Will Big Data Diminish the Role of Humans in Decision Making?

2016 ◽  
Keyword(s):  
Decision Making ◽  
Big Data

Big Data Myth

2019 ◽  
pp. 223-237
Author(s):  
Agata Mardosz-Grabowska
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Qualitative Data ◽  
New Technologies ◽  
Marketing Management ◽  
Individual Experience ◽  
Decision Making Process

Organizations are expected to act rationally; however, mythical thinking is often present among their members. It refers also to myths related to technology. New inventions and technologies are often mythologized in organizations. People do not understand how new technologies work and usually overestimate their possibilities. Also, myths are useful in dealing with ambivalent feelings, such as fears and hopes. The text focuses on the so-called “big data myth” and its impact on the decision-making process in modern marketing management. Mythical thinking related to big data in organizations has been observed both by scholars and practitioners. The aim of the chapter is to discuss the foundation of the myth, its components, and its impact on the decision-making process. Among others, a presence of a “big data myth” may be manifested by over-reliance on data, neglecting biases in the process of data analysis, and undermining the role of other factors, including intuition and individual experience of marketing professionals or qualitative data.

The Role of Big Data and Business Analytics in Decision Making

2021 ◽  
pp. 226-257
Author(s):  
Pedro Caldeira Neves ◽  
Jorge Rodrigues Bernardino
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Open Source ◽  
Specific Area ◽  
Business Analytics ◽  
Business Decision ◽  
Business Sustainability ◽  
Open Source Systems ◽  
Fundamental Shift

The amount of data in our world has been exploding, and big data represents a fundamental shift in business decision-making. Analyzing such so-called big data is today a keystone of competition and the success of organizations depends on fast and well-founded decisions taken by relevant people in their specific area of responsibility. Business analytics (BA) represents a merger between data strategy and a collection of decision support technologies and mechanisms for enterprises aimed at enabling knowledge workers such as executives, managers, and analysts to make better and faster decisions. The authors review the concept of BA as an open innovation strategy and address the importance of BA in revolutionizing knowledge towards economics and business sustainability. Using big data with open source business analytics systems generates the greatest opportunities to increase competitiveness and differentiation in organizations. In this chapter, the authors describe and analyze business intelligence and analytics (BI&A) and four popular open source systems – BIRT, Jaspersoft, Pentaho, and SpagoBI.

scholarly journals Semantics-Empowered Big Data Processing with Applications

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 39-54 ◽  
Author(s):  
Krishnaprasad Thirunarayan ◽  
Amit Sheth
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Processing ◽  
Social Systems ◽  
Intelligent Processing ◽  
Data Formats ◽  
New Concepts ◽  
Semantic Models ◽  
Trust Models

We discuss the nature of big data and address the role of semantics in analyzing and processing big data that arises in the context of physical-cyber-social systems. To handle volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle variety, we resort to semantic models and annotations of data so that intelligent processing can be done independent of heterogeneity of data formats and media. To handle velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize relevant new concepts, entities and facts. To handle veracity, we explore trust models and approaches to glean trustworthiness. These four v's of big data are harnessed by the semantics-empowered analytics to derive value to support applications transcending physical-cyber-social continuum.

scholarly journals Personalized Law and Fundamental Rights

2020 ◽  
Vol 1 (4) ◽  
pp. 56-73
Author(s):  
Tembot Z. Misostishkhov
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Legal Education ◽  
Fundamental Rights ◽  
Legal Positivism ◽  
Decision Making Process ◽  
Minimal Risk ◽  
Legal Norms ◽  
The Public

In recent years, scholars have focused increased attention on the idea of personalized law. It suggests the creation and enforcement of individualized legal norms based on the algorithmic processing of data in the similar manner companies personalize their services using Big Data tools. The article aims to define the role and position of personalized law and to evaluate the risks and consequences of personalization in the context of the emerging digital economy. The research analyses the theoretical grounds of personalized law and justifies its interpretation from the perspective of Hart’s legal positivism striking a balance between the sociological facticity of law and normativism. The study reveals the content, essential features of personalized law and defines its concept. The author analyses the correlation of personalized law with fundamental rights, thus evaluating the risks and consequences of personalization. Particularly, the errors of the approximation of a person’s actual will could occur as part of algorithmic decision-making thereby resulting in discrimination. It appears reasonable that at the beginning, algorithmic personalization should cover only those domains which have the minimal risk of the violation of fundamental norms and of intrusion into the field of social debates. The study underscores, that the transparency of the public sector and of the data-based algorithmic decision-making process is crucial in the context of personalized law, but nevertheless could debase its idea due to opportunistic practices. The issues identified during the research lead one to suggest that professionals who have both legal education and expertise in computer sciences would be in demand in the future. Such professionals could perform the role of independent experts and neutral authority monitoring compliance with data subject’s rights.

scholarly journals The Role of Big Data in Strategic Decision-Making

2021 ◽  
pp. 57-65
Author(s):  
Amal Bensautra ◽  
Amel Fassouli ◽  
Fella Ghida
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Strategic Decision ◽  
Strategic Decision Making

Boosting innovation performance through big data analytics:An empirical investigationon the role of firm agility

2021 ◽  
pp. 016555152110474
Author(s):  
Ahad ZareRavasan
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Current Knowledge ◽  
Big Data Analytics ◽  
Innovation Performance ◽  
Data Driven ◽  
Moderating Variables ◽  
Research Gap ◽  
Moderating Role

While past studies proposed the role of big data analytics (BDA) as one of the primary pathways to business value creation, current knowledge on the link between BDA and innovation performance remains limited. In this regard, this study intends to fill this research gap by developing a theoretical framework for understanding how and under which mechanisms BDA influences innovation performance. Firm agility (conceptualised as sensing agility, decision-making agility and acting agility) is used in this research as the mediator between BDA and innovation performance. Besides, this research conceptualises two moderating variables: data-driven culture and BDA team sophistication. This study employs partial least squares (PLS) to test and validate the proposed hypotheses using survey data of 185 firms. The results show that firm agility significantly mediates the link between BDA use and innovation performance. Besides, the results suggest that data-driven culture moderates the relation between sensing agility and decision-making agility. This research also supports the moderating role of BDA team sophistication on the link between BDA use and sensing agility.

Role of Big Data in Education

2022 ◽  
pp. 22-37
Author(s):  
Simin Ghavifekr ◽  
Seng Yue Wong
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Information Management ◽  
Higher Education Institutions ◽  
Big Data Analytics ◽  
Time Performance ◽  
Teaching Support

Big data has the variety of characteristics, such as real-time performance, timeliness short, and data mining analysis of large value generated. Application of big data in education can be reviewed in various aspects such as 1) providing students with appropriate teaching, 2) providing teaching support to teachers, and 3) providing information management for the administrations. This chapter can serve as a guide for the management of higher education institutions to recognize possible challenges in big data analytics and better prepare for them in future decision making.

Sign in / Sign up

Export Citation Format

Share Document