A Data Governance Framework for Industry 4.0

2021 ◽  
Vol 19 (12) ◽  
pp. 2130-2138
Author(s):  
Juan Yebenes Serrano ◽  
Marta Zorrilla
2020 ◽  
pp. 35-67
Author(s):  
Kristin Weber ◽  
Christiana Klingenberg

Author(s):  
Lili Aunimo ◽  
Ari V. Alamäki ◽  
Harri Ketamo

Constructing a big data governance framework is important when a company performs data-driven software development. The most important aspects of big data governance are data privacy, security, availability, usability, and integrity. In this chapter, the authors present a business case where a framework for big data governance has been built. The business case is about the development and continuous improvement of a new mobile application that is targeted for consumers. In this context, big data is used in product development, in building predictive modes related to the users and for personalization of the product. The main finding of the study is a novel big data governance framework and that a proper framework for big data governance is useful when building and maintaining trustworthy and value adding big data-driven predictive models in an authentic business environment.


Author(s):  
Lili Aunimo ◽  
Ari V. Alamäki ◽  
Harri Ketamo

Constructing a big data governance framework is important when a company performs data-driven software development. The most important aspects of big data governance are data privacy, security, availability, usability, and integrity. In this chapter, the authors present a business case where a framework for big data governance has been built. The business case is about the development and continuous improvement of a new mobile application that is targeted for consumers. In this context, big data is used in product development, in building predictive modes related to the users and for personalization of the product. The main finding of the study is a novel big data governance framework and that a proper framework for big data governance is useful when building and maintaining trustworthy and value adding big data-driven predictive models in an authentic business environment.


Author(s):  
Peter Verhezen

We are increasingly living in a digital world, where companies attempt to adapt to a new context of Industry 4.0. The authors believe that artificial intelligence and the use of logarithms will alter the game of competition. Digitization is moving our economy away from “financial capitalism” to “data capitalism,” and companies and their boards need to adopt the way they operate and steer the organization to new ecosystems where personalized service becomes part of the new digital strategy. Basically, it is not a battle of AI versus humans, but rather finding a way to enhance the collaboration of AI and humans in organizations. Despite the enormous potential benefits of AI, boards should not ignore the darker side of AI, namely the potential biasedness and sometimes unfairness of algorithms and privacy concerns and the ubiquitous cyberthreats. This is why proper data governance at the board level is needed. The authors suggest that this becomes a critical success factor to be addressed at boards, either as part of the risk management or strategic committee or as a separated digitization committee.


Sign in / Sign up

Export Citation Format

Share Document