scholarly journals Big data monetization throughout Big Data Value Chain: a comprehensive review

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Abou Zakaria Faroukhi ◽  
Imane El Alaoui ◽  
Youssef Gahi ◽  
Aouatif Amine
Keyword(s):  
Big Data ◽  
Value Creation ◽  
Value Chain ◽  
Big Data Analytics ◽  
High Volume ◽  
Value Chains ◽  
Data Generation ◽  
Comprehensive Review ◽  
Data Value ◽  
End To End

AbstractValue Chain has been considered as a key model for managing efficiently value creation processes within organizations. However, with the digitization of the end-to-end processes which began to adopt data as a main source of value, traditional value chain models have become outdated. For this, researchers have developed new value chain models, called Data Value Chains, to carry out data driven organizations. Thereafter, new data value chains called Big Data Value chain have emerged with the emergence of Big Data in order to face new data-related challenges such as high volume, velocity, and variety. These Big Data Value Chains describe the data flow within organizations which rely on Big Data to extract valuable insights. It is a set of ordered steps using Big Data Analytics tools and mainly built for going from data generation to knowledge creation. The advances in Big Data and Big Data Value Chain, using clear processes for aggregation and exploitation of data, have given rise to what is called data monetization. Data monetization concept consists of using data from an organization to generate profit. It may be selling the data directly for cash, or relying on that data to create value indirectly. It is important to mention that the concept of monetizing data is not as new as it looks, but with the era of Big Data and Big Data Value Chain it is becoming attractive. The aim of this paper is to provide a comprehensive review of value creation, data value, and Big Data value chains with their different steps. This literature has led us to construct an end-to-end exhaustive BDVC that regroup most of the addressed phases. Furthermore, we present a possible evolution of that generic BDVC to support Big Data Monetization. For this, we discuss different approaches that enable data monetization throughout data value chains. Finally, we highlight the need to adopt specific data monetization models to suit big data specificities.

scholarly journals An Adaptable Big Data Value Chain Framework for End-to-End Big Data Monetization

2020 ◽  
Vol 4 (4) ◽  
pp. 34
Author(s):  
Abou Zakaria Faroukhi ◽  
Imane El Alaoui ◽  
Youssef Gahi ◽  
Aouatif Amine
Keyword(s):  
Big Data ◽  
Value Chain ◽  
Business Models ◽  
Data Driven ◽  
Business Operations ◽  
Data Value ◽  
End To End ◽  
Almost All ◽  
Theoretical Foundations ◽  
Support Decision Making

Today, almost all active organizations manage a large amount of data from their business operations with partners, customers, and even competitors. They rely on Data Value Chain (DVC) models to handle data processes and extract hidden values to obtain reliable insights. With the advent of Big Data, operations have become increasingly more data-driven, facing new challenges related to volume, variety, and velocity, and giving birth to another type of value chain called Big Data Value Chain (BDVC). Organizations have become increasingly interested in this kind of value chain to extract confined knowledge and monetize their data assets efficiently. However, few contributions to this field have addressed the BDVC in a synoptic way by considering Big Data monetization. This paper aims to provide an exhaustive and expanded BDVC framework. This end-to-end framework allows us to handle Big Data monetization to make organizations’ processes entirely data-driven, support decision-making, and facilitate value co-creation. For this, we present a comprehensive review of existing BDVC models relying on some definitions and theoretical foundations of data monetization. Next, we expose research carried out on data monetization strategies and business models. Then, we offer a global and generic BDVC framework that supports most of the required phases to achieve data valorization. Furthermore, we present both a reduced and full monetization model to support many co-creation contexts along the BDVC.

scholarly journals Łańcuch wartości dużych zbiorów danych (big data) w rolnictwie – problemy i wyzwania regulacyjne

Studia BAS ◽  
2020 ◽  
Vol 3 (63) ◽  
pp. 101-125
Author(s):  
Katarzyna Kosior
Keyword(s):  
Big Data ◽  
Value Chain ◽  
Data Analytics ◽  
Codes Of Conduct ◽  
Big Data Analytics ◽  
Farming Systems ◽  
Developed Countries ◽  
Data Sets ◽  
Data Value ◽  
The Developed Countries

The aim of the article is to take a closer look at the emerging big data value chain in agriculture and contribute to a better understanding of major regulatory problems and challenges that relate to the development and functioning of the said value chain. The analysis encompasses cases and experiences gained in the developed countries, and particularly in the EU. Currently, there are no specific regulations or public policies that would apply to big data sets and big data analytics in agriculture. The development trajectories of digital agriculture (or smart farming) are shaped primarily by provisions included in private contracts that bind farmers with agricultural technology providers. The approach to data analytics in such ecosystems is basically driven by the logic of corporate interests, which implies that lesser attention is being paid to general development needs of the sector or broader social interests. The current organization of the big data value chain basically favors the largest and the wealthiest farms. These patterns may contribute to increasing income inequalities in the sector. In the longer term, they may also harm sustainable farming systems. Although informal codes of conduct developed at industry level provide for general standards for agricultural data sharing and use, there is a need for specific regulations and policies that would support sustainable and inclusive digital transformation in agriculture. Taking into account the broader public value of aggregated agricultural data sets, such regulations and policies should particularly encourage a closer cooperation between the public and the private sector.

scholarly journals Challenges and Research Disputes and Tools in Big Data Analytics

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1949-1952
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Storage ◽  
Value Chain ◽  
Data Analytics ◽  
Large Scale ◽  
Fault Tolerant ◽  
Big Data Analytics ◽  
Data Generation ◽  
Data Systems

Big Data is the era of data processing. Big Data is the Collate’s observer data sets that are complicated that traditional data-processing abilities.There are the various challenges include data analysis, capture the data, curation, search, sharing, stowage, transmission, visualization, and privacy violations. A large collections of petabytes of data is engendered day by day from the up-to-date information systems and digital era such as Internet of Things and cloud computing. Big data environs is used to attain, organize and analyse the numerous types of data. A large scale distributed file system which should be a fault tolerant, flexible and scalable. The term big data comes with the new challenges to input, process and output the data The technologies used by big data application to handle the massive data are Hadoop, Map Reduce, Pig, Apache Hive, No SQL and Spark. Initially, we extant the definition of big data and discuss big data challenges. Succeeding, The Propionate Paramour of Big Data Systems Models in the Into Prolonging Seam, Namely data Generation, data Assange, data Storage, and data Analytics. These four modules form a big data value chain. In accumulation, we present the prevalent Hadoop framework for addressing big data.

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.

Big Data and Big Data Analytics for Improved Healthcare Service and Management

2020 ◽  
Vol 8 (1) ◽  
pp. 13-51
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Moutan Mukhopadhyay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Future Market ◽  
Data Generation ◽  
Related Data ◽  
Healthcare Data ◽  
Different Types

Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.

Creating multiple value streams from big data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Keyword(s):  
Big Data ◽  
Value Creation ◽  
Content Type ◽  
It Alignment ◽  
Process Creation ◽  
Data Value ◽  
Value Streams ◽  
Pertinent Information ◽  
Multiple Value ◽  
Process Alignment

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings This research paper determines how service supply chains can create value with big data, by building cross-departmental processes. Based on the study’s results, the critical alignment capabilities for successful big data value creation are: IT-process alignment; IT-performance alignment; performance-process alignment; human-IT alignment; and human-process alignment. Additionally, overarching and underlying strategic and organizational alignment capabilities also impacted this value creation. The human impact on employees of big data-led process creation shouldn’t be underestimated. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

INNOVATIVE TRANSFORMATIONS OF THE VALUE CHAIN OF PHARMACEUTICAL ENTERPRISES

2021 ◽  
Author(s):  
Катерина Копішинська ◽  
Катерина Зінченко
Keyword(s):  
Correlation Analysis ◽  
Linear Relationship ◽  
Value Creation ◽  
Value Chain ◽  
Value Chains ◽  
High Tech ◽  
Pharmaceutical Companies ◽  
Development Scenarios ◽  
Current State ◽  
Pharmaceutical Enterprises

The research is devoted to the substantiation of the necessity of innovative transformations of the value chain of pharmaceutical enterprises. The current state of the international pharmaceutical market and its development scenarios developed by the WTO were analyzed, taking into account the changes caused by the COVID-19 coronavirus pandemic. The typology of value chains is considered and their element-by-element characteristics are given. A new, modern model of interaction in the chain of value creation of products is proposed. The substantiation of efficiency of creation of such chains is given. Based on the correlation analysis, the presence of a linear relationship between the indicators of Pharmaceutical R&D Spend and Revenue was established. To maximize the effect of R&D costs, pharmaceutical companies are recommended to carry out innovative transformations of the value chain, involving external manufacturers of high-tech devices, applications, etc.

The Compute Infrastructures for Big Data Analytics

2014 ◽  
pp. 74-109 ◽  
Author(s):  
Pethuru Raj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sources ◽  
Steady Increase ◽  
Data Generation ◽  
Abnormal Growth ◽  
The World ◽  
Tremendous Amount ◽  
And Storage

The implications of the digitization process among a bevy of trends are definitely many and memorable. One is the abnormal growth in data generation, gathering, and storage due to a steady increase in the number of data sources, structures, scopes, sizes, and speeds. In this chapter, the author shows some of the impactful developments brewing in the IT space, how the tremendous amount of data getting produced and processed all over the world impacts the IT and business domains, how next-generation IT infrastructures are accordingly getting refactored, remedied, and readied for the impending big data-induced challenges, how likely the move of the big data analytics discipline towards fulfilling the digital universe requirements of extracting and extrapolating actionable insights for the knowledge-parched is, and finally, the establishment and sustenance of the dreamt smarter planet.

Big Data Analytics in Bioinformatics

Biotechnology ◽  
2019 ◽  
pp. 1967-1984
Author(s):  
Dharmendra Trikamlal Patel
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Annual Growth ◽  
Predictive Analysis ◽  
The Internet ◽  
Data Generation ◽  
Intelligent Devices ◽  
The Internet Of Things

Voluminous data are being generated by various means. The Internet of Things (IoT) has emerged recently to group all manmade artificial things around us. Due to intelligent devices, the annual growth of data generation has increased rapidly, and it is expected that by 2020, it will reach more than 40 trillion GB. Data generated through devices are in unstructured form. Traditional techniques of descriptive and predictive analysis are not enough for that. Big Data Analytics have emerged to perform descriptive and predictive analysis on such voluminous data. This chapter first deals with the introduction to Big Data Analytics. Big Data Analytics is very essential in Bioinformatics field as the size of human genome sometimes reaches 200 GB. The chapter next deals with different types of big data in Bioinformatics. The chapter describes several problems and challenges based on big data in Bioinformatics. Finally, the chapter deals with techniques of Big Data Analytics in the Bioinformatics field.

Sign in / Sign up

Export Citation Format

Share Document