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Organizations are beginning to realize the potential benefits of big data and harnessing all of the data they are creating. However, a major impediment for many organizations is understanding where to start in big data and analytics implementation. In many respects, starting a successful implementation is not much different from any other project managed within the organization. The major stumbling block is knowing what questions to ask to get things going. This paper aims to help libraries and information organizations that are considering big data and analytics implementation to begin their journey by following a checklist of eight aspects to be considered in the development of a big data and analytics strategy.

The eight aspects to consider in big data and analytics implementation were developed using a combination of existing project management common knowledge, consultant recommendations and real-life experiences.

Organizations considering big data and analytics implementation need to explore aspects related to the data they have, what organizational problems they are trying to solve, how data governance will work in the new environment, as well as how they will define success in terms of their implementation. These are in addition to the technical issues one would normally expect in a systems implementation.

While there have been many articles written about the implementation of big data and analytics in organizations, most of these focus on technical issues rather than managerial and organizational concerns. In addition, none of these other articles have been from the perspective of library and information science. In this article, the focus is specifically on how information professionals may approach this problem.

Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.

This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.

Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.

The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.

The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.

The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.

The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.

Today, Big Data plays an imperative role in the creation, maintenance and loss of cyber assets of organisations. Research in connection to Big Data and cyber asset management is embryonic. Using evidence, the purpose of this paper is to argue that asset management in the context of Big Data is punctuated by a variety of vulnerabilities that can only be estimated when characteristics of such assets like being intangible are adequately accounted for.

Evidence for the study has been drawn from interviews of leaders of digital transformation projects in three organisations that are within the insurance industry, natural gas and oil, and manufacturing industries.

By examining the extant literature, the authors traced the type of influence that Big Data has over asset management within organisations. In a context defined by variability and volume of data, it is unlikely that the authors will be going back to restricting data flows. The focus now for asset managing organisations would be to improve semantic processors to deal with the vast array of data in variable formats.

Data used as evidence for the study are based on interviews, as well as desk research. The use of real-time data along with the use of quantitative analysis could lead to insights that have hitherto eluded the research community.

There is a serious dearth of the research in the context of innovative leadership in dealing with a threatened asset management space. Interpreting creative initiatives to deal with a variety of risks to data assets has clear value for a variety of audiences.

This paper aims to provide a roadmap for organizations to build big data projects and reap the most rewards out of their data. It covers all aspects of big data project implementation, from data collection to final project evaluation.

In each stage of the proposed roadmap, we introduce different sets of information and communications technology platforms and tools to assist IT professionals and managers in gaining a comprehensive understanding of the methods and technologies involved and in making the best use of them. The authors also complete the picture by illustrating the process through different real-world big data projects implementations.

By adopting the proposed roadmap, companies and organizations willing to establish an effective and rewarding big data solution can tackle all implementation challenges in each stage of their big data project setup: from strategy elaboration to final project evaluation. Their expectations of privacy and security are also baked, in advance, into the big data project design.

While technologies to build and run big data projects have started to mature and proliferate over the last couple of years, exploiting all potentials of big data is still at a relatively early stage. The value of this paper consists in providing a clear and systematic methodology to move businesses and organizations from an opinion-operated era where humans’ skills are a necessity to a data-driven and smart era where big data analytics plays a major role in discovering unexpected insights in the oceans of data routinely generated or collected.

The paper aims to help enterprises gain valuable knowledge about big data implementation in practice and improve their information management ability, as they accumulate experience, to reuse or adapt the proposed method to achieve a sustainable competitive advantage.

Guided by the theory of technological frames of reference (TFR) and transaction cost theory (TCT), this paper describes a real-world case study in the banking industry to explain how to help enterprises leverage big data analytics for changes. Through close integration with bank's daily operations and strategic planning, the case study shows how the analytics team frame the challenge and analyze the data with two analytic models – customer segmentation (unsupervised) and product affinity prediction (supervised), to initiate the adoption of big data analytics in precise marketing.

The study reported relevant findings from a longitudinal data analysis and identified some key success factors. First, non-technical factors, for example intuitive analytics results, appropriate evaluation baseline, multiple-wave implementation and selection of marketing channels critically influence big data implementation progress in organizations. Second, a successful campaign also relies on technical factors. For example, the clustering analytics could promote customers' response rates, and the product affinity prediction model could boost efficient transaction and lower time costs.

For theoretical contribution, this paper verified that the outstanding characteristics of online mutual fund platforms brought up by Nagle, Seamans and Tadelis (2010) could not guarantee organizations' competitive advantages from the aspect of TCT.

The purpose of this research is to develop a better understanding of the hurdles in implementing Lean Six Sigma (LSS) for operational excellence in digital emerging technology companies.

We have conducted case studies of LSS implementations in six US-based companies in the digital emerging technology industry.

Critical success factors (CSF) for LSS implementations in digital emerging technology companies are: (1) organizational leadership that is engaged to the implementation, (2) LSS methodology that is rebranded to fit existing shared values in the organization, (3) restructuring of the traditional LSS training program to include a more incremental, prioritized, on-the-job training approach and (4) a modified LSS project execution methodology that includes (a) condensing the phases and tools applied in LSS projects and (b) adopting more iterative project management methods compared to the standard phased LSS project approach.

The qualitative nature of our analysis and the geographic coverage of our sample limit the generalizability of our findings.

Implications comprise the awareness and knowledge of critical success factors and LSS methodology modifications specifically relevant for digital emerging technology companies or companies that share similarities in terms of focus on product development, innovation and growth, such as R&D departments in high-tech manufacturing companies.

Research on industry-specific enablers for successful LSS implementation in the digital emerging technology industry is virtually absent. Our research informs practitioners on how to implement LSS in this and alike industries, and points to aspects of such implementations that are worthy of further attention from the academic community.

The purpose of this essay is to illustrate how project management “pull” and AI or analytics technology “push” are likely to result in incremental and disruptive evolution of project management capabilities and practices.

This paper is written as a critical essay reflecting the experience and reflections of the author with many ideas drawn from and extending selected items from project management, artificial intelligence (AI) and analytics literatures.

Neither AI nor sophisticated analytics is likely to elicit hands on attention from project managers, other than those producing AI or analytics-based artifacts or using these tools to create their products and services. However, through the conduit of packaged software support for project management, new tools and approaches can be expected to more effectively support current activities, to streamline or eliminate activities that can be automated, to extend current capabilities with the availability of increased data, computing capacity and mathematically based algorithms and to suggest ways to reconceive how projects are done and whether they are needed.

This essay includes projections of possible, some likely and some unlikely, events and states that have not yet occurred. Although the hope and purpose are to alert readers to the possibilities of what may occur as logical extensions of current states, it is improbable that all such projections will come to pass at all or in the way described. Nonetheless, consideration of the future ranging from current trends, the interplay among intersecting trends and scenarios of future states can sharpen awareness of the effects of current choices regarding actions, decisions and plans improving the probability that the authors can move toward desired rather than undesired future states.

Project managers not involved personally with creating AI or analytics products can avoid mastering detailed skill sets in AI and analytics, but should scan for new software features and affordances that they can use enable new levels of productivity, net benefit creation and ability to sleep well at night.

This essay brings together AI, analytics and project management to imagine and anticipate possible directions for the evolution of the project management domain.

This systematic literature review aims to explore the adoption, global acceptance and implementation of human resources (HR) analytics (HRA) by reviewing literature on the subject. HRA adoption can assist HR professionals in managing complex procedures and making strategic human resource management (SHRM) decisions more effectively. The study also aims to identify the applications of analytics in various disciplines of management.

The review is conducted using a domain-based structured literature review (SLR), emphasizing the diffusion of innovative thinking and the adoption process of HRA among early adopters. The philosophical stances are analyzed with the combination of research onion model and PRISMA protocol. Secondary data are gathered from published journals, books, case studies, conference proceedings, web pages and media stories as the primary source of information.

The study finds that skilled professionals and management assistance can significantly impact adoption intentions, enabling professionals to deal with analytics. The examples and analytical models provided by early adopters allow managers to manage complex processes and make SHRM decisions.

The study suggests that the lack of use of quantitative techniques is a key limitation and should be considered in future studies. Despite the rise in the number of research papers on HRA, its application in the workplace remains limited.

This research can assist managers in implementing HRA and help resolve complex and inefficient processes, making SHRM decisions.

This study adds to the existing body of knowledge on how HRA can aid a company's efficacy and performance and can be considered one of the first to link adoption and HRA.

Recently, Vietnamese enterprises have begun to realize the potential benefits of big data and harnessing all of the data they have been creating. Experiencing the crisis time of the COVID-19 pandemic, they could apprehend more and more benefits of digitalizing trend. However, a big problem for many Vietnamese enterprises is understanding where to begin in implementing big data and analytics. The study’s main objective is to investigate the impact factors of implementing big data and analytics in Vietnamese enterprises post-COVID-19 pandemic.

The study is exploratively conducted with a quantitative survey approach and uses purposive techniques in collecting data. The sample focuses on Vietnamese enterprises which have experience with big data and analytics.

This study intended to highlight some aspects to consider when implementing big data and analytics in Vietnamese enterprises post-COVID-19 pandemic.

To the best of the author’s knowledge, this study is the first academic paper to study Vietnamese enterprises’ considerations of big data and analytics post-COVID-19 pandemic.

In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.