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.

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 Data competence maturity: developing data-driven decision making

2018 ◽  
Vol 11 (2) ◽  
pp. 139-158 ◽  
Author(s):  
Thomas G. Cech ◽  
Trent J. Spaulding ◽  
Joseph A. Cazier
Keyword(s):  
Decision Making ◽  
Secondary Education ◽  
Data Science ◽  
Data Driven ◽  
Decision Making Process ◽  
Maturity Model ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Development Data ◽  
Education Performance

Purpose The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education. Design/methodology/approach Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education. Findings The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process. Practical implications Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes. Originality/value This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.

Data science in the business environment: customer analytics case studies in SMEs

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jing Lu ◽  
Lisa Cairns ◽  
Lucy Smith
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Process Model ◽  
Data Science ◽  
Business Environment ◽  
Data Driven ◽  
Complex Data ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Customer Analytics

Purpose A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising. Design/methodology/approach Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions. Findings Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams. Research limitations/implications The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper. Originality/value Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making.

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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