Assessing the Overall Impact of Data Analytics on Company Decision Making and Innovation

2021 ◽  
Vol 8 (4) ◽  
pp. 34-51
Author(s):  
Tor Guimaraes ◽  
Ketan Paranjape
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Comprehensive Model ◽  
Business Innovation ◽  
Four Dimensions ◽  
Top Managers ◽  
Technical Proficiency ◽  
The Impact ◽  
Tools And Methods

To assess the impact of big data analytics (BDA) on company decision making, data was collected from 225 company top managers and chief data officers in charge of the BDA group to empirically test this relationship. The data represents a sample of companies which have formally implemented BDA for at least three years with varying degrees of success. Despite considerable differences from company to company, on average the results corroborated the importance of the BDA function along four dimensions (BDA tools and methods, personnel technical proficiency, company readiness, and applications quality) in supporting company decision making toward business innovation. Managers responsible for implementing BDA in their companies should seriously consider these findings to improve the likelihood of success in their projects. The results also call for the identification of other potential determinants for BDA success as a tool for company innovation, as well as potential moderators and mediators for inclusion in a more comprehensive model.

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.

Survey of Deep-learning Techniques in Big-Data analytics

2021 ◽  
Author(s):  
Shubhashish Goswami ◽  
Abhimanyu Kumar
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Deep Learning ◽  
Learning Process ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Operational Parameters ◽  
Learning Techniques ◽  
The Impact ◽  
Business Sectors

Abstract The present elaboration of Big-data research studies relying upon Deep-learning methods had revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms' operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. The present enhancements in the Deep-learning approaches in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition to having more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper elaborates the above statements stating the impact of the Deep-learning process utilizing the Big-data to operate in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing on having the impacts of operational parameters, concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors, the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

Data Analytics Supporting Knowledge Acquisition

2019 ◽  
pp. 146-165
Author(s):  
Soraya Sedkaoui
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Knowledge Acquisition ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Strong Support ◽  
Decision Makers ◽  
Organization Performance ◽  
Challenges And Opportunities ◽  
Tools And Methods

This chapter aims to make the case that analytics methods must respond to the significant changes that big data challenges are bringing to operationalizing the production of information and knowledge. More specifically it discusses the analytics dimension of big data challenges and its contribution for value creation. It shows that data analytics tools and methods offer strong support in knowledge acquisition and discovery. This suggests that the effectiveness of an analytics method must be measured based on how it promotes and enhances knowledge, how it improves patterns and understanding of the decision makers, and thereby how it improves their decision making and hence organization performance. This chapter explores the synergies between big data analytics and knowledge discovery by identifying challenges and opportunities in data analytics applications for knowledge acquisition.

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

Examining the potential of textual big data analytics for public policy decision-making: A case study with driverless cars in Denmark

2020 ◽  
Vol 98 ◽  
pp. 68-78 ◽  
Author(s):  
Aseem Kinra ◽  
Samaneh Beheshti-Kashi ◽  
Rasmus Buch ◽  
Thomas Alexander Sick Nielsen ◽  
Francisco Pereira
Keyword(s):  
Public Policy ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Policy Decision ◽  
Driverless Cars ◽  
Policy Decision Making

Analysis of Big Data Analytics in Healthcare Sector: Applications and Tools

2020 ◽  
Vol 17 (12) ◽  
pp. 5605-5612
Author(s):  
A. Kaliappan ◽  
D. Chitra
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Health Sector ◽  
Big Data Analytics ◽  
High Volume ◽  
Healthcare Sector ◽  
Genome Database ◽  
Tremendous Amount ◽  
The Impact ◽  
Different Sources

In today’s world, an immense measure of information in the form of unstructured, semi-structured and unstructured is generated by different sources all over the world in a tremendous amount. Big data is the termed coined to address these enormous amounts of data. One of the major challenges in the health sector is handling a high-volume variety of data generated from diverse sources and utilizing it for the wellbeing of human. Big data analytics is one of technique designed to operate with monstrous measures of information. The impact of big data in healthcare field and utilization of Hadoop system tools for supervising the big data are deliberated in this paper. The big data analytics role and its theoretical and conceptual architecture include the gathering of diverse information’s such as electronic health records, genome database and clinical decisions support systems, text representation in health care industry is investigated in this paper.

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