Effective Statistical Methods for Big Data Analytics

2017 ◽  
pp. 280-299 ◽  
Author(s):  
Cheng Meng ◽  
Ye Wang ◽  
Xinlian Zhang ◽  
Abhyuday Mandal ◽  
Wenxuan Zhong ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Knowledge Discovery ◽  
Statistical Methods ◽  
Large Scale ◽  
Big Data Analytics ◽  
Divide And Conquer ◽  
Data Driven ◽  
The Past ◽  
Large Scale Dataset

With advances in technologies in the past decade, the amount of data generated and recorded has grown enormously in virtually all fields of industry and science. This extraordinary amount of data provides unprecedented opportunities for data-driven decision-making and knowledge discovery. However, the task of analyzing such large-scale dataset poses significant challenges and calls for innovative statistical methods specifically designed for faster speed and higher efficiency. In this chapter, we review currently available methods for big data, with a focus on the subsampling methods using statistical leveraging and divide and conquer methods.

scholarly journals Smart Cities and Big Data Analytics: A Data-Driven Decision-Making Use Case

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 286-313
Author(s):  
Ahmed M. Shahat Osman ◽  
Ahmed Elragal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Smart Cities ◽  
Research Question ◽  
Big Data Analytics ◽  
Data Driven ◽  
Use Case ◽  
Data Driven Decision Making ◽  
Domain Independent

Interest in smart cities (SCs) and big data analytics (BDA) has increased in recent years, revealing the bond between the two fields. An SC is characterized as a complex system of systems involving various stakeholders, from planners to citizens. Within the context of SCs, BDA offers potential as a data-driven decision-making enabler. Although there are abundant articles in the literature addressing BDA as a decision-making enabler in SCs, mainstream research addressing BDA and SCs focuses on either the technical aspects or smartening specific SC domains. A small fraction of these articles addresses the proposition of developing domain-independent BDA frameworks. This paper aims to answer the following research question: how can BDA be used as a data-driven decision-making enabler in SCs? Answering this requires us to also address the traits of domain-independent BDA frameworks in the SC context and the practical considerations in implementing a BDA framework for SCs' decision-making. This paper's main contribution is providing influential design considerations for BDA frameworks based on empirical foundations. These foundations are concluded through a use case of applying a BDA framework in an SC's healthcare setting. The results reveal the ability of the BDA framework to support data-driven decision making in an SC.

Boosting innovation performance through big data analytics:An empirical investigationon the role of firm agility

2021 ◽  
pp. 016555152110474
Author(s):  
Ahad ZareRavasan
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Current Knowledge ◽  
Big Data Analytics ◽  
Innovation Performance ◽  
Data Driven ◽  
Moderating Variables ◽  
Research Gap ◽  
Moderating Role

While past studies proposed the role of big data analytics (BDA) as one of the primary pathways to business value creation, current knowledge on the link between BDA and innovation performance remains limited. In this regard, this study intends to fill this research gap by developing a theoretical framework for understanding how and under which mechanisms BDA influences innovation performance. Firm agility (conceptualised as sensing agility, decision-making agility and acting agility) is used in this research as the mediator between BDA and innovation performance. Besides, this research conceptualises two moderating variables: data-driven culture and BDA team sophistication. This study employs partial least squares (PLS) to test and validate the proposed hypotheses using survey data of 185 firms. The results show that firm agility significantly mediates the link between BDA use and innovation performance. Besides, the results suggest that data-driven culture moderates the relation between sensing agility and decision-making agility. This research also supports the moderating role of BDA team sophistication on the link between BDA use and sensing agility.

scholarly journals Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs

2019 ◽  
Vol 12 (1) ◽  
pp. 202
Author(s):  
Eun Sun Kim ◽  
Yunjeong Choi ◽  
Jeongeun Byun
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Medium Size ◽  
The Government ◽  
Using Data ◽  
High Level ◽  
D Investment

To expand the field of governmental applications of Big Data analytics, this study presents a case of data-driven decision-making using information on research and development (R&D) projects in Korea. The Korean government has continuously expanded the proportion of its R&D investment in small and medium-size enterprises to improve the commercialization performance of national R&D projects. However, the government has struggled with the so-called “Korea R&D Paradox”, which refers to how performance has lagged despite the high level of investment in R&D. Using data from 48,309 national R&D projects carried out by enterprises from 2013 to 2017, we perform a cluster analysis and decision tree analysis to derive the determinants of their commercialization performance. This study provides government entities with insights into how they might adjust their approach to Big Data analytics to improve the efficiency of R&D investment in small- and medium-sized enterprises.

scholarly journals Explanations as Discourse

2020 ◽  
Vol 24 ◽  
Author(s):  
Sadaf Afrashteh ◽  
Ida Someh ◽  
Michael Davern
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Customer Engagement ◽  
Data Sets ◽  
Research Directions ◽  
Large Scale Data ◽  
Scale Data

Big data analytics uses algorithms for decision-making and targeting of customers. These algorithms process large-scale data sets and create efficiencies in the decision-making process for organizations but are often incomprehensible to customers and inherently opaque in nature. Recent European Union regulations require that organizations communicate meaningful information to customers on the use of algorithms and the reasons behind decisions made about them. In this paper, we explore the use of explanations in big data analytics services. We rely on discourse ethics to argue that explanations can facilitate a balanced communication between organizations and customers, leading to transparency and trust for customers as well as customer engagement and reduced reputation risks for organizations. We conclude the paper by proposing future empirical research directions.

Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance

2021 ◽  
Vol 168 ◽  
pp. 120766
Author(s):  
Usama Awan ◽  
Saqib Shamim ◽  
Zaheer Khan ◽  
Najam Ul Zia ◽  
Syed Muhammad Shariq ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Circular Economy ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Analytics Capability

scholarly journals IAMM: A Maturity Model for Measuring Industrial Analytics Capabilities in Large-scale Manufacturing Facilities

2020 ◽  
Vol 7 (4) ◽  
Author(s):  
Peter O’Donovan ◽  
Ken Bruton ◽  
Dominic T.J. O’Sullivan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Data Driven ◽  
Maturity Model ◽  
Competitive Advantages ◽  
Engineering Statistics ◽  
Industrial Big Data ◽  
The Impact

Industrial big data analytics is an emerging multidisciplinary field, which incorporates aspects of engineering, statistics and computing, to produce data-driven insights that can enhance operational efficiencies, and produce knowledgebased competitive advantages. Developing industrial big data analytics capabilities is an ongoing process, whereby facilities continuously refine collaborations, workflows and processes to improve operational insights. Such activities should be guided by formal measurement methods, to strategically identify areas for improvement, demonstrate the impact of analytics initiatives, as well as deriving benchmarks across facilities and departments. This research presents a formal multi-dimensional maturity model for approximating industrial analytics capabilities, and demonstrates the model’s ability to assess the impact of an initiative undertaken in a real-world facility.

scholarly journals Implementing Big Data Analytics in Marketing Departments: Mixing Organic and Administered Approaches to Increase Data-Driven Decision Making

Informatics ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 66
Author(s):  
Devon S. Johnson ◽  
Debika Sihi ◽  
Laurent Muzellec
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Survey Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Top Management ◽  
Data Driven Decision Making ◽  
Marketing Department

This study examines the experience of marketing departments to become fully data-driven decision-making organizations. We evaluate an organic approach of departmental sensemaking and an administered approach by which top management increase the influence of analytics skilled employees. Data collection commenced with 15 depth interviews of marketing and analytics professionals in the US and Europe involved in the implementation of big data analytics (BDA) and was followed by a survey data of 298 marketing and analytics middle management professionals at United States based firms. The survey data supports the logic that BDA sensemaking is initiated by top management and is comprised of four primary activities: external knowledge acquisition, improving digitized data quality, big data analytics experimentation and big data analytics information dissemination. Top management drives progress toward data-driven decision-making by facilitating sensemaking and by increasing the influence of BDA skilled employees. This study suggests that while a shift toward enterprise analytics increases the quality of resource available to the marketing department, this approach could stymie the quality of marketing insights gained from BDA. This study presents a model of how to improve the quality of marketing insights and improve data-driven decision-making.

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