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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: 11 July 2016

Margie Jantti and Jennifer Heath

The purpose of this paper is to provide an overview of the development of an institution wide approach to learning analytics at the University of Wollongong (UOW) and the…

Abstract

Purpose

The purpose of this paper is to provide an overview of the development of an institution wide approach to learning analytics at the University of Wollongong (UOW) and the inclusion of library data drawn from the Library Cube.

Design/methodology/approach

The Student Support and Education Analytics team at UOW is tasked with creating policy, frameworks and infrastructure for the systematic capture, mapping and analysis of data from the across the university. The initial data set includes: log file data from Moodle sites, Library Cube, student administration data, tutorials and student support service usage data. Using the learning analytics data warehouse UOW is developing new models for analysis and visualisation with a focus on the provision of near real-time data to academic staff and students to optimise learning opportunities.

Findings

The distinct advantage of the learning analytics model is that the selected data sets are updated weekly, enabling near real-time monitoring and intervention where required. Inclusion of library data with the other often disparate data sets from across the university has enabled development of a comprehensive platform for learning analytics. Future work will include the development of predictive models using the rapidly growing learning analytics data warehouse.

Practical implications

Data warehousing infrastructure, the systematic capture and exporting of relevant library data sets are requisite for the consideration of library data in learning analytics.

Originality/value

What was not anticipated five years ago when the Value Cube was first realised, was the development of learning analytic services at UOW. The Cube afforded University of Wollongong Library considerable advantage: the framework for data harvesting and analysis was established, ready for inclusion within learning analytics data sets and subsequent reporting to faculty.

Details

Performance Measurement and Metrics, vol. 17 no. 2
Type: Research Article
ISSN: 1467-8047

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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: 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

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Article
Publication date: 8 January 2018

Megan Oakleaf

The purpose of this paper is to describe the need for academic libraries to demonstrate and increase their impact of student learning and success. It highlights the data

Abstract

Purpose

The purpose of this paper is to describe the need for academic libraries to demonstrate and increase their impact of student learning and success. It highlights the data problems present in existing library value correlation research and suggests a pathway to surmounting existing data obstacles. The paper advocates the integration of libraries into institutional learning analytics systems to gain access to more granular student learning and success data. It also suggests using library-infused learning analytics data to discover and act upon new linkages that may reveal library value in an institutional context.

Design/methodology/approach

The paper describes a pattern pervasive in existing academic library value correlation research and identifies major data obstacles to future research in this vein. The paper advocates learning analytics as one route to access more usable and revealing data. It also acknowledges several challenges to the suggested approach.

Findings

This paper describes learning analytics as it may apply to and support correlation research on academic library value. While this paper advocates exploring the integration of library data and institutional data via learning analytics initiatives, it also describes four challenges to this approach including librarian concerns related to the use of individual level data, the tension between claims of correlation and causation in library value research, the need to develop interoperability standards for library data and organizational readiness and learning analytics maturity issues.

Originality/value

This paper outlines a path forward for academic library value research that may otherwise be stymied by existing data difficulties.

Details

Information and Learning Science, vol. 119 no. 1/2
Type: Research Article
ISSN: 2398-5348

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Article
Publication date: 5 August 2021

Najah Almazmomi, Aboobucker Ilmudeen and Alaa A. Qaffas

In today's business setting, the business analytic capability, data-driven culture and product development features are highly pronounced in light of the firm's…

Abstract

Purpose

In today's business setting, the business analytic capability, data-driven culture and product development features are highly pronounced in light of the firm's competitive advantage. Though widespread attention has been given to the above concepts, there hasn't been much research done on how it could support achieving competitive advantage.

Design/methodology/approach

This research strongly lies on the theoretical background and empirically tests the hypothesized relationships. The primary survey of 272 responses was analysed by using the partial least squares structural equation modelling (PLS-SEM).

Findings

The findings of this study show a significant relationship for the constructs in the research model except for the third hypothesis. Accordingly, the firm's data-driven culture does not have a significant impact on new product newness.

Originality/value

This study empirically tests the business analytics capability, data-driven culture, and new product development features in the context of a firm's competitive advantage. The findings of this study contribute to the theoretical, practical and managerial aspects of this field.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

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Book part
Publication date: 25 November 2019

Sean Mackney and Robin Shields

This chapter examines the application of learning analytics techniques within higher education – learning analytics – and its application in supporting “student success.”…

Abstract

This chapter examines the application of learning analytics techniques within higher education – learning analytics – and its application in supporting “student success.” Learning analytics focuses on the practice of using data about students to inform interventions aimed at improving outcomes (e.g., retention, graduation, and learning outcomes), and it is a rapidly growing area of educational practice within higher education institutions (HEIs). This growth is spurring a number of commercial developments, with many companies offering “analytics solutions” to universities across the world. We review the origins of learning analytics and identify drives for its growth. We then discuss some possible implications for this growth, which focus on the ethics of data collection, use and sharing.

Details

The Educational Intelligent Economy: Big Data, Artificial Intelligence, Machine Learning and the Internet of Things in Education
Type: Book
ISBN: 978-1-78754-853-4

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Book part
Publication date: 4 January 2019

William D. Brink and M. Dale Stoel

The purpose of this study is to identify the specific skills and abilities within the broad category of data analytics that current business professionals believe are most…

Abstract

The purpose of this study is to identify the specific skills and abilities within the broad category of data analytics that current business professionals believe are most important for accounting graduates. Data analytics knowledge is clearly important, but this category is broad. Therefore, this study identifies the specific skills and abilities that are most important for accounting graduates so that faculty can create classroom materials most beneficial for the future accounting graduates. In 2013, the Association to Advance Collegiate Schools of Business developed new standards for accounting programs, including standard A7, related to information technology and analytics. The intent of the standard clearly focuses on increasing the level of technology and analytics studied within the accounting curriculum. However, the specific details and methods for achieving the intent of A7 remain an open question. This chapter uses prior research focused on business analytics education to identify potential analytic skills, tools, techniques, and management issues of concern within the accounting profession. A survey of 342 accounting professionals identifies suggested areas of analytic competencies for accounting graduates. Specifically, the authors find preferences for skills related to data interpretation and communication over any individual technical skills or statistical knowledge. These skills suggest a role for accountants as intermediaries who may need to translate analytic activities into business language. Post hoc, the authors examine the survey results for differences based on respondent characteristics. Interestingly, female respondents report lower beliefs about the importance of analytic skills. The authors also find some differences when examining different demographics within the respondents.

Details

Advances in Accounting Education: Teaching and Curriculum Innovations
Type: Book
ISBN: 978-1-78756-540-1

Keywords

Content available
Book part
Publication date: 7 October 2019

Vernon J. Richardson and Yuxin Shan

The accounting profession is beginning to demand data analytics skills from its professionals to handle the increasing amount of data available to address accounting…

Abstract

The accounting profession is beginning to demand data analytics skills from its professionals to handle the increasing amount of data available to address accounting questions. Indeed, the explosion of data availability and data are changing the accounting profession, providing accountants the opportunity to continue as key financial information providers to decision-makers. We conducted a survey of accounting department chairs to help understand if, when and how accounting programs would include data analytics in its curriculum. The authors find that 90.7% of accounting department chairs believe that data analytics belongs in the accounting curriculum, with 59.3% planning to introduce an accounting data analytics course in the next three to five years. Most (66.5%) prefer an accounting data analytics course as compared to the general business analytics course and more than half of respondents (56.2%) predict that their coverage of data analytics will be incorporated both throughout the regular accounting curriculum and in a standalone data analytics course. Combined with the requirement of 2018 Association to Advance Collegiate Schools of Business standards, the authors propose that data analytics should be incorporated both in the undergraduate level and graduate level, starting from basic analytics tools and ending with advanced emerging techniques.

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Article
Publication date: 28 October 2020

Anne Fleur van Veenstra, Francisca Grommé and Somayeh Djafari

Public sector data analytics concerns the process of retrieving data, data analysis, publication of the results as well as re-using the data by government organizations to…

Abstract

Purpose

Public sector data analytics concerns the process of retrieving data, data analysis, publication of the results as well as re-using the data by government organizations to improve their operations and enhance public policy. This paper aims to explore the use of public sector data analytics in the Netherlands and the opportunities and challenges of this use.

Design/methodology/approach

This paper finds 74 applications of public sector data analytics, identified by a Web search and consultation with policymakers. The applications are categorized by application type, organization(s) involved and application domain, and illustrative examples are used to elaborate opportunities and challenges.

Findings

Public sector data analytics is most frequently used for inspection and enforcement of social services and for criminal investigation. Even though its usage is often experimental, it raises concerns for scope creep, repeated targeting of the same (group of) individuals, personal data use by third parties and the transparency of governmental processes.

Research limitations/implications

Drawing on desk research, it was not always possible to identify which type of data or which technology was used in the applications that were found. Furthermore, the case studies are illustrative rather than providing an in-depth overview of opportunities and challenges of the use of data analytics in government.

Originality/value

Most studies either perform a literature overview or present a single case study; this paper presents a more comprehensive overview of how a public sector uses data analytics.

Details

Transforming Government: People, Process and Policy, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1750-6166

Keywords

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