scholarly journals Project Management Volume, Velocity, Variety: A Big Data Dynamics Approach

2021 ◽  
Author(s):  
Fredrik Kockum ◽  
Nicholas Dacre
Keyword(s):  
Big Data ◽  
Project Management ◽  
Management Volume ◽  
Volume Velocity ◽  
Data Dynamics

scholarly journals Project Management Volume, Velocity, Variety: A Big Data Dynamics Approach

2021 ◽  
Author(s):  
Fredrick Kockum ◽  
Nicholas Dacre
Keyword(s):  
Big Data ◽  
Mixed Method ◽  
Qualitative Data ◽  
Qualitative Interviews ◽  
Research Approach ◽  
Mixed Method Research ◽  
Volume Velocity ◽  
Data Dynamics ◽  
Management Processes ◽  
Dynamics Modelling

The era of Big Data has provided business organisations opportunities to improve their management processes. This developmental paper is adopting a mixed-method research approach where qualitative data will underpin a quantitative questionnaire. The early insights are based on an initial eleven qualitative interviews and conceptualised in the following three statements: (i) Project practitioners need to increase their data literacy; (ii) Project practitioners are not utilising the available Big Data based on the 3 Vs; Volume, Velocity and Variety; (iii) Project practitioners need to utilise the structured available data to augment the decision-making process to represent the complex environment of Big Data, the study adopts Complexity Theory as a theoretical framework. When completed, the research will demonstrate the results through System Dynamics modelling.

scholarly journals Research Status and Challenges of Data-Driven Construction Project Management in the Big Data Context

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Yao Huang ◽  
Qian Shi ◽  
Jian Zuo ◽  
Feniosky Pena-Mora ◽  
Jindao Chen
Keyword(s):  
Big Data ◽  
Project Management ◽  
Information Technologies ◽  
Construction Project ◽  
Data Driven ◽  
Theoretical Research ◽  
Construction Project Management ◽  
American Society ◽  
It Application ◽  
Data Context

The construction industry is facing a data tsunami, while emerging information technologies (IT) show great potential for the effective processing of these data or information. However, a comprehensive review for technological change, the resulting process, and organizational changes in the Big Data context, especially from the angle of whole lifecycle of construction project, is lacking. To fill the void, related works published in the databases of Web of Science, Science Direct, and American Society of Civil Engineers library are systematically reviewed. The general trend in emerging IT application in terms of construction project management (CPM) phases, technology and application, and research topics are revealed. Following this analysis, the particularized proposals in relation to each of the main topics within CPM is discussed. Furthermore, according to the advances and limitations of the current literature, corresponding future agendas such as the implementation of comprehensive data-driven CPM scenario are proposed to bridge the gaps between theoretical research and practical demands.

Big Data Analytics With Service-Oriented Architecture

2017 ◽  
pp. 216-234 ◽  
Author(s):  
Triparna Mukherjee ◽  
Asoke Nath
Keyword(s):  
Big Data ◽  
Service Oriented Architecture ◽  
Big Data Analytics ◽  
Dominant Mode ◽  
Volume Velocity ◽  
It Systems ◽  
Trading Partners ◽  
Integration Of Services ◽  
Service Oriented ◽  
Data Explosion

This chapter focuses on Big Data and its relation with Service-Oriented Architecture. We start with the introduction to Big Data Trends in recent times, how data explosion is not only faced by web and retail networks but also the enterprises. The notorious “V's” – Variety, volume, velocity and value can cause a lot of trouble. We emphasize on the fact that Big Data is much more than just size, the problem that we face today is neither the amount of data that is created nor its consumption, but the analysis of all those data. In our next step, we describe what service-oriented architecture is and how SOA can efficiently handle the increasingly massive amount of transactions. Next, we focus on the main purpose of SOA here is to meaningfully interoperate, trade, and reuse data between IT systems and trading partners. Using this Big Data scenario, we investigate the integration of Services with new capabilities of Enterprise Architectures and Management. This has had varying success but it remains the dominant mode for data integration as data can be managed with higher flexibility.

Perspectives of Big Data Analysis for Knowledge Generation in Project Management Contexts

2015 ◽  
pp. 1-18 ◽  
Author(s):  
George Leal Jamil ◽  
Luiz Fernando Magalhães Carvalho
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Project Management ◽  
Management Practices ◽  
Knowledge Generation ◽  
Process Data ◽  
Management Processes ◽  
Final Study ◽  
Study Case ◽  
Applicable Knowledge

Knowledge generation for Project Management (PM) is a critical modern issue. Projects are a complex, inter-related set of tasks that aim to provide a service or product in a controlled, managed way. In these scenarios, there is a continuous producing of data and information, which is a potential situation for Knowledge Management (KM) interaction. This chapter evaluates aspects and factors on how it is possible to process data and information, in order to generate applicable knowledge to improve project management. A specific consideration is to understand the observation of contexts of huge amounts of data—known nowadays as Big Data—and its potential knowledge generation for project management, as presented in the final study case. A better comprehension on how knowledge management practices, applied to Big Data contexts, can improve project management processes is the main objective in this chapter.

Big Data Multimedia Mining: Feature Extraction Facing Volume, Velocity, and Variety

2019 ◽  
pp. 61-87 ◽  
Author(s):  
Vedhas Pandit ◽  
Shahin Amiriparian ◽  
Maximilian Schmitt ◽  
Amr Mousa ◽  
Björn Schuller
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Volume Velocity

scholarly journals Big data, technology capability and construction project quality: a cross-level investigation

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Linhua Sang ◽  
Mingchuan Yu ◽  
Han Lin ◽  
Zixin Zhang ◽  
Ruoyu Jin
Keyword(s):  
Big Data ◽  
Quality Improvement ◽  
Project Management ◽  
Construction Project ◽  
Short Time Interval ◽  
Quality Performance ◽  
Content Type ◽  
Project Quality ◽  
Technology Capability ◽  
The Relationship

PurposeEmbracing big data has been at the forefront of research for project management. Although there is a consensus that the adoption of big data has significantly positive impact on project performance, far less is known about how this innovative information technology becomes an effective driver of construction project quality improvement. This study aims to better understand the mechanism and conditions under which big data can effectively improve project quality performance.Design/methodology/approachAdopting Chinese construction enterprises as samples, the theoretical framework proposed in this paper is verified by the empirical results of the two-level hierarchical linear model. The moderated mediation analysis is also conducted to test the hypotheses. Finally, the empirical findings are validated by a comparative case study.FindingsThe results show that big data facilitates the development of technology capability, which further produces remarkable quality performance. That is, a project team's technology capability acts as a mediator in the relationship between organizational adaptability of big data and predictive analytics and project quality performance. It is also observed that two types of project team interdependence (goal and task interdependence) positively moderate the mediation effect.Research limitations/implicationsThe questionnaire study from China only represents the relationship within a short time interval in the current context. Future studies should apply longitudinal designs to properly test the causality and use multiple data sources to ensure the validity and robustness of the conclusions.Practical implicationsThe value of big data in terms of quality improvement could not be determined in a vacuum; it also depends on the internal capability development and elaborate design of project governance.Originality/valueThis study provides an extension of the existing big data studies and fuels the ongoing debate on its actual outcomes in project management. It not only clarifies the direct effect of big data on project quality improvement but also identifies the mechanism and conditions under which the adoption of big data can play an effective role.

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