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This viewpoint study aims to make the case that the field of knowledge management (KM) must respond to the significant changes that big data/analytics is bringing to operationalizing the production of organizational data and information.

This study expresses the opinions of the guest editors of “Does Big Data Mean Big Knowledge? Knowledge Management Perspectives on Big Data and Analytics”.

A Big Data/Analytics-Knowledge Management (BDA-KM) model is proposed that illustrates the centrality of knowledge as the guiding principle in the use of big data/analytics in organizations.

This is an opinion piece, and the proposed model still needs to be empirically verified.

It is suggested that academics and practitioners in KM must be capable of controlling the application of big data/analytics, and calls for further research investigating how KM can conceptually and operationally use and integrate big data/analytics to foster organizational knowledge for better decision-making and organizational value creation.

The BDA-KM model is one of the early models placing knowledge as the primary consideration in the successful organizational use of big data/analytics.

To understand how the most successful organizations use big data and analytics innovate, researchers studied 341 respondents’ usage of big data and analytics tools for innovation.

Researchers asked about innovation goals, barriers to innovation, metrics used to measure innovation outcomes, treatment and types of innovation projects and the role of big data and analytics in innovation processes.

Three distinct groups emerged: Leaders, Strivers and Strugglers. Leaders are markedly different as a group: they innovate using big data and analytics within a structured approach, and they focus in particular on collaboration.

Respondents were from the 2014 IBM Innovation Survey. We conducted cluster analysis with 81 variables. The three cluster solution was determined deploying latent class analysis (LCA), a family of techniques based around clustering and data reduction for segmentation projects. It uses a number of underlying statistical models to capture differences between observed data or stimuli in the form of discrete (unordered) population segments; group segments; ordered factors (segments with an underlying numeric order); continuous factors; or mixtures of the above.

Leaders don’t just embrace analytics and actionable insights; they take them to the next level, integrating analytics and insights with innovation. Leaders follow three basic strategies that center on data, skills and tools and culture: promote excellent data quality and accessibility; make analytics and innovation a part of every role; build a quantitative innovation culture.

The research found that leaders leverage big data and analytics more effectively over a wider range of organizational processes and functions. They are significantly better at leveraging big data and analytics throughout the innovation process – from conceiving new ideas to creating new business models and developing new products and services.

Although published research is limited to big data, some research focuses on the challenges that companies face in implementing big data projects. Specifically, in the field of information systems, researchers realize that the success of Big Data projects is not only the result of data and analytics tools and processes, but also includes broader aspects. To address this issue, people have come up with a perception of big data analytics capabilities, often defined as the ability of businesses to take advantage of data management, infrastructure, and talent to turn business into competencies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

The relationship between analytics and organizational performance has been the subject of the extant research. Prior studies have highlighted the direct influence of analytics on organizational performance. For example, big data analytics capabilities are significantly correlated with market performance and operational performance. The mechanisms through which analytics affect organizations were also examined from various perspectives.

The paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

This study investigates the impact of big data analytics capabilities on green supply chain performance. Moreover, it assesses the mediating effect of the green innovation and moderating effect of technological intensity.

This study is based on primary data that were collected from the food and beverages manufacturing sector operating in Jordan. A total of 420 samples were used for the final data analysis. Data analysis was performed via structural equation modeling (SEM) using SmartPLS 3.3.9.

The results of the data analysis supported a positive relationship between big data analytics capabilities and the green supply chain performance as well as a mediating effect of green innovation. It was confirmed that technological intensity moderated the relationship of green innovation on green supply chain performance.

The study faced many limitations such as the method of collecting primary data, which relied on a questionnaire only and the use of cross-sectional data, as well as studying one context and in one country.

The findings can guide managers and policymakers in the Jordanian food and beverage manufacturing sector on how to manage organizational capabilities related to big data analytics to enhance green supply chain performance and improve green innovation in these firms.

This study developed a theoretical and empirical model to investigate the relationship between big data analytics capabilities, green innovation, technological intensity and green supply chain performance. This study offers new theoretical and managerial contributions that add value to the supply chain management and innovation literature by testing the moderated mediation model of these constructs in the food and beverages manufacturing sector in Jordan.

The purpose of this study is to identify the impact of intellectual capital on the innovation performance of the Jordanian banking sector and identify the moderating role of big data analytics.

For this study's purposes, 333 questionnaires were analysed. Convergent validity, discriminant validity and reliability tests were performed through structural equation modelling (SEM) in the Smart-PLS program. A bootstrapping technique was used to analyse the data.

Empirical results showed that each of the components of intellectual capital and big data analytics explains 63.5% of the variance in innovation performance and that all components of intellectual capital have a statistically significant impact on innovation performance. The results also revealed that the relationship between structural capital and innovation performance is moderated through big data analytics.

This cross-sectional study provides a snapshot at a given moment in time, a methodological limitation that affects the generalisation of its results, and the results are limited to one country.

This study promotes the idea of focusing on components of intellectual capital to enhance innovation performance in the Jordanian banking sector and knowing the effect of big data analytics in this relationship.

This study makes recommendations for financial policymakers to improve the effectiveness of intellectual capital practices and innovation performance in the context of big data analytics.

This study has important implications for leaders in the Jordanian banking sector, in general, as the study highlights the importance of intellectual capital to enhance the innovation performance, especially in light of the big data analytics in this sector, and thus increase the innovative capabilities of this banks, which leads to an increase in the level of innovation.

The importance of integrating cloud-based big data analytics software with enterprise resource planning (ERP) platforms is not clearly understood. Specifically, this study aims to look into firms that implemented SAP during the boom of ERP implementations. Further, this study aims to look into the type of cloud-based big data analytics software that those firms installed when cloud-based packages started to be available.

This study specifically looks at productivity and the sources of productivity, such as technical progress and efficiency change, using a non-parametric approach that does not constrain the analysis to any production function.

This study found that by the time cloud-based big data analytics software started to be available, SAP-adopters already had a competitive advantage over the non-SAP adopters manifested through productivity and specifically through technology and not efficiency. Later, when the same firms decided to integrate their ERP platforms with cloud-based big data analytics software, the firms that had installed SAP already had an initial advantage over the non-SAP-adopters.

In support of the theory of technology organization environment (Tornatzky and Fleisher, 1990) and Posner's theoretical framework (Posner, 1961), a cloud-based big data analytics software will not change the relative position that firms have in the industry, so a cloud-based big data analytics software by itself will not provide a competitive advantage over competitors. Still, it will ensure that the preliminary technological gap that SAP-adopters already had is not magnified.

Knowing the sources of productivity improvement and technological improvements will give managers greater leverage when negotiating budgets, negotiating long-term contracts in better terms and in the decision process.

This study fills a research gap by looking into the implementation of a cloud-based big data analytics software with ERP.

The rise of big data and analytics companies has significantly changed the business playground. Big data and the use of data analytics are being adopted more frequently, especially in companies that are looking for new methods to develop smarter capabilities and tackle challenges in the dynamic processes. Working with big data and applying a series of data analysis techniques require strong multidisciplinary skills and knowledge of statistics, econometrics, computer science, data mining, law and business ethics, etc. Higher education institutions (HEIs) are concerned by this phenomenon which is also changing learning needs and require a reorientation toward the development of novel approaches and advancements in their programs. The purpose of this paper is to introduce and define big data analytics as having an immense potential for generating value for businesses. In this context, one prominent strategy is to integrate big data analytics in educational programs to enrich student’ understanding of the role of big data, especially those who want to explore their entrepreneurial ways and improve their effectiveness. So, the main purpose of this article consists, on the one hand, in why HEIs must carefully think about how to provide powerful learning tools and open a new entrepreneurship area in this field, and, why, on the other hand, future entrepreneurs (students) have to use data analytics and how they can integrate, operationally, analytics methods to extract value and enhance their professional capabilities.

The author has established an expert viewpoint to discuss the notion of data analytics, identify new ways and re-think what really is new, for both entrepreneurs and HEIs, in the area of big data. This study provides insights into how students can improve their skills and develop new business models through the use of IT tools and by providing the ability to analyze data. This can be possible by bringing the tool of analytics into the higher educational learning system. New analytics methods have to help find new ways to process data and make more intelligent decisions. A brief overview of data analytics and its roles in the context of entrepreneurship and the rise of data entrepreneur is then presented. The paper also outlines the role of educational programs in helping address big data challenges and transform possibilities into opportunities. The key factors of implementing an efficient big data analytics in learning programs, to better orientate and guide students’ project idea, are also explored. The paper concludes with suggestions for further research and limitations of the study.

The findings in this paper suggest that analytics can be of crucial importance for student entrepreneurial practice if correctly aligned with their business processes and learning needs and can also lead to significant improvement in their performance and quality of the decisions they make. The added value of big data is the ability to identify useful data and turn it into usable information by identifying patterns and exploiting new algorithms, tools and new project solutions. So, the move toward the introduction of big data and analytics tools in higher education addresses how this new opportunity can be operationalized.

There are some limitations to this research paper. The research findings have significant implications for HEIs in the field of analytics (mathematics and computer science), and thus, it is not generalizable with any further context. Also, the viewpoint is centered on the data analytics process as a value generator for entrepreneurial opportunities.

This research can be considered as a contribution with literature about educational quality, entrepreneurship and big data analytics. This study describes that new analytics thinking and computational skills are needed for the newer generation of entrepreneurs to handle the challenges of big data. But, preparing them to capture, analyze, store and manage the large amounts of data available today – so they can see value in data – is not just about implementing and using new technologies. This is also, about, a dynamic, operational and modern educational learning process from which a student can extract the maximum benefit. In another words: How to make new opportunities from these data? Which data to select for the analysis? and How to efficiently apply analytical techniques to generate value?

Big data analytics is a set of procedures and technologies that entails new forms of integration to uncover large unknown values from large data sets that are various, complex and of an immense scale. The use of big data analytics is generally considered to improve organisational performance. However, this depends on capabilities of different organisations to provide the resources required for big data analytics. This study aims to investigate the influence of big data analytics on organisational performance of Technical University of Kenya (TUK) and Strathmore University (SU).

This study was conducted as a mixed method research to enable a deep understanding of the concept. Primary data was collected through structured questionnaires and interviews with clientele and information communication technology staff from the TUK and SU, both in Nairobi, Kenya. Secondary data was collected through interviews and questionnaires. Data was analysed and presented using descriptive statistics.

The findings revealed that most of the variables of organisational performance such as innovativeness, creativeness, effectiveness, productiveness and efficiency are affected positively by conducting big data analytics in both institutions. The results demonstrate that the TUK showed a negative relationship between big data analytics and competiveness and profitability while SU showed a positive relationship between the two variables. In terms of regression analysis, the findings revealed that SU showed a good relationship between independent and dependant variables while the TUK had a weak influence.

This study is original in terms of its subject matter, scope and application.

The purpose of this paper is to analyze the views and capabilities of librarians for the implementation of Big Data analytics in academic libraries of Pakistan. The study also sets out to check the relationship between the required skills of librarians and the application of Big Data analytics.

A survey was conducted to gather the required data from the targeted audience. The targeted population of the study was Head/In charge library managers of Pakistani university libraries, which were 173 in total. All the respondents (academic librarians) were invited through an e-mail to respond to the survey voluntarily. Out of 173 respondents from higher education commission of Pakistan chartered university libraries, 118 librarians (68.2 percent) completed the survey that was finally considered, and after checking data, recommendation for analysis was made. To analyze the collected data, statistical technique Pearson correlation was applied using statistical package for social science version 25 to know the strength of the mutual correlation of variables.

The findings of the study show a strong correlation between the required competencies and skills of librarians for the implementation of Big Data analytics in academic libraries. In all variables of the study, the correlation was highly significant, except two of the variables, including “concept of Big Data” and “different forms of data.” The study also reveals that most of the respondents were well aware of the concept of Big Data analytics. Moreover, they were using a large amount of data to carry out various library operations, including the acquisition, preservation, curation and analysis of data.

This study is significant in the sense that it fills a substantial gap in the literature regarding the perspective of librarians on Big Data analytics.

The purpose of this paper is to examine the role of big data text analytics as an enabler of knowledge management (KM). The paper argues that big data text analytics represents an important means to visualise and analyse data, especially unstructured data, which have the potential to improve KM within organisations.

The study uses text analytics to review 196 articles published in two of the leading KM journals – Journal of Knowledge Management and Journal of Knowledge Management Research & Practice – in 2013 and 2014. The text analytics approach is used to process, extract and analyse the 196 papers to identify trends in terms of keywords, topics and keyword/topic clusters to show the utility of big data text analytics.

The findings show how big data text analytics can have a key enabler role in KM. Drawing on the 196 articles analysed, the paper shows the power of big data-oriented text analytics tools in supporting KM through the visualisation of data. In this way, the authors highlight the nature and quality of the knowledge generated through this method for efficient KM in developing a competitive advantage.

The research has important implications concerning the role of big data text analytics in KM, and specifically the nature and quality of knowledge produced using text analytics. The authors use text analytics to exemplify the value of big data in the context of KM and highlight how future studies could develop and extend these findings in different contexts.

Results contribute to understanding the role of big data text analytics as a means to enhance the effectiveness of KM. The paper provides important insights that can be applied to different business functions, from supply chain management to marketing management to support KM, through the use of big data text analytics.

The study demonstrates the practical application of the big data tools for data visualisation, and, with it, improving KM.