Impacting Big Data analytics in higher education through Six Sigma techniques

2017 ◽  
Vol 66 (5) ◽  
pp. 662-679 ◽  
Author(s):  
Chad Laux ◽  
Na Li ◽  
Corey Seliger ◽  
John Springer
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Student Success ◽  
Research Design ◽  
Six Sigma ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Content Type ◽  
Conceptual Research

Purpose The purpose of this paper is to develop a framework for utilizing Six Sigma (SS) principles and Big Data analytics at a US public university for the improvement of student success. This research utilizes findings from the Gallup index to identify performance factors of higher education. The goal is to offer a reimagined SS DMAIC methodology that incorporates Big Data principles. Design/methodology/approach The authors utilize a conceptual research design methodology based upon theory building consisting of discovery, description, explanation of the disciplines of SS and Big Data. Findings The authors have found that the interdisciplinary approach to SS and Big Data may be grounded in a framework that reimagines the define, measure, analyze, improve and control (DMAIC) methodology that incorporates Big Data principles. The authors offer propositions of SS DMAIC to be theory tested in subsequent study and offer the practitioner managing the performance of higher education institutions (HEIs) indicators and examples for managing the student success mission of the organization. Research limitations/implications The study is limited to conceptual research design with regard to the SS and Big Data interdisciplinary research. For performance management, this study is limited to HEIs and non-FERPA student data. Implications of this study include a detailed framework for conducting SS Big Data projects. Practical implications Devising a more effective management approach for higher education needs to be based upon student success and performance indicators that accurately measure and support the higher education mission. A proactive approach should utilize the data rich environment being generated. The individual that is most successful in engaging and managing this effort will have the knowledge and skills that are found in both SS and Big Data. Social implications HEIs have historically been significant contributors to the development of meritocracy in democratic societies. Due to a variety of factors, HEIs, especially publicly funded institutions, have been under stress due to a reduction of public funding, resulting in more limited access to the public in which they serve. Originality/value This paper examines Big Data and SS in interdisciplinary effort, an important contribution to SS but lacking a conceptual foundation in the literature. Higher education, as an industry, lacks penetration and adoption of continuous improvement efforts, despite being under tremendous cost pressures and ripe for disruption.

Analytics based decision making

2014 ◽  
Vol 6 (4) ◽  
pp. 332-340 ◽  
Author(s):  
Deepak Agrawal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Design Methodology ◽  
Historical Perspective ◽  
Big Data Analytics ◽  
Analytical Techniques ◽  
Content Type ◽  
Informed Decisions ◽  
Different Types

Purpose – This paper aims to trace the history, application areas and users of Classical Analytics and Big Data Analytics. Design/methodology/approach – The paper discusses different types of Classical and Big Data Analytical techniques and application areas from the early days to present day. Findings – Businesses can benefit from a deeper understanding of Classical and Big Data Analytics to make better and more informed decisions. Originality/value – This is a historical perspective from the early days of analytics to present day use of analytics.

scholarly journals Ambidextrous organization and agility in big data era

2018 ◽  
Vol 24 (5) ◽  
pp. 1091-1109 ◽  
Author(s):  
Riccardo Rialti ◽  
Giacomo Marzi ◽  
Mario Silic ◽  
Cristiano Ciappei
Keyword(s):  
Big Data ◽  
Conceptual Framework ◽  
Data Analytics ◽  
Design Methodology ◽  
Business Process Management ◽  
Process Management ◽  
Big Data Analytics ◽  
Organizational Agility ◽  
Content Type ◽  
Practical Implications

Purpose The purpose of this paper is to explore the effect of big data analytics-capable business process management systems (BDA-capable BPMS) on ambidextrous organizations’ agility. In particular, how the functionalities of BDA-capable BPMS may improve organizational dynamism and reactiveness to challenges of Big Data era will be explored. Design/methodology/approach A theoretical analysis of the potential of BDA-capable BPMS in increasing organizational agility, with particular attention to the ambidextrous organizations, has been performed. A conceptual framework was subsequently developed. Next, the proposed conceptual framework was applied in a real-world context. Findings The research proposes a framework highlighting the importance of BDA-capable BPMS in increasing ambidextrous organizations’ agility. Moreover, the authors apply the framework to the cases of consumer-goods companies that have included BDA in their processes management. Research limitations/implications The principal limitations are linked to the need to validate quantitatively the proposed framework. Practical implications The value of the proposed framework is related to its potential in helping managers to fully understand and exploit the potentiality of BDA-capable BPMS. Moreover, the implications show some guidelines to ease the implementation of such systems within ambidextrous organizations. Originality/value The research offers a model to interpret the effects of BDA-capable BPMS on ambidextrous organizations’ agility. In this way, the research addresses a significant gap by exploring the importance of information systems for ambidextrous organizations’ agility.

Davenport and Prusak on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 7-11 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Early Years ◽  
Content Type ◽  
Potential Risks ◽  
The Relationship

Purpose Larry Prusak and Tom Davenport have long been leading voices in the knowledge management (KM) field. This interview aims to explore their views on the relationship between KM and big data/analytics. Design/methodology/approach An interview was conducted by email with Larry Prusak and Tom Davenport in 2015 and updated in 2016. Findings Prusak and Davenport hold differing views on the role of KM today. They also see the relationship between KM and big data/analytics somewhat differently. Davenport, in particular, has much to say on how big data/analytics can be best utilized by business as well as its potential risks. Originality/value It is important to understand how two of the most serious KM thinkers since the early years of KM understand the relationship between big data/analytics, KM and organizations. Their views can help shape thinking in these fields.

What attracts vehicle consumers’ buying

2019 ◽  
Vol 120 (1) ◽  
pp. 57-78 ◽  
Author(s):  
Fuli Zhou ◽  
Ming K. Lim ◽  
Yandong He ◽  
Saurabh Pratap
Keyword(s):  
Big Data ◽  
Operations Management ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Content Type ◽  
Consumption Behavior ◽  
Integrated Method ◽  
Data Analytic

Purpose The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

Does big data mean big knowledge? KM perspectives on big data and analytics

2017 ◽  
Vol 21 (1) ◽  
pp. 1-6 ◽  
Author(s):  
David J. Pauleen ◽  
William Y.C. Wang
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Content Type ◽  
Guiding Principle ◽  
Proposed Model ◽  
Organizational Value ◽  
Practical Implications

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.

The framework of talent analytics using big data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arnold Saputra ◽  
Gunawan Wang ◽  
Justin Zuopeng Zhang ◽  
Abhishek Behl
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Talent Management ◽  
Content Type ◽  
Using Data ◽  
Business Challenges ◽  
Practical Implications

PurposeThe era of work 4.0 demands organizations to expedite their digital transformation to sustain their competitive advantage in the market. This paper aims to help the human resource (HR) department digitize and automate their analytical processes based on a big-data-analytics framework.Design/methodology/approachThe methodology applied in this paper is based on a case study and experimental analysis. The research was conducted in a specific industry and focused on solving talent analysis problems.FindingsThis research conducts digital talent analysis using data mining tools with big data. The talent analysis based on the proposed framework for developing and transforming the HR department is readily implementable. The results obtained from this talent analysis using the big-data-analytics framework offer many opportunities in growing and advancing a company's talents that are not yet realized.Practical implicationsBig data allows HR to perform analysis and predictions, making more intelligent and accurate decisions. The application of big data analytics in an HR department has a significant impact on talent management.Originality/valueThis research contributes to the literature by proposing a formal big-data-analytics framework for HR and demonstrating its applicability with real-world case analysis. The findings help organizations develop a talent analytics function to solve future leaders' business challenges.

How data analytics is changing entrepreneurial opportunities?

2018 ◽  
Vol 10 (2) ◽  
pp. 274-294 ◽  
Author(s):  
Soraya Sedkaoui
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Computer Science ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Educational Programs ◽  
Learning Needs ◽  
Content Type ◽  
Entrepreneurial Opportunities

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?

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Singh ◽  
Saurabh Agrawal ◽  
Abhishek Sahu ◽  
Yigit Kazancoglu
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Sector ◽  
Future Research ◽  
Research Gaps ◽  
Content Type

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohamad Bahrami ◽  
Sajjad Shokouhyar
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Supply Chain ◽  
Firm Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Management Culture

PurposeBig data analytics capability (BDAC) can affect firm performance in several ways. The purpose of this paper is to understand how BDA capabilities affect firm performance through supply chain resilience in the presence of the risk management culture.Design/methodology/approachThe study adopted a cross-sectional approach to collect survey-based responses to examine the hypotheses. 167 responses were collected and analyzed using partial least squares in SmartPLS3. The respondents were generally senior IT executives with education and experience in data and business analytics.FindingsThe results show that BDA capabilities increase supply chain resilience as a mediator by enhancing innovative capabilities and information quality, ultimately leading to improved firm performance. In addition, the relationship between supply chain resilience and firm performance is influenced by risk management culture as a moderator.Originality/valueThe present study contributes to the relevant literature by demonstrating the mediating role of supply chain resilience between the BDA capabilities relationship and firm performance. In this context, some theoretical and managerial implications are proposed and discussed.

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