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.

The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility

2019 ◽  
Vol 32 (2) ◽  
pp. 297-318 ◽  
Author(s):  
Santanu Mandal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Investment Decision ◽  
Big Data Analytics ◽  
Content Type ◽  
Investment Decision Making ◽  
Management Capabilities ◽  
The Impact

Purpose The importance of big data analytics (BDA) on the development of supply chain (SC) resilience is not clearly understood. To address this, the purpose of this paper is to explore the impact of BDA management capabilities, namely, BDA planning, BDA investment decision making, BDA coordination and BDA control on SC resilience dimensions, namely, SC preparedness, SC alertness and SC agility. Design/methodology/approach The study relied on perceptual measures to test the proposed associations. Using extant measures, the scales for all the constructs were contextualized based on expert feedback. Using online survey, 249 complete responses were collected and were analyzed using partial least squares in SmartPLS 2.0.M3. The study targeted professionals with sufficient experience in analytics in different industry sectors for survey participation. Findings Results indicate BDA planning, BDA coordination and BDA control are critical enablers of SC preparedness, SC alertness and SC agility. BDA investment decision making did not have any prominent influence on any of the SC resilience dimensions. Originality/value The study is important as it addresses the contribution of BDA capabilities on the development of SC resilience, an important gap in the extant literature.

scholarly journals Big data analytics and sustainable textile manufacturing

2020 ◽  
Vol 58 (8) ◽  
pp. 1699-1714 ◽  
Author(s):  
Dieu Hack-Polay ◽  
Mahfuzur Rahman ◽  
Md Morsaline Billah ◽  
Hesham Z. Al-Sabbahy
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Environmental Sustainability ◽  
Textile Industry ◽  
Data Analytics ◽  
New Technologies ◽  
Developing World ◽  
Big Data Analytics ◽  
Content Type ◽  
Member Exchange

PurposeThe purpose of this article is to discuss issues associated with the application big data analytics for decision-making about the introduction of new technologies in the textile industry in the developing world.Design/methodology/approachThe leader–member exchange theoretical framework to consider the nature of the relationships between owners and followers to identify the potential issues that affect decision-making was used. However, decisions to adopt such environmentally friendly biotechnologies are hampered by the lack of awareness amongst owners, intergenerational conflict and cultural impediments.FindingsThe article found that the limited use of this valuable technological resource is linked to several factors, mainly cultural, generational and educational factors. The article exposes two key new technologies that could help the industry reduce its carbon footprint.Originality/valueThe study suggests more awareness raising amongst plant owners and greater empowerment of new generations in decision-making in the industry. This study, therefore, bears significant implications for environmental sustainability in the developing world where the textile industry is one of the major polluting industries affecting water quality and human health.

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.

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.

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.

scholarly journals Motivation to use big data and big data analytics in external auditing

2019 ◽  
Vol 34 (7) ◽  
pp. 750-782 ◽  
Author(s):  
Lina Dagilienė ◽  
Lina Klovienė
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Constructivist Grounded Theory ◽  
Theory Approach ◽  
Motivating Factors ◽  
Content Type ◽  
Different Types ◽  
Current Practices

Purpose This paper aims to explore organisational intentions to use Big Data and Big Data Analytics (BDA) in external auditing. This study conceptualises different contingent motivating factors based on prior literature and the views of auditors, business clients and regulators regarding the external auditing practices and BDA. Design/methodology/approach Using the contingency theory approach, a literature review and 21 in-depth interviews with three different types of respondents, the authors explore factors motivating the use of BDA in external auditing. Findings The study presents a few key findings regarding the use of BD and BDA in external auditing. By disclosing a comprehensive view of current practices, the authors identify two groups of motivating factors (company-related and institutional) and the circumstances in which to use BDA, which will lead to the desired outcomes of audit companies. In addition, the authors emphasise the relationship of audit companies, business clients and regulators. The research indicates a trend whereby external auditors are likely to focus on the procedures not only to satisfy regulatory requirements but also to provide more value for business clients; hence, BDA may be one of the solutions. Research limitations/implications The conclusions of this study are based on interview data collected from 21 participants. There is a limited number of large companies in Lithuania that are open to co-operation. Future studies may investigate the issues addressed in this study further by using different research sites and a broader range of data. Practical implications Current practices and outcomes of using BD and BDA by different types of respondents differ significantly. The authors wish to emphasise the need for audit companies to implement a BD-driven approach and to customise their audit strategy to gain long-term efficiency. Furthermore, the most challenging factors for using BDA emerged, namely, long-term audit agreements and the business clients’ sizes, structures and information systems. Originality/value The original contribution of this study lies in the empirical investigation of the comprehensive state-of-the-art of BDA usage and motivating factors in external auditing. Moreover, the study examines the phenomenon of BD as one of the most recent and praised developments in the external auditing context. Finally, a contingency-based theoretical framework has been proposed. In addition, the research also makes a methodological contribution by using the approach of constructivist grounded theory for the analysis of qualitative data.

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.

Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Minwoo Lee ◽  
Wooseok Kwon ◽  
Ki-Joon Back
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Prediction Model ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Support Vector ◽  
Content Type ◽  
Extreme Gradient Boosting ◽  
Review Helpfulness

Purpose Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial intelligence (AI) is one of the emerging big data analytics techniques, hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data. Design/methodology/approach The study analyzed 1,483,858 restaurant reviews collected from Yelp.com. After a thorough literature review, the study identified and added to the prediction model 4 attributes containing 11 key determinants of review helpfulness. Four ML algorithms, namely, multivariate linear regression, random forest, support vector machine regression and extreme gradient boosting (XGBoost), were used to find a better prediction model for customer decision-making. Findings By comparing the performance metrics, the current study found that XGBoost was the best model to predict review helpfulness among selected popular ML algorithms. Results revealed that attributes regarding a reviewer’s credibility were fundamental factors determining a review’s helpfulness. Review helpfulness even valued credibility over ratings or linguistic contents such as sentiment and subjectivity. Practical implications The current study helps restaurant operators to attract customers by predicting review helpfulness through ML-based predictive modeling and presenting potential helpful reviews based on critical attributes including review, reviewer, restaurant and linguistic content. Using AI, online review platforms and restaurant websites can enhance customers’ attitude and purchase decision-making by reducing information overload and search cost and highlighting the most crucial review helpfulness features and user-friendly automated search results. Originality/value To the best of the authors’ knowledge, the current study is the first to develop a prediction model of review helpfulness and reveal essential factors for helpful reviews. Furthermore, the study presents a state-of-the-art ML model that surpasses the conventional models’ prediction accuracy. The findings will improve practitioners’ marketing strategies by focusing on factors that influence customers’ decision-making.

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