scholarly journals SELF-SERVICE BUSINESS ANALYTICS AND DATA GOVERNANCE

2020 ◽  
Author(s):  
Mihaela MUNTEAN ◽  
Constantin Viorel NEGRUŢ ◽  
Florin MILITARU
Keyword(s):  
Business Analytics ◽  
Data Governance ◽  
Service Business ◽  
Self Service

scholarly journals The Triadic Relationship of Sense-Making, Analytics, and Institutional Influences

Informatics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 3
Author(s):  
Imad Bani-Hani ◽  
Soumitra Chowdhury ◽  
Arianit Kurti
Keyword(s):  
Data Analytics ◽  
Business Environment ◽  
Sense Making ◽  
Business Analytics ◽  
Service Business ◽  
Institutional Influences ◽  
Self Service ◽  
Relationship Of ◽  
The Relationship ◽  
Triadic Relationship

The current business environment demands the enablement of organization-wide use of analytics to support a fact-based decision making. Such movement within the organization require employees to take advantage of the self-service business analytics tools to independently fulfil their needs. However, assuming independence in data analytics requires employees to make sense of several elements which collectively contribute to the generation of required insights. Building on sense-making, self-service business analytics, and institutions literature, this paper explores the relationship between sense-making and self-service business analytics and how institutions influence and shape such relationship. By adopting a qualitative perspective and using 22 interviews, we have empirically investigated a model developed through our literature review and provided more understanding of the sense-making concept in a self-service business analytics context.

scholarly journals Fusion Cubes

2013 ◽  
Vol 9 (2) ◽  
pp. 66-88 ◽  
Author(s):  
Alberto Abelló ◽  
Jérôme Darmont ◽  
Lorena Etcheverry ◽  
Matteo Golfarelli ◽  
Jose-Norberto Mazón ◽  
...  
Keyword(s):  
Business Intelligence ◽  
Decision Process ◽  
Decision Makers ◽  
Related Research ◽  
Service Business ◽  
Short Lifespan ◽  
Research Challenges ◽  
Self Service ◽  
Core Idea ◽  
And Storage

Self-service business intelligence is about enabling non-expert users to make well-informed decisions by enriching the decision process with situational data, i.e., data that have a narrow focus on a specific business problem and, typically, a short lifespan for a small group of users. Often, these data are not owned and controlled by the decision maker; their search, extraction, integration, and storage for reuse or sharing should be accomplished by decision makers without any intervention by designers or programmers. The goal of this paper is to present the framework we envision to support self-service business intelligence and the related research challenges; the underlying core idea is the notion of fusion cubes, i.e., multidimensional cubes that can be dynamically extended both in their schema and their instances, and in which situational data and metadata are associated with quality and provenance annotations.

Self-Service Business Intelligence Adoption in Business Enterprises

2017 ◽  
Vol 13 (3) ◽  
pp. 65-85 ◽  
Author(s):  
Mohammad Daradkeh ◽  
Radwan Moh'd Al-Dwairi
Keyword(s):  
Technology Acceptance ◽  
Business Intelligence ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Equation Modeling ◽  
System Quality ◽  
Service Business ◽  
Business Enterprises ◽  
Self Service ◽  
Analysis Quality

Despite the growing popularity of self-service business intelligence (SSBI) tools, empirical research that investigates their acceptance by business professionals is still scarce. This paper presents and tests an integrated model of the antecedents of users' acceptance of SSBI tools in business enterprises. The proposed model is developed based on the technology acceptance model (TAM) and incorporating information and system quality from DeLone and McLean IS success model. It also includes an important factor from the business intelligence literature called analysis quality. To test the model, data were collected through a questionnaire survey from 331 business users working in a variety of industries in Jordan. Data were analysed using structural equation modeling (SEM) techniques. The results demonstrated that the three quality factors– information quality, system quality and analysis quality – are key antecedents of perceived usefulness and ease of use, which in turn were found to be strong predictors of users' intention to use SSBI tools. The findings of this study provide several implications for research and practice, and thus should help in the design and deployment of more user-accepted SSBI tools.

scholarly journals User-Related Challenges of Self-Service Business Intelligence

2020 ◽  
pp. 1-15
Author(s):  
Christian Lennerholt ◽  
Joeri Van Laere ◽  
Eva Söderström
Keyword(s):  
Business Intelligence ◽  
Service Business ◽  
Self Service

scholarly journals Already grown-up or still in puberty? A bibliometric review of 16 years of data governance research

2019 ◽  
Vol 19 (1) ◽  
pp. 105-120
Author(s):  
Marvin Jagals ◽  
Erik Karger ◽  
Frederik Ahlemann
Keyword(s):  
Research Field ◽  
Public Institutions ◽  
Intellectual Structure ◽  
Future Research ◽  
Business Analytics ◽  
Data Governance ◽  
Corporate Networks ◽  
Real Time Analysis ◽  
Bibliometric Review ◽  
Opening Up

The amount of data and the speed at which it increases grows rapidly. Companies and public institutions try to manage this increasing flood of data effectively and in a manner that adds value. Besides, the companies and public institutions also join corporate networks or platforms to increase their value by sharing their data. The evolution of traditional business intelligence into business analytics, including real-time analysis, increases the high demand for qualitative data. Data governance tries to create a framework to manage these issues. This interdisciplinary research field has now been in existence for nearly two decades. With this contribution, we attempt to provide the research field with a blueprint. This paper aims to explore the past to understand the present and shape the future of data governance. We give an overview of how the research field changed from 2005 to 2020, commenting on its development and pointing out future research paths based on our findings. We, therefore, conducted a bibliometric analysis to describe the research field’s bibliometric and intellectual structure. The findings show that for years the research field concentrated on a few topics, which currently undergoes change and has led to an opening up of the research field. Finally, the results are discussed and future research strands are highlighted

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