Big Data and Data Analytics Research

The Big Data and Data Analytics is a brand new paradigm, for the integration of Internet Technology in the human and machine context. For the first time in the history of the human mankind we are able to transforming raw data that are massively produced by humans and machines in to knowledge and wisdom capable of supporting smart decision making, innovative services, new business models, innovation, and entrepreneurship. For the Web Science research, this is a new methodological and technological spectrum of advanced methods, frameworks and functionalities never experienced in the past. At the same moment communities out of web science need to realize the potential of this new paradigm with the support of new sound business models and a critical shift in the perception of decision making. In this short visioning article, the authors are analyzing the main aspects of Big Data and Data Analytics Research and they provide their own metaphor for the next years. A number of research directions are outlined as well as a new roadmap towards the evolution of Big Data to Smart Decisions and Cognitive Computing. The authors do hope that the readers would like to react and to propose their own value propositions for the domain initiating a scientific dialogue beyond self-fulfilled expectations.

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Understanding Data Analytics Is Good but Knowing How to Use It Is Better!

Big Data Analytics for Entrepreneurial Success - Advances in Business Information Systems and Analytics ◽

10.4018/978-1-5225-7609-9.ch005 ◽

2019 ◽

pp. 125-160

Keyword(s):

Big Data ◽

Data Analytics ◽

Business Models ◽

Big Data Analytics ◽

Success Factor ◽

New Business ◽

Technical Architecture ◽

The Veil ◽

Key Success Factor ◽

New Business Models

Collecting the data and being able to generate value from it: this is certainly the key success factor of tomorrow's champions, one that will allow you to innovate and create new business models. Faced with the 3Vs of big data, many companies are embarking on big data projects with the main objective: generating value. The goal is to succeed, by the detailed analysis of large amounts of data, to lift the veil and discover hitherto hidden models and barely perceptible correlations, as many new business opportunities that companies must grasp. The key to the success of any big data analytics initiative is to define your goals, identify specific business questions that a suitable technical architecture will need to answer, and use the data experts to generate value from data by using specific algorithms.

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Datenbasierte Produktionsoptimierung*/Data-driven production optimization: Areas of development for manufacturing companies

wt Werkstattstechnik online ◽

10.37544/1436-4980-2018-03-4 ◽

2018 ◽

Vol 108 (03) ◽

pp. 108-112

Author(s):

D. Bauer ◽

T. Maurer ◽

T. Bauernhansl

Keyword(s):

Big Data ◽

Data Analytics ◽

Applied Research ◽

Business Models ◽

Big Data Analytics ◽

Production Optimization ◽

Data Driven ◽

Manufacturing Companies ◽

New Business ◽

New Business Models

Unternehmen sehen in Big-Data-Analysen ein großes Potenzial zur Optimierung der klassischen Produktionsziele sowie zur Entwicklung neuer Geschäftsmodelle. Eine Studie des Fraunhofer IPA analysiert, welche Herausforderungen bei der Umsetzung dieser Potenziale auftreten. Darauf aufbauend werden Entwicklungsfelder für die angewandte Forschung und produzierende Unternehmen erarbeitet.   Companies expect huge benefits from big data analytics both to improve traditional production targets and to develop new business models. A study conducted by Fraunhofer IPA analyzes the upcoming challenges in exploiting these opportunities. It provides the basis for identifying areas of development for applied research and for manufacturing companies.

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Business Models and Ecosystem for Big Data

The Elements of Big Data Value ◽

10.1007/978-3-030-68176-0_11 ◽

2021 ◽

pp. 269-288

Author(s):

Sonja Zillner

Keyword(s):

Big Data ◽

Virtual Worlds ◽

Business Models ◽

Value Added ◽

Systematic Investigation ◽

Data Driven ◽

Strategic Partnerships ◽

New Business ◽

Big Data Technologies ◽

Value Propositions

AbstractWith the recent technical advances in digitalisation and big data, the real and the virtual worlds are continuously merging, which, again, leads to entire value-added chains being digitalised and integrated. The increase in industrial data combined with big data technologies triggers a wide range of new technical applications with new forms of value propositions that shift the logic of how business is done. To capture these new types of value, data-driven solutions for the industry will require new business models. The design of data-driven AI-based business models needs to incorporate various perspectives ranging from customer and user needs and their willingness to pay for new data-driven solutions to data access and the optimal use of technologies, while taking into account the currently established relationships with customers and partners. Successful data-driven business models are often based on strategic partnerships, with two or more players establishing the basis for sustainable win-win situations through transparent resource-, investment-, risk-, data- and value-sharing. This chapter will explore the different data-driven business approaches and highlight in this context the importance of functioning ecosystems on the various levels. The chapter will conclude with an introduction to the data-driven innovation framework, a proven methodology to guide the systematic investigation of data-driven business opportunities while incorporating the dynamics of the underlying ecosystems.

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