Innovation Platforms

2018 ◽  
pp. 72-95
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Business Model ◽  
Value Chain ◽  
Analytical Techniques ◽  
Human Interaction ◽  
Business Relationship ◽  
Potential Relationship ◽  
Relationship Of ◽  
Cross Fertilization

Data and analytics is the heart of a digital business platform. Today, big data (BD) becomes useful when it enriches decision making that is enhanced by application of analytical techniques and some element of human interaction. With the merging of data and information vs. knowledge and intelligence, this chapter investigates an opportunity for cross-fertilization between BD and the field of digital business with related disciplines. Primary BD and analytics platform is a set of business capabilities. This chapter aims to investigate the potential relationship of BD and analytics platform and digital business platform. In doing so, it develops a BD value chain framework, BD business model pattern (BDBMP) with related levels of BD maturity improvement. This framework could be used to find answers on the basic BD and digital business relationship questions.

Analytics based decision making

2014 ◽  
Vol 6 (4) ◽  
pp. 332-340 ◽  
Author(s):  
Deepak Agrawal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Design Methodology ◽  
Historical Perspective ◽  
Big Data Analytics ◽  
Analytical Techniques ◽  
Content Type ◽  
Informed Decisions ◽  
Different Types

Purpose – This paper aims to trace the history, application areas and users of Classical Analytics and Big Data Analytics. Design/methodology/approach – The paper discusses different types of Classical and Big Data Analytical techniques and application areas from the early days to present day. Findings – Businesses can benefit from a deeper understanding of Classical and Big Data Analytics to make better and more informed decisions. Originality/value – This is a historical perspective from the early days of analytics to present day use of analytics.

Toward a Conceptualization of Big Data Value Chain

2019 ◽  
pp. 76-92 ◽  
Author(s):  
Rim Louati ◽  
Sonia Mekadmi
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Value Chain ◽  
Data Capture ◽  
Digital Devices ◽  
Business Executives ◽  
Data Value ◽  
Near Future ◽  
Different Sources

The generation of digital devices such as web 2.0, smartphones, social media and sensors has led to a growing rate of data creation. The volume of data available today for organizations is big. Data are produced extensively every day in many forms and from many different sources. Accordingly, firms in several industries are increasingly interested in how to leverage on these “big data” to draw valuable insights from the various kinds of data and to create business value. The aim of this chapter is to provide an integrated view of big data management. A conceptualization of big data value chain is proposed as a research model to help firms understand how to cope with challenges, risks and benefits of big data. The suggested big data value chain recognizes the interdependence between processes, from business problem identification and data capture to generation of valuable insights and decision making. This framework could provide some guidance to business executives and IT practitioners who are going to conduct big data projects in the near future.

The Potential of Big Data Analysis in the Shipbuilding Industry: A Way of Increasing Competitiveness

2021 ◽  
pp. 53-74
Author(s):  
Andrea Cappelli ◽  
Iacopo Cavallini
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Value Chain ◽  
Information Needs ◽  
Demand Forecasting ◽  
Big Data Analysis ◽  
Production Costs ◽  
Shipbuilding Industry ◽  
Analysis Methods

It is possible to exploit potentials of Big Data in the shipbuilding industry in order to increase efficiency and company performance. Big Data analysis will probably have a great impact on strengthening the competitiveness in the whole sector, providing various types of benefits and effective support to the decision-making system. Academics maintain that analysis methods and algorithms can offer spe-cific guidelines to managers and practitioners in order to satisfy their information needs. Even though it is recognized that the techniques for Big Data analysis are relevant, only a few studies provide practical guidelines on how to apply these techniques in specific industries like shipbuilding. This preliminary study aims to develop a conceptual framework of Big Data anal-ysis based on the value chain approach. By using a deductive methodology, the framework is built taking into consideration four phases of the value chain in the shipbuilding industry - i.e. pre-production, design, production, and post-production. For its relevance, the study considers the pre-production phase, trying to classify data sources, analysis methods, and algorithms for the main activities of this node and also providing various suggestions to shipbuilding managers and practitioners. The researchers develop the framework by considering secondary data collected from the literature analysis. Our results can successfully support decision making in shipbuilding companies, making processes and operations more cost-effective and helping companies be more competitive. Specifically, in the pre-production node this will lead to real-time demand forecasting and a more reliable estimation of initial production costs.

scholarly journals RELATIONSHIP BETWEEN BIG DATA AND DECISION SUPPORT SYSTEMS

2020 ◽  
Vol 11 (1) ◽  
pp. 82-93
Author(s):  
Gustavo Grander ◽  
Luciano Ferreira da Silva ◽  
Alan Tadeu Moraes Moraes ◽  
Paulo Sergio Gonçalves de Oliveira
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Analytical Techniques ◽  
Strong Relationship ◽  
Decision Making Processes ◽  
Scopus Database

This article aimed to identify relationships between Big Data and Decision Support Systems. For this, we conducted a search in the Scopus database and as a result, we identified a report according to the increased frequency of publications, frequency of publications in journals and, using the VOSviewer software, we performed an analysis of words co-citation. We identified 5 groups of keywords that suggest different areas of study (e.g., logistics, health and social media), as well as a more recent focus on studies aimed at sustainable development, machine learning, analytical techniques and decision-making processes decision. An important contribution that should also be highlighted was the strong relationship between the keywords Big Data, artificial intelligence and decision making, suggesting studies involving the three terms in a large number of works. 

scholarly journals Analytical Techniques for Decision Making on Information Security for Big Data Breaches

2018 ◽  
Vol 17 (02) ◽  
pp. 527-545 ◽  
Author(s):  
Aiiad Albeshri ◽  
Vijey Thayananthan
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Information Security ◽  
Access Control ◽  
Key Management ◽  
Analytical Techniques ◽  
Data Infrastructure ◽  
Online Systems ◽  
Data Breaches ◽  
Priority Model

In the big data processes, management and analytics are primary areas where we can introduce the decision making on information security to mitigate the big data breaches. According to the growing number of online systems and big data handling, mitigating the big data breaches is the serious problem during the processing period which needs to be monitored using appropriate technique. The goal of this research is to prevent the big data breaches using correct decision making based on information security concepts such as access control with authentication which depend on the management policies. The analytical approach of information security solution can also be useful for securing the big data infrastructure and key management that improve the big data breaches. As an analytical method, information security which focuses on detecting and securing the big data breaches is considered with access control. Here, we have introduced the multi-priority model influenced with the network calculus and access control which monitors the breaches during the big data processing. In the results and analysis, we can provide a graph which shows the monitoring improvement for decision making during the mitigation of big data breaches.

Toward a Conceptualization of Big Data Value Chain

2022 ◽  
pp. 319-335
Author(s):  
Rim Louati ◽  
Sonia Mekadmi
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Value Chain ◽  
Data Capture ◽  
Digital Devices ◽  
Business Executives ◽  
Data Value ◽  
Near Future ◽  
Different Sources

The generation of digital devices such as web 2.0, smartphones, social media and sensors has led to a growing rate of data creation. The volume of data available today for organizations is big. Data are produced extensively every day in many forms and from many different sources. Accordingly, firms in several industries are increasingly interested in how to leverage on these “big data” to draw valuable insights from the various kinds of data and to create business value. The aim of this chapter is to provide an integrated view of big data management. A conceptualization of big data value chain is proposed as a research model to help firms understand how to cope with challenges, risks and benefits of big data. The suggested big data value chain recognizes the interdependence between processes, from business problem identification and data capture to generation of valuable insights and decision making. This framework could provide some guidance to business executives and IT practitioners who are going to conduct big data projects in the near future.

Big Data and the Paradox of Diversity

2016 ◽  
Vol 2 (2) ◽  
pp. 39-54 ◽  
Author(s):  
Bernhard Rieder
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Analytical Techniques ◽  
Guiding Principle ◽  
Societal Context ◽  
Traditional Societies ◽  
The Many

Abstract This paper develops a critique of Big Data and associated analytical techniques by focusing not on errors - skewed or imperfect datasets, false positives, underrepresentation, and so forth - but on data mining that works. After a quick framing of these practices as interested readings of reality, I address the question of how data analytics and, in particular, machine learning reveal and operate on the structured and unequal character of contemporary societies, installing “economic morality” (Allen 2012) as the central guiding principle. Rather than critiquing the methods behind Big Data, I inquire into the way these methods make the many differences in decentred, non-traditional societies knowable and, as a consequence, ready for profitable distinction and decision-making. The objective, in short, is to add to our understanding of the “profound ideological role at the intersection of sociality, research, and commerce” (van Dijck 2014: 201) the collection and analysis of large quantities of multifarious data have come to play. Such an understanding needs to embed Big Data in a larger, more fundamental critique of the societal context it operates in.

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