Big data: Value creation in clinical nutrition

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.

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Research on Big Data Value Creation System Applied to CPS

2021 7th IEEE Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS) ◽

10.1109/bigdatasecurityhpscids52275.2021.00019 ◽

2021 ◽

Author(s):

Xiaoyan Chen ◽

Jiangling Ge ◽

Wei Huang

Keyword(s):

Big Data ◽

Value Creation ◽

Data Value

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Connecting big data management capabilities with employee ambidexterity in Chinese multinational enterprises through the mediation of big data value creation at the employee level

International Business Review ◽

10.1016/j.ibusrev.2019.101604 ◽

2020 ◽

Vol 29 (6) ◽

pp. 101604 ◽

Cited By ~ 4

Author(s):

Saqib Shamim ◽

Jing Zeng ◽

Umair Shafi Choksy ◽

Syed Muhammad Shariq

Keyword(s):

Big Data ◽

Data Management ◽

Value Creation ◽

Multinational Enterprises ◽

Data Value ◽

Management Capabilities ◽

Chinese Multinational Enterprises

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

Journal Of Big Data ◽

10.1186/s40537-019-0281-5 ◽

2020 ◽

Vol 7 (1) ◽

Cited By ~ 8

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.

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Big Data Value Creation by Example

The Elements of Big Data Value ◽

10.1007/978-3-030-68176-0_10 ◽

2021 ◽

pp. 245-268

Author(s):

Jean-Christophe Pazzaglia ◽

Daniel Alonso

Keyword(s):

Machine Learning ◽

Big Data ◽

European Commission ◽

Satellite Data ◽

Value Creation ◽

Business Practices ◽

Private Partnership ◽

Data Value ◽

Value Association ◽

Start Ups

AbstractThe Big Data Value contractual Public-Private Partnership between the European Commission and the Big Data Value Association (BDVA) was signed in October 2014. Since then, more than 50 projects and numerous BDVA members have explored how data can drive innovation across the data stack and how industries can transform business practices. Meanwhile, start-ups have been working at the confluence of new sources of data (e.g. IoT, DNA, HD pictures, satellite data) and new or revisited processing paradigms (e.g. Edge computing, blockchain, machine learning) to tackle new use cases and to provide disruptive solutions for known problems. This chapter details a collection of stories showing concrete examples of the value created thanks to a renewed usage of data.

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Firm-level capabilities towards big data value creation

Journal of Business Research ◽

10.1016/j.jbusres.2020.07.036 ◽

2020 ◽

Author(s):

Morten Brinch ◽

Angappa Gunasekaran ◽

Samuel Fosso Wamba

Keyword(s):

Big Data ◽

Value Creation ◽

Firm Level ◽

Data Value

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A recipe for big data value creation

Business Process Management Journal ◽

10.1108/bpmj-03-2018-0082 ◽

2019 ◽

Vol 25 (5) ◽

pp. 1085-1100 ◽

Cited By ~ 3

Author(s):

Ossi Ylijoki ◽

Jari Porras

Keyword(s):

Big Data ◽

Value Creation ◽

Success Factors ◽

Process Theory ◽

Performance Impact ◽

Holistic Model ◽

Content Type ◽

Business Context ◽

Data Value ◽

Probabilistic Processes

PurposeThe purpose of this paper is to present a process-theory-based model of big data value creation in a business context. The authors approach the topic from the viewpoint of a single firm.Design/methodology/approachThe authors reflect current big data literature in two widely used value creation frameworks and arrange the results according to a process theory perspective.FindingsThe model, consisting of four probabilistic processes, provides a “recipe” for converting big data investments into firm performance. The provided recipe helps practitioners to understand the ingredients and complexities that may promote or demote the performance impact of big data in a business context.Practical implicationsThe model acts as a framework which helps to understand the necessary conditions and their relationships in the conversion process. This helps to focus on success factors which promote positive performance.Originality/valueUsing well-established frameworks and process components, the authors synthetize big data value creation-related papers into a holistic model which explains how big data investments translate into economic performance, and why the conversion sometimes fails. While the authors rely on existing theories and frameworks, the authors claim that the arrangement and application of the elements to the big data context is novel.

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