Big data analytics: transforming data to action

2017 ◽  
Vol 23 (3) ◽  
pp. 703-720 ◽  
Author(s):  
Daniel Bumblauskas ◽  
Herb Nold ◽  
Paul Bumblauskas ◽  
Amy Igou
Keyword(s):  
Big Data ◽  
Business Process ◽  
Data Analytics ◽  
Business Process Management ◽  
Large Scale ◽  
Process Management ◽  
Big Data Analytics ◽  
Model Framework ◽  
Content Type ◽  
Actionable Knowledge

Purpose The purpose of this paper is to provide a conceptual model for the transformation of big data sets into actionable knowledge. The model introduces a framework for converting data to actionable knowledge and mitigating potential risk to the organization. A case utilizing a dashboard provides a practical application for analysis of big data. Design/methodology/approach The model can be used both by scholars and practitioners in business process management. This paper builds and extends theories in the discipline, specifically related to taking action using big data analytics with tools such as dashboards. Findings The authors’ model made use of industry experience and network resources to gain valuable insights into effective business process management related to big data analytics. Cases have been provided to highlight the use of dashboards as a visual tool within the conceptual framework. Practical implications The literature review cites articles that have used big data analytics in practice. The transitions required to reach the actionable knowledge state and dashboard visualization tools can all be deployed by practitioners. A specific case example from ESP International is provided to illustrate the applicability of the model. Social implications Information assurance, security, and the risk of large-scale data breaches are a contemporary problem in society today. These topics have been considered and addressed within the model framework. Originality/value The paper presents a unique and novel approach for parsing data into actionable knowledge items, identification of viruses, an application of visual dashboards for identification of problems, and a formal discussion of risk inherent with big data.

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.

Big data breaches and customer compensation strategies

2017 ◽  
Vol 37 (1) ◽  
pp. 56-74 ◽  
Author(s):  
Thomas Kude ◽  
Hartmut Hoehle ◽  
Tracy Ann Sykes
Keyword(s):  
Big Data ◽  
Personality Traits ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Content Type ◽  
Customer Data ◽  
Data Breaches ◽  
Large Scale Data ◽  
Scale Data

Purpose Big Data Analytics provides a multitude of opportunities for organizations to improve service operations, but it also increases the threat of external parties gaining unauthorized access to sensitive customer data. With data breaches now a common occurrence, it is becoming increasingly plain that while modern organizations need to put into place measures to try to prevent breaches, they must also put into place processes to deal with a breach once it occurs. Prior research on information technology security and services failures suggests that customer compensation can potentially restore customer sentiment after such data breaches. The paper aims to discuss these issues. Design/methodology/approach In this study, the authors draw on the literature on personality traits and social influence to better understand the antecedents of perceived compensation and the effectiveness of compensation strategies. The authors studied the propositions using data collected in the context of Target’s large-scale data breach that occurred in December 2013 and affected the personal data of more than 70 million customers. In total, the authors collected data from 212 breached customers. Findings The results show that customers’ personality traits and their social environment significantly influences their perceptions of compensation. The authors also found that perceived compensation positively influences service recovery and customer experience. Originality/value The results add to the emerging literature on Big Data Analytics and will help organizations to more effectively manage compensation strategies in large-scale data breaches.

Big data analytics with swarm intelligence

2016 ◽  
Vol 116 (4) ◽  
pp. 646-666 ◽  
Author(s):  
Shi Cheng ◽  
Qingyu Zhang ◽  
Quande Qin
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Swarm Intelligence ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Underlying Mechanism ◽  
Research Area ◽  
Future Research ◽  
Content Type

Purpose – The quality and quantity of data are vital for the effectiveness of problem solving. Nowadays, big data analytics, which require managing an immense amount of data rapidly, has attracted more and more attention. It is a new research area in the field of information processing techniques. It faces the big challenges and difficulties of a large amount of data, high dimensionality, and dynamical change of data. However, such issues might be addressed with the help from other research fields, e.g., swarm intelligence (SI), which is a collection of nature-inspired searching techniques. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the potential application of SI in big data analytics is analyzed. The correspondence and association between big data analytics and SI techniques are discussed. As an example of the application of the SI algorithms in the big data processing, a commodity routing system in a port in China is introduced. Another example is the economic load dispatch problem in the planning of a modern power system. Findings – The characteristics of big data include volume, variety, velocity, veracity, and value. In the SI algorithms, these features can be, respectively, represented as large scale, high dimensions, dynamical, noise/surrogates, and fitness/objective problems, which have been effectively solved. Research limitations/implications – In current research, the example problem of the port is formulated but not solved yet given the ongoing nature of the project. The example could be understood as advanced IT or data processing technology, however, its underlying mechanism could be the SI algorithms. This paper is the first step in the research to utilize the SI algorithm to a big data analytics problem. The future research will compare the performance of the method and fit it in a dynamic real system. Originality/value – Based on the combination of SI and data mining techniques, the authors can have a better understanding of the big data analytics problems, and design more effective algorithms to solve real-world big data analytical problems.

Big data integration with business processes: a literature review

2017 ◽  
Vol 23 (3) ◽  
pp. 477-492 ◽  
Author(s):  
Samuel Fosso Wamba ◽  
Deepa Mishra
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Business Process ◽  
Design Methodology ◽  
Business Process Management ◽  
Process Management ◽  
Business Processes ◽  
Future Research ◽  
Content Type ◽  
Practical Implications

Purpose The purpose of this paper is to improve the understanding of the integration of business process management (BPM), business process re-engineering (BPR) and business process innovation (BPI) with big data. It focusses on synthesizing research published in the period 2006-2016 to establish both what the authors know and do not know about this topic, identifying areas for future research. Design/methodology/approach The research is based on a review of 49 published papers on big data, BPM, BPR and BPI in the top journals in the field 2006-2016. Findings In this paper, the authors have identified the most influential works based on citations and PageRank methods. Through network analysis the authors identify four major clusters that provide potential opportunities for future investigation. Practical implications It is important for practitioners to be aware of the benefits of big data, BPM, BPR and BPI integration. This paper provides valuable insights for practitioners. Originality/value This paper is based on a comprehensive literature review, which gives big data researchers the opportunity to understand business processes in depth. In addition, highlighting many gaps in the current literature and developing an agenda for future research, will save time and effort for readers looking to research topics within big data and business processes.

Big data for big pharma

2018 ◽  
Vol 24 (5) ◽  
pp. 1110-1123 ◽  
Author(s):  
Giuseppe Festa ◽  
Imen Safraou ◽  
Maria Teresa Cuomo ◽  
Ludovico Solima
Keyword(s):  
Big Data ◽  
Grounded Theory ◽  
Business Process ◽  
Business Process Management ◽  
Process Management ◽  
Theoretical Framework ◽  
Theory Approach ◽  
Process Standardization ◽  
Content Type ◽  
The Impact

Purpose Big pharma, which comprise the most important companies in the pharmaceutical sector, are ambidextrous organizations by nature. Big data can heavily influence this characteristic by simultaneously requiring adequate business process management. In fact, the impact of big data on business process management can assist big pharma in increasing process efficiency (which is related to the research and development pipeline) and process efficacy (related to product portfolio management). The purpose of this paper is to investigate this possibility and opportunity. Design/methodology/approach In the absence of specific scientific studies, as indicated by a review of the existing literature, the authors have adopted a grounded theory approach. This research has observed multiple cases (the 15 most important big pharma companies worldwide) through an electronic survey conducted on secondary data. The study has allowed the generation of a theoretical framework based on the (direct) relationship between knowledge process standardization (as the dependent variable) and big data (as the independent variable) in organizations oriented toward ambidexterity, such as big pharma in the specific scope of this research. Findings As big data utilization becomes widespread along the pipeline (or even along the value chain/supply chain), business process management increasingly uses (or tends to use) standardization, adopting process standardization as the main coordination mechanism to manage big knowledge. This theory is even more true when considering the moderating role of ambidexterity. An enterprise oriented toward structural ambidexterity (such as big pharma) that uses big data will require increased process standardization to manage big knowledge. Alternatively, an enterprise oriented toward contextual ambidexterity that uses big data will require increased output standardization. Originality/value Based on an analytical literature review, no research to date has focused strict attention on the influence that big data can have on business process management to improve the natural ambidexterity of big pharma. The main unique feature of this research relies on this point. The main value of the research originates from the theoretical framework reconstructed by grounded theory, which constitutes a powerful strategic tool to support executives and managers of big pharma in organizing business process management for their ambidextrous organizations using big data.

The role of big data in shaping ambidextrous business process management

2018 ◽  
Vol 24 (5) ◽  
pp. 1163-1175 ◽  
Author(s):  
Luca Dezi ◽  
Gabriele Santoro ◽  
Heger Gabteni ◽  
Anna Claudia Pellicelli
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Business Process ◽  
Business Process Management ◽  
Qualitative Methodology ◽  
Process Management ◽  
Business Processes ◽  
Service Firms ◽  
Content Type ◽  
The Impact

Purpose The purpose of this paper is to explore how big data can shape ambidextrous business process management (BPM) in terms of exploitation and exploration. Design/methodology/approach A qualitative methodology involving case studies has been chosen to explore the impact of big data deployment on exploitative and explorative business processes. Findings The results of case studies offer some opportunities and challenges for service firms related to both the exploitative and the explorative aspects of BPM driven by big data. Originality/value The deployment of big data in business processes has attracted a large amount of interest recently. However, these studies are mostly conceptual, so empirical research about this complex relationship is quite rare, especially research with specific arguments regarding exploitative and explorative activities. This paper aims to fill this gap by offering empirical evidence for big data-driven business processes.

Smart production systems drivers for business process management improvement

2020 ◽  
Vol 26 (5) ◽  
pp. 1075-1092 ◽  
Author(s):  
Maciel M. Queiroz ◽  
Samuel Fosso Wamba ◽  
Marcio C. Machado ◽  
Renato Telles
Keyword(s):  
Business Process ◽  
Industry 4.0 ◽  
Business Process Management ◽  
Process Management ◽  
Business Models ◽  
Production Systems ◽  
Big Data Analytics ◽  
Content Type ◽  
Theoretical Contribution ◽  
Integrative Framework

PurposeThe Industry 4.0 phenomenon offers opportunities and challenges to all business models. Despite the literature advances in this field, little attention has been paid to the interplay of smart production systems (SPSs), big data analytics (BDA), cyber-physical systems (CPS), internet of things (IoT), and the potential business process management (BPM) improvements. This study aims to identify the main drivers and their implications for improved BPM.Design/methodology/approachThis study employed a narrative literature review of studies concerning smart-production-systems-related issues in the context of Industry 4.0.FindingsThe study identified 26 drivers from the literature associated with SPSs that have an impact on improved BPM. These drivers are presented in an integrative framework considering BDA, CPS, and the IoT.Research limitations/implicationsThe framework's component integration is yet not tested. However, this study offers a significant theoretical contribution by presenting drivers that can be utilised to develop constructs, exploring critical factors related to the interplay of SPSs and improved BPM, and shading light on Industry 4.0's main elements. The study also makes suggestions for further research.Practical implicationsThe proposed framework, with its 26 drivers, provides insights for practitioners and decision-makers interested in gaining an in-depth understanding of the complexities of SPSs and improved BPM.Originality/valueThis study integrates BDA, CPS, and IoT into a framework with 26 drivers associated with SPSs to improve BPM.

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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