scholarly journals Self-Service Business Intelligence success factors that create value for business

2021 ◽  
Author(s):  
Jonida Sinaj ◽  
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Success Factors ◽  
Data Driven ◽  
Competitive Advantages ◽  
Data Driven Decision Making ◽  
Service Business ◽  
The Future ◽  
Self Service

Business Intelligence and Analytics have change the business needs, but the market requires a more data- driven decision-making environment. Self-service Business Intelligence initiatives are providing more competitive advantages currently. The role of the users and freedom of access is one of the essential advantages that SSBI holds. Despite this fact, there is still needed analysis on how business can gain more value from SSBI, based on the technological, operational and organizational aspects. The work in this paper serves to analysis on the SSBI requirements that bring value to business. The paper is organized starting from building knowledge by upon the existing literature and exploring the domain. Data will be collected by interviewing experts of the fields. The main findings will provide future suggestion related to the topic and the results will serve both the companies that have implemented it and the ones that want to see it as a perspective in the future.

scholarly journals Improving Decision Quality for Business Users Based on Cloud-based Self-Service Business Intelligence Tools

2020 ◽  
pp. 1-11
Author(s):  
Christian Ploder ◽  
Reinhard Bernsteiner ◽  
Thomas Dilger
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Data Science ◽  
Data Driven ◽  
Decision Quality ◽  
Data Driven Decision Making ◽  
Significant Difference ◽  
Self Service ◽  
Business Informatics ◽  
User Friendly

The ever-growing volume of data promotes data-driven decision-making in more cases and more areas than before. The development of user-friendly self-service BI (SSBI) tools enable business users to autonomously execute tasks in the area of Business Intelligence (BI), statistical analysis, or data science. Cloud computing offers the opportunity to provide SSBI as services as well. This paper focusses on cloud-based SSBI tools and their support for data-driven decision-making by business users. This paper aims to identify the influence of a deeper understanding of business informatics on (a) the handling of the cloud-based SSBI tools and (b) the data-driven decision making performance. An experimental setting was used to collect empirical data. Two groups with equal knowledge in business administration, but different backgrounds in business informatics have been defined. Based on different backgrounds in business informatics, the results show no significant difference in handling the cloud-based SSBI tool but reveal significant differences in decision-making performance.

Investigating the Role of Data-Driven Decision-Making Within School Improvement Processes

2021 ◽  
pp. 1707-1725
Author(s):  
Venesser Fernandes
Keyword(s):  
Decision Making ◽  
Organizational Change ◽  
School Improvement ◽  
Critical Role ◽  
Data Driven ◽  
Evidence Based ◽  
Data Driven Decision Making ◽  
Continuous School Improvement ◽  
Decision Making Processes

This chapter provides a detailed literature review exploring the importance of data-driven decision-making processes in current Australian school improvement processes within a context of evidence-based organizational change and development. An investigation into the concept of decision-making and its effect on organizational culture is conducted as change and development are considered to be the new constants in the current discourse around continuous school improvement in schools. In a close examination of literature, this chapter investigates how key factors such as collaboration, communication, and organizational trust are achieved through data-driven decision-making within continuous school improvement processes. The critical role of leadership in sustaining data cultures is also examined for its direct impact on continuous school improvement processes based on evidence-based organizational change and development practices. Future implications of data-driven decision-making to sustain continuous school improvement and accountability processes in Australian schools are discussed.

scholarly journals Sharing by proxy: Invisible users in the sharing economy

First Monday ◽  
2018 ◽  
Author(s):  
Gemma Newlands ◽  
Christoph Lutz ◽  
Christian Pieter Hoffmann
Keyword(s):  
Decision Making ◽  
Sharing Economy ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Future Of Work ◽  
Trust And Reputation ◽  
The Future ◽  
User Data ◽  
Reputation Mechanisms ◽  
Definition Of

With the future of work increasingly data-driven, platforms automate decisions based on the collection of vast quantities of user data. However, non-users constitute a challenge as they provide little to no data for either platforms or other users. We focus on a category of (non-)users that has not received any attention in research: users-by-proxy. Users-by-proxy make use of sharing services but they are not themselves part of the sharing transaction. Platforms cannot analyze their behavior to tailor services or allocate labor most effectively. Users-by-proxy also have significant implications for trust and reputation mechanisms. In this conceptual contribution, we provide a definition of users-by-proxy as a third category between users and non-users, developing a typology of users-by-proxy based on motives of non-/use. We focus on the ramifications of users-by-proxy for the future of work and their significance for the limits of data-driven decision-making.

Student Involvement and Data-Driven Decision Making

2010 ◽  
Vol 43 (4) ◽  
pp. 1246-1271 ◽  
Author(s):  
Brianna L. Kennedy ◽  
Amanda Datnow
Keyword(s):  
Decision Making ◽  
Education Reform ◽  
Educational Reform ◽  
Student Voice ◽  
Student Involvement ◽  
Case Analysis ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Cross Case Analysis

Existing literature supports the inclusion of students in education reform, documenting benefits for both students and educators. When student voice is not included in reform efforts, these efforts are more likely to flounder. The emerging educational reform of data-driven decision making (DDDM) offers promise for increasing student achievement. However, scant research documents the involvement of students in DDDM reforms. Using a theoretical framework that advocates for democratically involving students in education reform, this cross-case analysis examines the role of students in DDDM reforms in elementary and high schools known to be exemplars of data-driven decision making. Based on findings of efforts made by exemplar districts as well as actions they did not take to involve students, the authors conclude that a new typology is necessary for assessing student involvement in DDDM. Consequently, the authors propose a new three-tiered typology for conceptualizing this phenomenon.

Investigating the Role of Data-Driven Decision-Making Within School Improvement Processes

2019 ◽  
pp. 201-219 ◽  
Author(s):  
Venesser Fernandes
Keyword(s):  
Decision Making ◽  
Organizational Change ◽  
School Improvement ◽  
Critical Role ◽  
Data Driven ◽  
Evidence Based ◽  
Data Driven Decision Making ◽  
Continuous School Improvement ◽  
Decision Making Processes

This chapter provides a detailed literature review exploring the importance of data-driven decision-making processes in current Australian school improvement processes within a context of evidence-based organizational change and development. An investigation into the concept of decision-making and its effect on organizational culture is conducted as change and development are considered to be the new constants in the current discourse around continuous school improvement in schools. In a close examination of literature, this chapter investigates how key factors such as collaboration, communication, and organizational trust are achieved through data-driven decision-making within continuous school improvement processes. The critical role of leadership in sustaining data cultures is also examined for its direct impact on continuous school improvement processes based on evidence-based organizational change and development practices. Future implications of data-driven decision-making to sustain continuous school improvement and accountability processes in Australian schools are discussed.

Modelling in Support of Decision Making in Business Intelligence

2021 ◽  
pp. 115-144
Author(s):  
Roumiana Ilieva ◽  
Malinka Ivanova ◽  
Tzvetilina Peycheva ◽  
Yoto Nikolov
Keyword(s):  
Decision Making ◽  
Predictive Modeling ◽  
Business Intelligence ◽  
Business Processes ◽  
Future Research ◽  
Business World ◽  
Future Research Directions ◽  
Self Service ◽  
Key Steps

Modelling in support of decision making in business intelligence (BI) starts with exploring the BI systems, driven by artificial intelligence (AI). The purpose why AI will be the core of next-gen analytics and why BI will be empowered by it are determined. The role of AI and machine learning (ML) in business processes automation is analyzed. The benefits from AI integration in BI platforms are summarized. Then analysis goes through predictive modeling in the domain of e-commerce. The use of ML for predictive modeling is overviewed. Construction of predictive and clustering models is proposed. After that the importance of self-services in BI platforms is outlined. In this context the self-service BI is defined and what are the key steps to create successful self-service BI model are sketched. The effects of potential threads which are the results of the big data in the business world are examined and some suggestions for the future have been made. Lastly, game-changer trends in BI and future research directions are traced.

scholarly journals Data-Driven Decision Making: Qualitative Study at Digital Startup using Semantic Domain Analysis

I-STATEMENT ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 09-14
Author(s):  
Asri Pertiwi ◽  
Nahlia Roseno
Keyword(s):  
Decision Making ◽  
Qualitative Study ◽  
Business Intelligence ◽  
Domain Analysis ◽  
Data Driven ◽  
Future Research ◽  
Data Driven Decision Making ◽  
Semantic Domain ◽  
Domain Semantics

Business Intelligence (BI) is commonly applied to large companies, but there are a few evidence of BI practice in startups. Although startup founder understand that data and information are very important, but how this used for decision making needs to be further explored. Through interviews with four startup’s founders, the transcript result were analyzed using domain semantics and taxonomy analysis. Several findings are outlined which are followed by suggestions for future research.

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