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Article
Publication date: 13 February 2017

David J. Pauleen and William Y.C. Wang

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…

Abstract

Purpose

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.

Design/methodology/approach

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

Findings

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.

Research limitations/implications

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

Practical implications

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.

Originality/value

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.

Details

Journal of Knowledge Management, vol. 21 no. 1
Type: Research Article
ISSN: 1367-3270

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Article
Publication date: 21 September 2015

Anthony Marshall, Stefan Mueck and Rebecca Shockley

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Details

Strategy & Leadership, vol. 43 no. 5
Type: Research Article
ISSN: 1087-8572

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Article
Publication date: 14 August 2018

Kar Hooi Tan

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…

Abstract

Purpose

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.

Design/methodology/approach

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

Findings

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.

Practical implications

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

Originality/value

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.

Details

Strategic Direction, vol. 34 no. 8
Type: Research Article
ISSN: 0258-0543

Keywords

Content available
Article
Publication date: 20 July 2021

Rosita Capurro, Raffaele Fiorentino, Stefano Garzella and Alessandro Giudici

The purpose of this paper is to analyze, from a dynamic capabilities perspective, the role of big data analytics in supporting firms' innovation processes.

Abstract

Purpose

The purpose of this paper is to analyze, from a dynamic capabilities perspective, the role of big data analytics in supporting firms' innovation processes.

Design/methodology/approach

Relevant literature is reviewed and critically assessed. An interpretive methodology is used to analyze empirical data from interviews of big data analytics experts at firms within digitally related sectors.

Findings

This study shows how firms leverage big data to gain “richer” and “deeper” data at the inter-sections between the digital and physical worlds. The authors provide evidence for the importance of counterintuitive strategies aimed at developing innovative products, services or solutions with characteristics that may initially diverge, even significantly, from established customer/user needs.

Practical implications

The authors’ findings offer insights to help practitioners manage innovation processes in the physical world while taking investments in big data analytics into account.

Originality/value

The authors provide insights into the evolution of scholarly research on innovation directed toward opportunities to create a competitive advantage by offering new products, services or solutions diverging, even significantly, from established customer demand.

Details

European Journal of Innovation Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1460-1060

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Article
Publication date: 4 February 2021

Karen Mcbride and Christina Philippou

Accounting education is re-inventing itself as technology impacts the practical aspects of accounting in the real world and education tries to keep up. Big Data and data

Abstract

Purpose

Accounting education is re-inventing itself as technology impacts the practical aspects of accounting in the real world and education tries to keep up. Big Data and data analytics have begun to influence elements of accounting including audit, accounting preparation, forensic accounting and general accountancy consulting. The purpose of this paper is to qualitatively analyse the current skills provision in accounting Masters courses linked to data analytics compared to academic and professional expectations of the same.

Design/methodology/approach

The academic expectations and requirements of the profession, related to the impact of Big Data and data analytics on accounting education were reviewed and compared to the current provisions of this accounting education in the form of Masters programmes. The research uses an exploratory, qualitative approach with thematic analysis.

Findings

Four themes were identified of the skills required for the effective use of Big Data and data analytics. These were: questioning and scepticism; critical thinking skills; understanding and ability to analyse and communicating results. Questioning and scepticism, as well as understanding and ability to analyse, were frequently cited explicitly as elements for assessment in various forms of accounting education in the Masters courses. However, critical thinking and communication skills were less explicitly cited in these accounting education programmes.

Research limitations/implications

The research reviewed and compared current academic literature and the requirements of the professional accounting bodies with Masters programmes in accounting and data analytics. The research identified key themes relevant to the accounting profession that should be explicitly developed and assessed within accounting education for Big Data and data analytics at both university and professional levels. Further analysis of the in-depth curricula, as opposed to the explicitly stated topic coverage, could add to this body of research.

Practical implications

This paper considers the potential combined role of professional qualification examinations and master’s degrees in skills provision for future practitioners in accounting and data analysis. This can be used to identify the areas in which accounting education can be further enhanced by focus or explicit mention of skills that are both developed and assessed within these programmes.

Social implications

The paper considers the interaction between academic and professional practice in the areas of accounting education, highlighting skills and areas for development for students currently considering accounting education and data analytics.

Originality/value

While current literature focusses on integrating data analysis into existing accounting and finance curricula, this paper considers the role of professional qualification examinations with Masters degrees as skills provision for future practitioners in accounting and data analysis.

Details

Accounting Research Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1030-9616

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Article
Publication date: 6 May 2020

Lucy Wachera Kibe, Tom Kwanya and Ashah Owano

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…

Abstract

Purpose

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).

Design/methodology/approach

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.

Findings

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.

Originality/value

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

Details

Global Knowledge, Memory and Communication, vol. 69 no. 6/7
Type: Research Article
ISSN: 2514-9342

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Article
Publication date: 9 March 2020

Godwin-Charles Ogbeide, Yao-Yi Fu and Amanda Kay Cecil

The purpose of this paper is to establish a conceptual framework on how hospitality and tourism educators could incorporate new technology and big data analytics into…

Abstract

Purpose

The purpose of this paper is to establish a conceptual framework on how hospitality and tourism educators could incorporate new technology and big data analytics into program curriculum.

Design/methodology/approach

The research developed a logic model to visualize the benefits/impact of hospitality and tourism data analyst via a literature review approach.

Findings

The incorporation of statistics, research and the knowledge of data exploration, analysis and insight into hospitality programs would enhance students’ data analysis competencies.

Research limitations/implications

This is a literature review paper, based on philosophical perspectives from literature review. It would be nice to conduct an empirical study with regard to data analytics in the hospitality and tourism industry.

Practical implications

The hospitality and tourism program coordinators and/or directors are urged to inspire more students who are interested in adding statistics and accounting studies to the hospitality and tourism field. Also, the hospitality and tourism data analyst would secure attractive job offers as well as enhance the average salary of hospitality and tourism graduates.

Social implications

Hospitality and tourism data analytics would secure attractive job offers as well as enhance the average salary of hospitality and tourism graduates.

Originality/value

The paper explored the impact of big data analytics in the hospitality and tourism industry and made recommendations for hospitality and tourism data analytics curricula.

酒店/旅游课程设置是否做好了迎接大数据时代的准备?

研究目的

本论文旨在建立一个概念模型, 以指导酒店旅游教育者们如何引进新科技和大数据分析到现有的课程设置里.

研究设计/方法/途径

本论文通过文献综述的方式, 提出一个logic模型以描画酒店旅游数据分析的好处/影响。

研究结果

酒店课程里加入统计、研究方法和数据勘探的内容对于帮助学生提高数据分析能力有帮助。

研究理论限制

本论文采用概述的形式, 以文献综述的角度, 建立理论模型。如果可以加入实际模型测试, 比如针对酒店旅游业做实际的数据分析, 那么结果将更加丰富。

研究现实/社会意义

酒店旅游项目协调员和/或负责人应该鼓励更多对统计和会计有兴趣的学生从事酒店旅游业。此外, 酒店旅游数据分析员将获得令人羡慕的工作机会和优越的薪资以提高酒店旅游毕业生的平均薪资水平。

研究原创性/价值

本论文探索了大数据分析在酒店旅游业中的影响, 以及对酒店旅游数据分析课程设置做出建议。

关键词

大数据分析, 酒店旅游数据分析员, 数据科学家, 统计学和研究能力, 酒店旅游教育家, 运动学分析

纸张类型

文献评论

Details

Journal of Hospitality and Tourism Technology, vol. 12 no. 1
Type: Research Article
ISSN: 1757-9880

Keywords

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Article
Publication date: 9 May 2018

Soraya Sedkaoui

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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?

Details

International Journal of Innovation Science, vol. 10 no. 2
Type: Research Article
ISSN: 1757-2223

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Article
Publication date: 29 March 2019

Khurshid Ahmad, Zheng JianMing and Muhammad Rafi

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Data Technologies and Applications, vol. 53 no. 2
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 13 February 2017

Zaheer Khan and Tim Vorley

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

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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

Details

Journal of Knowledge Management, vol. 21 no. 1
Type: Research Article
ISSN: 1367-3270

Keywords

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