Roles and Capabilities of Enterprise Architecture in Big Data Analytics Technology Adoption and Implementation

Gong, Yiwei; Janssen, Marijn

doi:10.4067/s0718-18762021000100104

Cited by 38 publications

(39 citation statements)

References 59 publications

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“…At the individual-level, the tendency of resistance to ‘Data Science’ project execution either by an employee or by a mid-level manager is either due to fear of ‘failure’ or ‘loss of control’ or ‘operational disruption’ (Mikalef et al 2020c ; Shahbaz et al 2019 ). The enablers in the form of developing dynamic capability, such as experience in ‘dealing with complexity’, ‘high tolerance for complexity’ (Gong and Janssen 2021 ; Walker and Brown 2019 ) and ‘Top-management-Team’ support (Alaskar et al 2020 ; Behl et al 2019 ; Chaurasia and Verma 2020 ; Foshay et al 2015 ; Halaweh and Massry 2015 ; Lai et al 2018 ; Lamba and Singh 2018 ; Lautenbach et al 2017 ; Popovič et al 2018 ; Ransbotham et al 2017 ; Verma and Bhattacharyya 2017 ; Walker and Brown 2019 ; Wang et al 2018c ) are a must to address the barriers to considerable extent. Organizational environment for an individual in communicating the benefits of ‘Data Science’ (Chakravorty 2020 ; Gong and Janssen 2021 ; Verma 2017 ) is also a barrier for ‘Data Science’ project success.…”

Section: Resultsmentioning

confidence: 99%

“…The enablers in the form of developing dynamic capability, such as experience in ‘dealing with complexity’, ‘high tolerance for complexity’ (Gong and Janssen 2021 ; Walker and Brown 2019 ) and ‘Top-management-Team’ support (Alaskar et al 2020 ; Behl et al 2019 ; Chaurasia and Verma 2020 ; Foshay et al 2015 ; Halaweh and Massry 2015 ; Lai et al 2018 ; Lamba and Singh 2018 ; Lautenbach et al 2017 ; Popovič et al 2018 ; Ransbotham et al 2017 ; Verma and Bhattacharyya 2017 ; Walker and Brown 2019 ; Wang et al 2018c ) are a must to address the barriers to considerable extent. Organizational environment for an individual in communicating the benefits of ‘Data Science’ (Chakravorty 2020 ; Gong and Janssen 2021 ; Verma 2017 ) is also a barrier for ‘Data Science’ project success. Creating opportunities to interact with leadership team and adopting a deliberate storytelling technique (Boldosova 2019 ) would be helpful in overcoming the communication gap barriers.…”

Section: Resultsmentioning

confidence: 99%

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A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

Reddy

Bhattacharjee

Mishra

et al. 2022

Inf Syst E-Bus Manage

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While embracing digitalization that is further accentuated by the Covid-19 pandemic, the real business outcome is achieved through a robust and well-crafted ‘Data Science Strategy’ (DSS), as significant constituent of Enterprise Digital Strategy. Extant literature has studied the challenges in adoption of components of ‘Data Science’ in discrete for various industry sectors and domains. There is dearth of studies on comprehensive ‘Data Science’ adoption as an umbrella constituting all of its components. The study conducts a “Systematic Literature Review (SLR)” on enablers and barriers affecting the implementation and success of DSS in enterprises. The SLR comprised of 113 published articles during the period 1998 and 2021. In this SLR, we address the gap by synthesizing and proposing a novel framework of ‘Enablers and Barriers’ influencing the success of DSS in enterprises. The proposed framework of ‘Data Science Strategy’ can help organizations taking the right steps towards successful implementation of ‘Data Science’ projects. Supplementary Information The online version contains supplementary material available at 10.1007/s10257-022-00550-x.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

Reddy

Bhattacharjee

Mishra

et al. 2022

Inf Syst E-Bus Manage

View full text Add to dashboard Cite

show abstract

“…A data-driven work atmosphere is enhanced by the willingness of executives to actually follow data-driven decision templates rather than their instincts (Dubey et al 2018;Kiron et al 2014;Pedro et al 2019) and to revise prior decisions if data analysis overrides them (Mikalef, Boura, et al, 2018). Moreover, a strong commitment to data-driven decision-making by upper management characterizes a data-driven culture (Chen et al 2015;Gong and Janssen 2020). Top-level support of initiatives and analytical activities within the company strengthens the acceptance among and involvement of employees (Cao et al 2019;Chen et al 2015).…”

Section: Culture and Governance Capabilitymentioning

confidence: 99%

Human-related capabilities in big data analytics: a taxonomy of human factors with impact on firm performance

Korherr

Kanbach

2021

Rev Manag Sci

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This study intends to provide scholars and practitioners with an understanding of human resource challenges in the context of Big Data Analytics (BDA). This paper provides a holistic framework of human-related capabilities that organizations must consider when implementing BDA to facilitate decision-making. For this purpose, the authors conducted a systematic literature review adapted from Tranfield et al. (BJM 14:207–222, 2003) to identify relevant studies. The 75 publications reviewed provided the sample for an inductive, and systematic data evaluation following the well-known and accepted approach introduced by Gioia et al. (ORM 16:15–31, 2012). The comprehensive review uncovered 33 first-order concepts linked to human-related capabilities, which were distilled into 15 s-order themes and then merged into five aggregated dimensions: Personnel Capability, Management Capability, Organizational Capability, Culture and Governance Capability, and Strategy and Planning Capability. The study is, to the best of the authors’ knowledge, the first to categorize all relevant human-related capabilities for successful BDA application. As such, it not only provides the scientific basis for further research, but also serves as a useful overview of the critical factors for BDA use in decision-making processes.

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“…In the context of big data in education, some specific big data architectures or framework has is proposed for education [11]. However, there are still limitations in adopting big data analytics architecture for enterprises as current frameworks provide generic architecture for big data analytics [12]. These frameworks do not give a detailed learning analytics process for higher education.…”

Section: Introductionmentioning

confidence: 99%

Learning analytics for higher education: proposal of big data ingestion architecture

Amare

Šimonová

2021

SHS Web of Conf.

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Research background: Higher education institutions are generating multiple formats of data from diverse sources across the globe. The data ingestion layer is responsible for collecting data and transform for analysis. Learning analytics plays a vital role in providing decision-making support and selection of suitable timely intervention. The lack of tailored big-data ingestion architectures for academics led to several implementation challenges. Purpose of the article: The purpose of this article is to propose data ingestion architecture enabled for big data learning analytics. Methods: The study reviews existing literature to examine big-data ingestion tools and frameworks; and identify big-data ingestion challenges. An optimized framework for the real world learning analytics application was not yet in place at global higher educations. Consequently, the big-data ingestion pipeline is experiencing challenges of inefficient and complex data access, slow processing time, and security issues associated with transferring data to the system. The proposed data ingestion architecture is based on review of recent literature and adapts best international practices, guidelines, and techniques to meet the demand of current big-data ingestion issues. Findings & value added: This study identifies the current global challenges in implementing learning analytics projects. Review of recent big data ingestion techniques has been done based on defined metrics tuned for learning analytics purposes. The proposed data ingestion framework would increase the effectiveness of collecting, importing, processing and storing of learning data. Besides, the proposed architecture contributes to the construction of full-fledged big-data learning analytics ecosystem of higher educations.

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Roles and Capabilities of Enterprise Architecture in Big Data Analytics Technology Adoption and Implementation

Cited by 38 publications

References 59 publications

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

Human-related capabilities in big data analytics: a taxonomy of human factors with impact on firm performance

Learning analytics for higher education: proposal of big data ingestion architecture

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