| Title |
The use of readiness assessment for big data projects |
| ID_Doc |
44465 |
| Authors |
Barham, H; Daim, T |
| Title |
The use of readiness assessment for big data projects |
| Year |
2020 |
| Published |
|
| DOI |
10.1016/j.scs.2020.102233 |
| Abstract |
Big data projects, including smart-city-related big data projects, are facing an alarmingly high percentage of failure. The reasons behind this phenomenon and the lessons learned from it are well researched. However, big data projects are still failing, as there is a lack of effective models to leverage the lessons learned from previous projects to evaluate an organization's readiness for a big data project. In this paper, the authors introduce a novel readiness assessment model. This model leverages the lessons learned from previous projects and the experience of experts to be better prepared for an upcoming smart-cityrelated big data project. Cities can use the model to evaluate their readiness for this type of project in a structured and comprehensive way that will allow for higher chances of conducting a successful big data project. To develop the model, hierarchical decision modeling (HDM) and expert judgment quantification were used to provide the categorization and relative ranking of factors that influence smart-city-related big data projects. HDM is an effective way to understand the relationship between multiple factors and allows for expert panels to prioritize those factors. Moreover, desirability functions were used to extend the understanding of the factors' dynamics and what needs to be done to better prepare for the challenges associated with each factor. Finally, the model was tested by applying it to several smart-city-related big data projects to show its value. This research highlights the importance of readiness assessment for conducting big data projects and provides a readiness assessment model that cities can use to prepare for an upcoming big data project. |
| Author Keywords |
Technology management; City readiness; Big data projects |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000566902900008 |
| WoS Category |
Construction & Building Technology; Green & Sustainable Science & Technology; Energy & Fuels |
| Research Area |
Construction & Building Technology; Science & Technology - Other Topics; Energy & Fuels |
| PDF |
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