurity risks. DOI: 10.13140/RG.2.2.31695.43681

Gartner (2014). Turning Dark Data into Smart Data: How Email and File Level Analytics Can Lead to Greater Business Value in the Age of Information. Retreived on Apr 4th 2023 from https://www.gartner.com/en/information-technology/glossary/dark-data

Gimpel, G. (2020a). Dark data: the invisible resource that can drive performance now. Journal of Business Strategy, Vol. 42 No. 4, pp. 223-232. https://doi.org/10.1108/JBS-02-2020-0046 DOI: https://doi.org/10.1108/JBS-02-2020-0046

Gimple, G. (2020b). Bringing dark data into the light: Illuminating existing IoT data lost within your organization. Business Horizons 63, 519-530, doi: 10.1016/j.bushor.2020.03.009 DOI: https://doi.org/10.1016/j.bushor.2020.03.009

Gimpel, G. & Alter, A. (2021). Benefit From the Internet of Things Right Now by Accessing Dark Data. IT Professional. 23(2), 45-49, doi: 10.1109/mitp.2020.3025483 DOI: https://doi.org/10.1109/MITP.2020.3025483

Hand, D. J., (2020). Dark Data: Why What You Don't Know Matters. USA: Princeton University Press. DOI: https://doi.org/10.1515/9780691198859

Hawkins, B. E., Huie, J. R., Almeida, C., Chen, J. & Ferguson, A. R. (2020). Data Dissemination: Shortening the Long Tail of Traumatic Brain Injury Dark Data. Journal of Neurotrauma. 37, 2414–2423, doi: 10.1089/neu.2018.6192 DOI: https://doi.org/10.1089/neu.2018.6192

HighQuest Solution (2016). Dark Data Making your Organisation data-enabled? Retrieved on April 4th, 2019 from https://doczz.net/doc/8987800/white-paper-dark-data-making-your-organisation-data

Hitachi (2013). Big Data - Shining the light on enterprise dark data (EDD). Retrieved April 15, 2019 from https://www.hitachivantara.com/en-us/resources.html

Imdad, M. et al. (2020). Dark Data: Opportunities and Challenges. International Research Journal of Computer Science and Technology. 1(1), 38-46.

Kambies, T., Roma, P., Mittal, N. & Sharma, S. K. (2017) Dark analytics: Illuminating opportunities hidden within unstructured data. Retrieved on Oct. 16, 2022 from https: //www2.deloitte.com/insights/us/en/focus/tech-trends/2017/dark-data- analyzing- unstructured- data.html

Kevin, N. M., et. al. (2016). Dark data: Business Analytical tools and Facilities for illuminating dark data. Scientific Research Journal. 4, 1-10.

Lehmann, H. (2011). The Dynamics of International Information Systems: Anatomy of a Grounded Theory Investigation. New Zealand: Springer.

Lincoln, Y. S., Guba, E. G. (1984). Naturalistic inquiry. California: Sage.

Liu, Y. et al. (2019). A Framework for Image Dark Data Assessment. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11641. Springer, Cham. doi:10.1007/978-3-030-26072-9_1. DOI: https://doi.org/10.1007/978-3-030-26072-9_1

Lugmayr, A., Stockleben, B., Scheib, C., Mailaparampil, M.A. (2017) Cognitive big data: survey and review on big data research and its implications. What is really “new” in big data?. Journal of Knowledge Management, 21(1), pp.197-212, doi: 10.1108/JKM-07-2016-0307 DOI: https://doi.org/10.1108/JKM-07-2016-0307

Martin, E. J. (2016). Dark Data: Analyzing Unused and Ignored Information. Retrieved on April 4th, 2023 from https://www.thetilt.com/content/dark-data-analyzing-unused-ignored-information

Munot, K., Mehta, N., Mishra, S. & Khanna, B. (2019). Importance of Dark Data and its Applications. 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN). 1-6, doi: 10.1109/ICSCAN.2019.8878789. DOI: https://doi.org/10.1109/ICSCAN.2019.8878789

Neff, E.P. (2018). Dark data see the light. Lab Animal. 47(2), 45-48, doi: 10.1038/laban.1405 DOI: https://doi.org/10.1038/laban.1405

Patton, M. Q., (2014). Qualitative research and analysis method. (4th ed.). California, SAGE.

Schembera, B. (2019). The dark side of data management. Retrieved on April 4th, 2023 from https://www.researchgate.net/publication/355545901

Schembera, B. & Duran, J. M. (2020). Dark Data as the New Challenge for Big Data Science and the Introduction of the Scientific Data Officer. Philosophy & Technology. 33, 93–115, doi: 10.1007/s13347-019-00346-x DOI: https://doi.org/10.1007/s13347-019-00346-x

Splunk (2019). The state of dark data. Retrieved on July 3rd, 2020 from https://www.splunk.com/en_us/form/the-state-of-dark-data.html

Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. Thousand Oaks, CA: Sage

Veritas (2015). The databerg report: see what others don’t, identify the value, risk and cost of your data. California: Veritas.

Veritas (2016). State of Information Governance: 2016 Report. California: Veritas

Veritas, DLT Solutions & GovLoop (2017). Dark Data Management: The Next Frontier for Government Data. Washington: GovLoop

Waide, R.B., Brunt, J.W., & Servilla, M.S. (2017). Demystifying the landscape of ecological data repositories in the United States. BioScience. 67(12), pp.1044-1051. DOI: https://doi.org/10.1093/biosci/bix117