Analyzing the Global Big Data Maturity Model Domains for Better Adoption of Big Data Projects
For many years now, big data has revolutionized the world. Today, companies know that creating the most value from their data is essential for their growth. However, not all big data projects are successful; in fact, it is fundamental for companies to make the correct assessment of their capabilitie...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Regional Information Center for Science and Technology (RICeST)
2023-10-01
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| Series: | International Journal of Information Science and Management |
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| Online Access: | https://ijism.isc.ac/article_708199_46dff30cd892acb23f3d93e699febef7.pdf |
| _version_ | 1797267928925601792 |
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| author | Soukaina Mouhib Houda Anoun Mohammed Ridouani Larbi Hassouni |
| author_facet | Soukaina Mouhib Houda Anoun Mohammed Ridouani Larbi Hassouni |
| author_sort | Soukaina Mouhib |
| collection | DOAJ |
| description | For many years now, big data has revolutionized the world. Today, companies know that creating the most value from their data is essential for their growth. However, not all big data projects are successful; in fact, it is fundamental for companies to make the correct assessment of their capabilities and identify the potential problems to address before the starting point, and this is through maturity models. In previous work, we proposed a new Maturity Model and its framework to track companies’ progress toward successful big data implementation. We identified and categorized the factors influencing big data projects into six categories: strategy alignment, data, people, governance, technology, and methodology. The model provided a final score representing the readiness level for an organization to start its big data implementation. In this paper, we focus specifically on the Global Big Data Maturity assessment tool results. We analyze the importance of maturity domains and detail the final score calculation method using the AHP technique. For this research, we reached out to nineteen North African companies’ big data experts to give us input about their ongoing projects, and the steps are: (1) Collect nineteen big data expert’s ranks for each maturity domain using online forms; (2) Use these ranks alongside the Analytic Hierarchy Process method to have the domain’s weights, which were [0.173, 0.278, 0.128; 0.190; 0.064; 0.166], respectively for the domains [strategy alignment, data, people, governance, technology, and methodology]; Then (3) use the domain’s weights alongside assessment inputs, to calculate accurate weighted scores. As a result, AHP ranks show that the data dimension has the most impact on big data projects’ success, followed by strategy, methodology, governance, people, and, last but not least, technology. The framework dashboards show that most interviewed North African companies have great big data maturity levels. |
| first_indexed | 2024-04-25T01:24:23Z |
| format | Article |
| id | doaj.art-23f26620d4124df297c15e337755f652 |
| institution | Directory Open Access Journal |
| issn | 2008-8302 2008-8310 |
| language | English |
| last_indexed | 2024-04-25T01:24:23Z |
| publishDate | 2023-10-01 |
| publisher | Regional Information Center for Science and Technology (RICeST) |
| record_format | Article |
| series | International Journal of Information Science and Management |
| spelling | doaj.art-23f26620d4124df297c15e337755f6522024-03-09T06:16:36ZengRegional Information Center for Science and Technology (RICeST)International Journal of Information Science and Management2008-83022008-83102023-10-012148310210.22034/ijism.2023.1977940.0708199Analyzing the Global Big Data Maturity Model Domains for Better Adoption of Big Data ProjectsSoukaina Mouhib0Houda Anoun1Mohammed Ridouani2Larbi Hassouni3Ph.D. Student, RITM Laboratory, Hassan II University, Casablanca, Morocco.Professor., RITM Laboratory, Hassan II University, Casablanca, Morocco.Professor., RITM Laboratory, Hassan II University, Casablanca, Morocco.Professor, RITM Laboratory Hassan II University,Casablanca, MoroccoFor many years now, big data has revolutionized the world. Today, companies know that creating the most value from their data is essential for their growth. However, not all big data projects are successful; in fact, it is fundamental for companies to make the correct assessment of their capabilities and identify the potential problems to address before the starting point, and this is through maturity models. In previous work, we proposed a new Maturity Model and its framework to track companies’ progress toward successful big data implementation. We identified and categorized the factors influencing big data projects into six categories: strategy alignment, data, people, governance, technology, and methodology. The model provided a final score representing the readiness level for an organization to start its big data implementation. In this paper, we focus specifically on the Global Big Data Maturity assessment tool results. We analyze the importance of maturity domains and detail the final score calculation method using the AHP technique. For this research, we reached out to nineteen North African companies’ big data experts to give us input about their ongoing projects, and the steps are: (1) Collect nineteen big data expert’s ranks for each maturity domain using online forms; (2) Use these ranks alongside the Analytic Hierarchy Process method to have the domain’s weights, which were [0.173, 0.278, 0.128; 0.190; 0.064; 0.166], respectively for the domains [strategy alignment, data, people, governance, technology, and methodology]; Then (3) use the domain’s weights alongside assessment inputs, to calculate accurate weighted scores. As a result, AHP ranks show that the data dimension has the most impact on big data projects’ success, followed by strategy, methodology, governance, people, and, last but not least, technology. The framework dashboards show that most interviewed North African companies have great big data maturity levels.https://ijism.isc.ac/article_708199_46dff30cd892acb23f3d93e699febef7.pdfmcdmahpfuzzy ahpbig data maturity modelbig data projects |
| spellingShingle | Soukaina Mouhib Houda Anoun Mohammed Ridouani Larbi Hassouni Analyzing the Global Big Data Maturity Model Domains for Better Adoption of Big Data Projects International Journal of Information Science and Management mcdm ahp fuzzy ahp big data maturity model big data projects |
| title | Analyzing the Global Big Data Maturity Model Domains for Better Adoption of Big Data Projects |
| title_full | Analyzing the Global Big Data Maturity Model Domains for Better Adoption of Big Data Projects |
| title_fullStr | Analyzing the Global Big Data Maturity Model Domains for Better Adoption of Big Data Projects |
| title_full_unstemmed | Analyzing the Global Big Data Maturity Model Domains for Better Adoption of Big Data Projects |
| title_short | Analyzing the Global Big Data Maturity Model Domains for Better Adoption of Big Data Projects |
| title_sort | analyzing the global big data maturity model domains for better adoption of big data projects |
| topic | mcdm ahp fuzzy ahp big data maturity model big data projects |
| url | https://ijism.isc.ac/article_708199_46dff30cd892acb23f3d93e699febef7.pdf |
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