scholarly journals Big Data for the Sustainability of Healthcare Project Financing

2019 ◽  
Vol 11 (13) ◽  
pp. 3748 ◽  
Author(s):  
Roberto Moro Visconti ◽  
Donato Morea
Keyword(s):  
Big Data ◽  
Business Model ◽  
Predictive Analytics ◽  
Interdisciplinary Approach ◽  
Data Driven ◽  
Project Finance ◽  
Public And Private ◽  
Smart Healthcare ◽  
Economic Marginality ◽  
Sustainable Planning

This study aims to detect if and how big data can improve the quality and timeliness of information in infrastructural healthcare Project Finance (PF) investments, making them more sustainable, and increasing their overall efficiency. Interactions with telemedicine or disease management and prediction are promising but are still underexploited. However, given rising health expenditure and shrinking budgets, data-driven cost-cutting is inevitably required. An interdisciplinary approach combines complementary aspects concerning big data, healthcare information technology, and PF investments. The methodology is based on a business plan of a standard healthcare Public-Private Partnership (PPP) investment, compared with a big data-driven business model that incorporates predictive analytics in different scenarios. When Public and Private Partners interact through networking big data and interoperable databases, they boost value co-creation, improving Value for Money and reducing risk. Big data can also help by shortening supply chain steps, expanding economic marginality and easing the sustainable planning of smart healthcare investments. Flexibility, driven by timely big data feedbacks, contributes to reducing the intrinsic rigidity of long-termed PF healthcare investments. Healthcare is a highly networked and systemic industry, that can benefit from interacting with big data that provide timely feedbacks for continuous business model re-engineering, reducing the distance between forecasts and actual occurrences. Risk shrinks and sustainability is fostered, together with the bankability of the infrastructural investment.

Building an Analytics Culture to Boost a Data-Driven Entrepreneur's Business Model

2019 ◽  
pp. 260-291
Author(s):  
Soraya Sedkaoui ◽  
Mounia Khelfaoui
Keyword(s):  
Big Data ◽  
Small Business ◽  
Business Model ◽  
Business Process ◽  
Data Analytics ◽  
Data Driven ◽  
Business And Society ◽  
Crucial Importance

This chapter treats the movement that marks, affects, and transforms any part of business and society. It is about big data that is creating, and the value generating that companies, startups, and entrepreneurs have to derive through sophisticated methods and advanced tools. This chapter suggests that analytics can be of crucial importance for business and entrepreneurial practices if correctly aligned with business process needs and can also lead to significant improvement of their performance and quality of the decisions they make. So, the main purpose of this chapter are exploring why small business, entrepreneur, and startups have to use data analytics and how they can integrate, operationally, analytics methods to extract value and create new opportunities.

Survey of the Different Type of Data Analytics Algorithms

2017 ◽  
Vol 8 (1) ◽  
pp. 50-64 ◽  
Author(s):  
Michael Kevin Hernandez
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
High Volume ◽  
Data Driven ◽  
Advantages And Disadvantages ◽  
Definition Of ◽  
Data Driven Decisions ◽  
Quantitative Definition

From as early as 1854 to today, society has been gathering, processing, transforming, modeling and visualizing data to help drive data-driven decisions. The qualitative definition of big data can be defined more conclusively as data that has high volume, velocity, and variety. Whereas, the quantitative definition of big data does vary with respect to time due to the dependence of the time's technology and processing capabilities. However, making use of that big data to facilitate data-driven decisions, one should employ either descriptive, predictive, or prescriptive analytics. This article has discussed and summarized the advantages and disadvantages of the algorithms that fell under descriptive and predictive analytics. Given the sheer number of the different types of algorithms and the amount of versatile data mining software available sometimes, the best big data analytics results can come from mixing two to three of the mentioned algorithms.

Big data-driven business model innovation by traditional industries in the Chinese economy

2017 ◽  
Vol 10 (3) ◽  
pp. 229-251 ◽  
Author(s):  
Sarah Cheah ◽  
Shenghui Wang
Keyword(s):  
Big Data ◽  
Business Model ◽  
Deductive Reasoning ◽  
Production Systems ◽  
Business Model Innovation ◽  
Chinese Economy ◽  
Data Driven ◽  
Economic Resource ◽  
Content Type ◽  
Integrated Framework

Purpose This study aims to construct mechanisms of big data-driven business model innovation from the market, strategic and economic perspectives and core logic of business model innovation. Design/methodology/approach The authors applied deductive reasoning and case analysis method on manufacturing firms in China to validate the mechanisms. Findings The authors have developed an integrated framework to deduce the elements of big data-driven business model innovation. The framework comprises three elements: perspectives, business model processes and big data-driven business model innovations. As we apply the framework on to three Chinese companies, it is evident that the mechanisms of business model innovation based on big data is a progressive and dynamic process. Research limitations/implications The case sample is relatively small, which is a typical trade-off in qualitative research. Practical implications A robust infrastructure that seamlessly integrates internet of things, front-end customer systems and back-end production systems is pivotal for companies. The management has to ensure its organization structure, climate and human resources are well prepared for the transformation. Social implications When provided with a convenient crowdsourcing platform to provide feedback and witness their suggestions being implemented, users are more likely to share insights about their use experience. Originality/value Extant studies of big data and business model innovation remain disparate. By adding a new dimension of intellectual and economic resource to the resource-based view, this paper posits an important link between big data and business model innovation. In addition, this study has contributed to the theoretical lens of value by contextualizing the value components of a business model and providing an integrated framework.

scholarly journals Capturing value from big data – a taxonomy of data-driven business models used by start-up firms

2016 ◽  
Vol 36 (10) ◽  
pp. 1382-1406 ◽  
Author(s):  
Philipp Max Hartmann ◽  
Mohamed Zaki ◽  
Niels Feldmann ◽  
Andy Neely
Keyword(s):  
Big Data ◽  
Business Model ◽  
Business Models ◽  
Clustering Algorithms ◽  
Reference Points ◽  
Data Driven ◽  
Content Type ◽  
Start Up ◽  
Different Types ◽  
Start Ups

Purpose The purpose of this paper is to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business models (DDBMs). By providing a framework to systematically analyse DDBMs, the study provides an introduction to DDBM as a field of study. Design/methodology/approach To develop the taxonomy of DDBMs, business model descriptions of 100 randomly chosen start-up firms were coded using a DDBM framework derived from literature, comprising six dimensions with 35 features. Subsequent application of clustering algorithms produced six different types of DDBM, validated by case studies from the study’s sample. Findings The taxonomy derived from the research consists of six different types of DDBM among start-ups. These types are characterised by a subset of six of nine clustering variables from the DDBM framework. Practical implications A major contribution of the paper is the designed framework, which stimulates thinking about the nature and future of DDBMs. The proposed taxonomy will help organisations to position their activities in the current DDBM landscape. Moreover, framework and taxonomy may lead to a DDBM design toolbox. Originality/value This paper develops a basis for understanding how start-ups build business models capture value from data as a key resource, adding a business perspective to the discussion of big data. By offering the scientific community a specific framework of business model features and a subsequent taxonomy, the paper provides reference points and serves as a foundation for future studies of DDBMs.

scholarly journals A Roadmap to Drive Adoption of Data Ecosystems

2021 ◽  
pp. 41-62
Author(s):  
Sonja Zillner ◽  
Laure Le Bars ◽  
Nuria de Lama ◽  
Simon Scerri ◽  
Ana García Robles ◽  
...  
Keyword(s):  
Big Data ◽  
Business Models ◽  
Interdisciplinary Approach ◽  
Data Driven ◽  
Societal Benefit ◽  
European Data ◽  
Strategic Approach ◽  
Data Value ◽  
Data Ecosystem ◽  
Access To Data

AbstractTo support the adoption of big data value, it is essential to foster, strengthen, and support the development of big data value technologies, successful use cases and data-driven business models. At the same time, it is necessary to deal with many different aspects of an increasingly complex data ecosystem. Creating a productive ecosystem for big data and driving accelerated adoption requires an interdisciplinary approach addressing a wide range of challenges from access to data and infrastructure, to technical barriers, skills, and policy and regulation. In order to overcome the adoption challenges, collective action from all stakeholders in an effective, holistic and coherent manner is required. To this end, the Big Data Value Public-Private Partnership (BDV PPP) was established to develop the European data ecosystem and enable data-driven digital transformation, delivering maximum economic and societal benefit, and achieving and sustaining Europe’s leadership in the fields of big data value creation and Artificial Intelligence. This chapter describes the different steps that have been taken to address the big data value adoption challenges: first, the establishment of the BDV PPP to mobilise and create coherence with all stakeholders in the European data ecosystem; second, the introduction of five strategic mechanisms to encourage cooperation and coordination in the data ecosystem; third, a three-phase roadmap to guide the development of a healthy European data ecosystem; and fourth, a systematic and strategic approach towards actively engaging the key communities in the European Data Value Ecosystem.

scholarly journals Exploring Futuring and Predictive Analytics for Developing Organizational Strategy

2020 ◽  
Vol 1 (4) ◽  
pp. 1-9
Author(s):  
Victor Alan Starns
Keyword(s):  
Big Data ◽  
Leadership Styles ◽  
Predictive Analytics ◽  
Adaptive Leadership ◽  
Public And Private ◽  
Business Decisions ◽  
The Public ◽  
History Of ◽  
The Relationship

The study addresses the type of leadership styles that promote Innovation, transformational, and adaptive leadership—in this study of futuring, discussing the evolving role of the futurist within the organization. The discussion of the history of futurists within the organization for the public and private sectors looks at the part of the futurist to determine the relevance of today compared to 20 years ago. Also, it looks at how the futurist can utilize the futurist's techniques in their organization. The study of predictive analytics addresses the use of predictive analytics with examples of companies that use predictive analytics, and the reasons they use predictive analytics—examining predictive analytics to determine if they are driving more informed business decisions. The article explores the relationship between big data and predictive analytics and if big data is responsible for the popularity of predictive analytics.

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