scholarly journals Big Data Analytics: Ethical Dilemmas, Power Imbalances, and Design Science Research

2021 ◽  
Vol 49 (1) ◽  
pp. 448-453
Author(s):  
Michael D. Myers ◽  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Design Science ◽  
Ethical Dilemmas ◽  
Big Data Analytics ◽  
Science Research ◽  
Design Science Research

scholarly journals Design Science Research: Evaluation in the Lens of Big Data Analytics

Systems ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 27 ◽  
Author(s):  
Ahmed Elragal ◽  
Moutaz Haddara
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Design Research ◽  
Research Evaluation ◽  
Design Science ◽  
Big Data Analytics ◽  
Science Research ◽  
Design Science Research ◽  
Evaluation Phase ◽  
New Type

Given the different types of artifacts and their various evaluation methods, one of the main challenges faced by researchers in design science research (DSR) is choosing suitable and efficient methods during the artifact evaluation phase. With the emergence of big data analytics, data scientists conducting DSR are also challenged with identifying suitable evaluation mechanisms for their data products. Hence, this conceptual research paper is set out to address the following questions. Does big data analytics impact how evaluation in DSR is conducted? If so, does it lead to a new type of evaluation or a new genre of DSR? We conclude by arguing that big data analytics should influence how evaluation is conducted, but it does not lead to the creation of a new genre of design research.

scholarly journals Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations

2019 ◽  
Vol 12 (1) ◽  
pp. 277 ◽  
Author(s):  
Vinicius Luiz Ferraz Minatogawa ◽  
Matheus Munhoz Vieira Franco ◽  
Izabela Simon Rampasso ◽  
Rosley Anholon ◽  
Ruy Quadros ◽  
...  
Keyword(s):  
Big Data ◽  
Performance Management ◽  
Business Model ◽  
Small Businesses ◽  
Data Analytics ◽  
Design Science ◽  
Big Data Analytics ◽  
Business Model Innovation ◽  
Raw Material ◽  
Animal Origin

Business model innovation is considered key for organizations to achieve sustainability. However, there are many problems involving the operationalization of business model innovation. We used a design science methodology to develop an artifact to assist business model innovation efforts. The artifact uses performance measurement indicators of the company’s business model, which are powered by Big Data analytics to endow customer-driven business model innovation. Then, we applied the artifact in a critical case study. The selected company is a fashion ecommerce that proposes a vegan and sustainable value using recycled plastic bottle yarn as raw material, and ensures that no material with animal origin is used. Our findings show that the artifact successfully assists a proactive and continuous effort towards business model innovation. Although based on technical concepts, the artifact is accessible to the context of small businesses, which helps to democratize the practices of business model innovation and Big Data analytics beyond large organizations. We contribute to the business model innovation literature by connecting it to performance management and Big Data and providing paths for its operationalization. Consequently, in practice, the proposed artifact can assist managers dealing with business model as a dynamic element towards a sustainable company.

scholarly journals Lean Based Maturity Framework Integrating Value, BIM and Big Data Analytics: Evidence from AEC Industry

2021 ◽  
Vol 13 (18) ◽  
pp. 10029
Author(s):  
Gökhan Demirdöğen ◽  
Nihan Sena Diren ◽  
Hande Aladağ ◽  
Zeynep Işık
Keyword(s):  
Big Data ◽  
Construction Industry ◽  
Negative Impact ◽  
Design Science ◽  
Big Data Analytics ◽  
Value Added ◽  
Science Research ◽  
Research Problem ◽  
Design Cycle ◽  
Aec Industry

The construction industry is considered as one of the least productive, highest energy consuming, and least digitized industries. The Lean Management (LM) philosophy became a significant way for eliminating non-value-added activities and wastes during a building’s lifecycle. However, studies have shown that philosophies are not efficient by themselves to solve the issues of the construction industry. They need to be supported with the appropriate technologies and tools. Therefore, the integrated use of Building Information Modelling (BIM) with LM or Value Engineering (VE) were proposed in the literature. Nonetheless, it was also seen that BIM can provide more insights and improvements when BIM is integrated with data analysis tools to analyze BIM data. In the literature, the synergies between these concepts are generally addressed pairwise, and there is no comprehensive framework which identifies their relationships. Therefore, this study aims to develop a maturity framework that facilitates the adoption of LM, VE, BIM, and Big Data Analytic (BDA) concepts to address long-standing productivity and digitalization issues in the Architecture, Engineering, and Construction (AEC) industry. Design Science Research (DSR) methodology and its three-cycle view (relevance, rigor, and design cycle) were applied to build the proposed maturity framework. Two interviews were performed to identify and observe research problem in relevance cycle. In the rigor cycle, a comprehensive literature review was performed to create a base for the development of the maturity framework. In addition to the developed base of the framework, lean processes were added to this cycle. In the design cycle, the developed framework was evaluated and validated by five experts through face-to-face interviews. The importance of employer’s requirements to adopt the proposed methodologies, the negative impact of change orders, the importance of pre-construction phases to facilitate value creation and waste elimination, and the usage of common data environment with BIM were identified as the prominent application and adaptation issues.

Big data analytics and demand forecasting in supply chains: a conceptual analysis

2018 ◽  
Vol 29 (2) ◽  
pp. 739-766 ◽  
Author(s):  
Erik Hofmann ◽  
Emanuel Rutschmann
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Conceptual Analysis ◽  
Scientific Literature ◽  
Design Science ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Scientific Work ◽  
Content Type ◽  
Forecasting Methods

Purpose Demand forecasting is a challenging task that could benefit from additional relevant data and processes. The purpose of this paper is to examine how big data analytics (BDA) enhances forecasts’ accuracy. Design/methodology/approach A conceptual structure based on the design-science paradigm is applied to create categories for BDA. Existing approaches from the scientific literature are synthesized with industry knowledge through experience and intuition. Accordingly, a reference frame is developed using three steps: description of conceptual elements utilizing justificatory knowledge, specification of principles to explain the interplay between elements, and creation of a matching by conducting investigations within the retail industry. Findings The developed framework could serve as a guide for meaningful BDA initiatives in the supply chain. The paper illustrates that integration of different data sources in demand forecasting is feasible but requires data scientists to perform the job, an appropriate technological foundation, and technology investments. Originality/value So far, no scientific work has analyzed the relation of forecasting methods to BDA; previous works have described technologies, types of analytics, and forecasting methods separately. This paper, in contrast, combines insights and provides advice on how enterprises can employ BDA in their operational, tactical, or strategic demand plans.

scholarly journals AUDIT DATA ANALYTICS DENGAN MS-EXCEL: USE CASE PEMERIKSAAN PAJAK

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Febrian Febrian
Keyword(s):  
Data Analytics ◽  
Design Science ◽  
Science Research ◽  
Design Science Research ◽  
Use Case ◽  
Audit Data

Dalam pemeriksaan pajak , kegiatan utama yang dilaksanakan adalah  pengujian bukti. Pengujian bukti ini dilakukan dengan melakukan pengolahan data yang diterima dari  Wajib Pajak. Isu yang berkembang adalah data yang diolah dari Wajib Pajak tidak hanya berupa data dalam bentuk fisik, akan tetapi  juga berupa data elektronik seperti pembukuan general ledger (buku besar). Proses pengolahan data elektronik ini menjadi sangat penting pada masa  sekarang. Disiplin ilmu pemeriksaan pun ikut berkembang dengan adanya istilah Audit Data Analytics yang merupakan bagian dari Teknik Audit Berbantuan Komputer untuk melakukan pengolahan data elektronik tersebut. Penelitian  ini menggunakan metode design  science research mengajukan  temuan  artefak  yang  berkaitan  dengan  model  dan  instantiasi  (instantiation) MS-Excel untuk implementasi ADA. Hasil penelitian ini diharapkan dapat menjadi tambahan referensi dalam pemeriksaan pajak berupa artefak dalam bentuk instantiasi penggunaan MS-Excel untuk melakukan data analytics dengan sumber dari  general ledger wajib pajak.  

scholarly journals Big Data, Data Science and Emerging Analytic tools : Impact in social science

2019 ◽  
Author(s):  
Satabdi Saha ◽  
Tapabrata Maiti
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Social Science ◽  
Data Analytics ◽  
Data Science ◽  
Social Science Research ◽  
Big Data Analytics ◽  
Science Research ◽  
Data Sources ◽  
The Internet

Rapid advancement of the Internet and Internet of Things have led to companies generating gigantic volumes of data in every field of business. Big data research has thus become one of the most prominent topic of discussion garnering simultaneous attention from academia and industry. This paper attempts to understand the significance of big data in current scientific research and outline its unique characteristics, otherwise unavailable from traditional data sources. We focus on how big data has altered the scope and dimension of data science thus making it severely interdisciplinary. We further discuss the significance and opportunities of big data in the domain of social science research with a scrutiny of the challenges previously faced while using smaller datasets. Given the extensive utilization of big data analytics in all forms of socio-technical research, we argue the need to critically interrogate its assumptions and biases; thereby advocating the need for creating a just and ethical big data world.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

A Review on Big Data Analytics Tools in Context with Scalability

2019 ◽  
Vol 7 (2) ◽  
pp. 273-277
Author(s):  
Ajay Kumar Bharti ◽  
Neha Verma ◽  
Deepak Kumar Verma
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data Analytics Solutions and Challenges

2017 ◽  
Vol 49 (004) ◽  
pp. 825--830
Author(s):  
A. AHMED ◽  
R.U. AMIN ◽  
M. R. ANJUM ◽  
I. ULLAH ◽  
I. S. BAJWA
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics
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