The Maturity Measurement of Big Data Adoption in Manufacturing Companies Using the TDWI Maturity Model
Downloads
Background: Big data technology has been used in several sectors in Indonesia. Adoption of big technology provides great potential for research, especially achievement in the implementation of big data in manufacturing companies. The Data Warehousing Institute (TDWI) Maturity Model is a tool that can be used to measure the state of "As-is" implementation of big data using 5 main dimensions. Maturity level shows the level of organizational ability to adjust big data technology currently.
Objective: This study aims to measure the level of maturity in the implementation of big data technology in manufacturing companies PT. XYZ. This measurement is considered very important because it can know the process of managing data that is structured and has a high volume of data and provides more transparent reporting. This can help the company in making decisions that provide good information, so the company can increase the trust of stakeholders.
Methods: This study uses qualitative methods to analyze research data using TWDI Maturity Model tools. Interview technique is used to retrieve respondent data where interview preparation guidelines are made by paying attention to 5 dimensions and 50 indicators in TDWI.
Results: The research showed that the implementation of big data technology in the company as a whole has reached the level of corporate adoption. Infrastructure, data management, and analytics dimensions have reached the corporate adoption level while the organizational and governance dimensions are still at an early adoption level.
Conclusion: To measure the maturity level of adoption of big data technology in manufacturing companies can use qualitative methods with TDWI Maturity model tools, interview guides for data retrieval by considering the 5 dimensions and 50 indicators that exist in TDWI.
N. Mohamed and J. Al-Jaroodi, "Real-time big data analytics: Applications and challenges,” in Proceedings of the 2014 International Conference on High Performance Computing and Simulation, HPCS 2014, 2014, doi: 10.1109/HPCSim.2014.6903700.
M. E. Stucke and A. P. Grunes, "Introduction: Big Data and Competition Policy,” Big Data Compet. Policy, 2016.
D. Bumblauskas, H. Nold, P. Bumblauskas, and A. Igou, "Big data analytics: transforming data to action,” Bus. Process Manag. J., 2017, doi: 10.1108/BPMJ-03-2016-0056.
A. Uluwiyah and Y. Setiadi, "Improving data quality through big data: Case study on big data-mobile positioning data in Indonesia tourism statistics,” in Proceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security, 2018, doi: 10.1109/IWBIS.2017.8275101.
S. Mariyah, "Identification of big data opportunities and challenges in statistics Indonesia,” in Proceedings - 2014 International Conference on ICT for Smart Society: "Smart System Platform Development for City and Society, GoeSmart 2014”, ICISS 2014, 2014, doi: 10.1109/ICTSS.2014.7013148.
T. C. Economics, "For Big Data Analytics There ' s No Such Thing as Too Big,” NOT A Pap., 2012.
A. McAfee and E. Brynjolfsson, "Big data: The management revolution,” Harv. Bus. Rev., 2012.
M. Comuzzi and A. Patel, "How organisations leverage: Big Data: A maturity model,” Ind. Manag. Data Syst., 2016, doi: 10.1108/IMDS-12-2015-0495.
O. Tene and J. Polonetsky, "Privacy in the Age of Big Data: A Time for Big Decisions,” Stanford Law Rev. Online, 2012, doi: 10.5121/ijgca.2012.3203.
A. Labrinidis and H. V. Jagadish, "Challenges and opportunities with big data,” Proc. VLDB Endow., 2012, doi: 10.14778/2367502.2367572.
R. V. Zicari, "Big data: Challenges and opportunities,” in Big Data Computing, 2013.
P. Chandarana and M. Vijayalakshmi, "Big data analytics frameworks,” in 2014 International Conference on Circuits, Systems, Communication and Information Technology Applications, CSCITA 2014, 2014, doi: 10.1109/CSCITA.2014.6839299.
V. Marx, "The big challenges of big data,” Nature, 2013, doi: 10.1038/498255a.
Widodo, L. Nurcholis, M. Nugroho, and T. Wikaningrum, "The development model of the influence of knowledge quality towards organizational performance based on entrepreneurial learning,” Int. J. Qual. Res., 2019, doi: 10.24874/IJQR13.03-02.
K. Govindan, T. C. E. Cheng, N. Mishra, and N. Shukla, "Big data analytics and application for logistics and supply chain management,” Transportation Research Part E: Logistics and Transportation Review. 2018, doi: 10.1016/j.tre.2018.03.011.
C. Dremel, S. Overhage, S. Schlauderer, and J. Wulf, "Towards a Capability Model for Big Data Analytics,” pp. 1141–1155, 2017.
S. Sagiroglu and D. Sinanc, "Big data: A review,” in Proceedings of the 2013 International Conference on Collaboration Technologies and Systems, CTS 2013, 2013, doi: 10.1109/CTS.2013.6567202.
F. Halper and K. Krishnan, "TDWI BIG DATA MATURITY MODEL GUIDE: Interpreting Your Assessment Score,” 2014.
F. Halper and D. Stodder, "TDWI Advanced Analytics Maturity Model Guide: interpreting your assessment score,” TDWI Res., 2018.
F. Halper and D. Stodder, "TDWI Analytics Maturity Model Guide,” 2014.
S. Akter, S. F. Wamba, A. Gunasekaran, R. Dubey, and S. J. Childe, "How to improve firm performance using big data analytics capability and business strategy alignment?,” Int. J. Prod. Econ., 2016, doi: 10.1016/j.ijpe.2016.08.018.
H. Sulaiman, Z. C. Cob, and N. Ali, "Big data maturity model for Malaysian zakat institutions to embark on big data initiatives,” in 2015 4th International Conference on Software Engineering and Computer Systems, ICSECS 2015: Virtuous Software Solutions for Big Data, 2015, doi: 10.1109/ICSECS.2015.7333084.
V. Morabito and V. Morabito, "Big Data Governance,” in Big Data and Analytics, 2015.
B. Farah, "A Value Based Big Data Maturity Model,” J. Manag. Policy Pract., 2017.
I. Hausladen and M. Schosser, "Towards a maturity model for big data analytics in airline network planning,” J. Air Transp. Manag., 2020, doi: 10.1016/j.jairtraman.2019.101721.
S. F. Wamba, A. Gunasekaran, S. Akter, S. J. fan Ren, R. Dubey, and S. J. Childe, "Big data analytics and firm performance: Effects of dynamic capabilities,” J. Bus. Res., 2017, doi: 10.1016/j.jbusres.2016.08.009.
M. Mach-Król, "on Assessing an Organization'S Preparedness To Adopt and Make Use of Big Data.,” Jak Oceniać Gotowość Organ. Do Wykorzystania Big Data., 2016, doi: 10.15611/ie.2016.1.07.
T. H. Davenport and D. J. Patil, "Data scientist: The sexiest job of the 21st century,” Harv. Bus. Rev., 2012.
Authors who publish with this journal agree to the following terms:
All accepted papers will be published under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain copyright and grant the journal right of first publication. CC-BY Licenced means lets others to Share (copy and redistribute the material in any medium or format) and Adapt (remix, transform, and build upon the material for any purpose, even commercially).
