Business Intelligence Impacts on Design of Enterprise Systems

2018 ◽  
pp. 2932-2942 ◽  
Author(s):  
Saeed Rouhani ◽  
Dusanka Milorad Lecic
Keyword(s):  
Decision Support ◽  
Business Intelligence ◽  
Enterprise Systems ◽  
Future Research ◽  
Functional Requirements ◽  
Intelligent Software ◽  
Individual System ◽  
Quality Aspects ◽  
Important Quality

The approach to decision support as an individual system has been replaced by a new viewpoint of intelligent software and systems. Based on this new approach, enterprise systems are designed to have Business Intelligence (BI) as an umbrella concept which covers various enabler tools and capabilities in the form of non-functional requirements. The current state-of-the-art in decision support takes the intelligence requirements of enterprise systems as important quality aspects into consideration, along with their functional and non-functional needs, but the literature lacks studies on the evaluation of these intelligence requirements. In this book chapter, business intelligence and enterprise systems literature are reviewed. Also based on the latest researches, the position of BI on these systems is discussed. In the following, through the study of BI capabilities and proposing them as non-functional, the BI Impacts on the design of enterprise systems and software would be described along the direction for future research and insights for information systems development.

Business Intelligence Impacts on Design of Enterprise Systems

2019 ◽  
pp. 778-789
Author(s):  
Saeed Rouhani ◽  
Dusanka Milorad Lecic
Keyword(s):  
Decision Support ◽  
Business Intelligence ◽  
Enterprise Systems ◽  
Future Research ◽  
Functional Requirements ◽  
Intelligent Software ◽  
Current State ◽  
Individual System ◽  
Quality Aspects ◽  
Important Quality

The approach to decision support as an individual system has been replaced by a new viewpoint of intelligent software and systems. Based on this new approach, enterprise systems are designed to have business intelligence (BI) as an umbrella concept that covers various enabler tools and capabilities in the form of non-functional requirements. The current state of the art in decision support takes the intelligence requirements of enterprise systems as important quality aspects into consideration, along with their functional and non-functional needs, but the literature lacks studies on the evaluation of these intelligence requirements. In this chapter, business intelligence and enterprise systems literature are reviewed. Also based on the latest researches, the position of BI on these systems is discussed. In the following, through the study of BI capabilities and proposing them as non-functional, the BI impacts on the design of enterprise systems and software are described along the directions for future research and insights for information systems development.

An Integrated Approach for a Knowledge-based Clinical Workstation: Architecture and Experience

1998 ◽  
Vol 37 (01) ◽  
pp. 16-25 ◽  
Author(s):  
P. Ringleb ◽  
T. Steiner ◽  
P. Knaup ◽  
W. Hacke ◽  
R. Haux ◽  
...  
Keyword(s):  
Decision Support ◽  
Information Systems ◽  
Patient Care ◽  
Hospital Information Systems ◽  
Medical Decision ◽  
Daily Routine ◽  
Functional Requirements ◽  
Support Functions ◽  
Knowledge Based ◽  
Clinical Workstation

Abstract:Today, the demand for medical decision support to improve the quality of patient care and to reduce costs in health services is generally recognized. Nevertheless, decision support is not yet established in daily routine within hospital information systems which often show a heterogeneous architecture but offer possibilities of interoperability. Currently, the integration of decision support functions into clinical workstations is the most promising way. Therefore, we first discuss aspects of integrating decision support into clinical workstations including clinical needs, integration of database and knowledge base, knowledge sharing and reuse and the role of standardized terminology. In addition, we draw up functional requirements to support the physician dealing with patient care, medical research and administrative tasks. As a consequence, we propose a general architecture of an integrated knowledge-based clinical workstation. Based on an example application we discuss our experiences concerning clinical applicability and relevance. We show that, although our approach promotes the integration of decision support into hospital information systems, the success of decision support depends above all on an adequate transformation of clinical needs.

Decision support tools for oil spill response (OSR-DSTs): Approaches, challenges, and future research perspectives

2021 ◽  
Vol 167 ◽  
pp. 112313
Author(s):  
Zhaoyang Yang ◽  
Zhi Chen ◽  
Kenneth Lee ◽  
Edward Owens ◽  
Michel C. Boufadel ◽  
...  
Keyword(s):  
Decision Support ◽  
Oil Spill ◽  
Future Research ◽  
Decision Support Tools ◽  
Research Perspectives ◽  
Support Tools ◽  
Oil Spill Response

Influences of Stress on Interactions with Decision Support

2020 ◽  
Vol 64 (1) ◽  
pp. 1886-1890
Author(s):  
Kylie Litaker ◽  
Christopher B. Mayhorn
Keyword(s):  
Decision Support ◽  
Information Source ◽  
Negative Influence ◽  
Future Research ◽  
Physiological Measures ◽  
Task Motivation ◽  
Situational Stress ◽  
Before And After ◽  
Scanning Task ◽  
Source Of Information

People regularly interact with automation to make decisions. Research shows that reliance on recommendations can depend on user trust in the decision support system (DSS), the source of information (i.e. human or automation), and situational stress. This study explored how information source and stress affect trust and reliance on a DSS used in a baggage scanning task. A preliminary sample of sixty-one participants were given descriptions for a DSS and reported trust before and after interaction. The DSS gave explicit recommendations when activated and participants could choose to rely or reject the choice. Results revealed a bias towards self-reliance and a negative influence of stress on trust, particularly for participants receiving help from automation. Controlling for perceived reliability may have eliminated trust biases prior to interaction, while stress may have influenced trust during the task. Future research should address potential differences in task motivation and include physiological measures of stress.

scholarly journals Bridging Science and Practice-Importance of Stakeholders in the Development of Decision Support: Lessons Learned

2021 ◽  
Vol 13 (10) ◽  
pp. 5744
Author(s):  
Innocent K. Tumwebaze ◽  
Joan B. Rose ◽  
Nynke Hofstra ◽  
Matthew E. Verbyla ◽  
Daniel A. Okaali ◽  
...  
Keyword(s):  
Decision Support ◽  
Stakeholder Participation ◽  
Scientific Data ◽  
Lessons Learned ◽  
Future Research ◽  
Decision Support Tools ◽  
Local Data ◽  
Funding Sources ◽  
Sanitation And Hygiene ◽  
Support Tools

User-friendly, evidence-based scientific tools to support sanitation decisions are still limited in the water, sanitation and hygiene (WASH) sector. This commentary provides lessons learned from the development of two sanitation decision support tools developed in collaboration with stakeholders in Uganda. We engaged with stakeholders in a variety of ways to effectively obtain their input in the development of the decision support tools. Key lessons learned included: tailoring tools to stakeholder decision-making needs; simplifying the tools as much as possible for ease of application and use; creating an enabling environment that allows active stakeholder participation; having a dedicated and responsive team to plan and execute stakeholder engagement activities; involving stakeholders early in the process; having funding sources that are flexible and long-term; and including resources for the acquisition of local data. This reflection provides benchmarks for future research and the development of tools that utilize scientific data and emphasizes the importance of engaging with stakeholders in the development process.

scholarly journals Üzleti intelligencia megoldások alkalmazásának sikertényezői - A hazai szolgáltató szektor nagyvállalatainak körében végzett mélyinterjús kutatás

2019 ◽  
Vol 19 (2) ◽  
pp. 23
Author(s):  
Gergely Görcsi ◽  
Gergő Barta ◽  
Zsuzsanna Széles
Keyword(s):  
Decision Support ◽  
Continuous Improvement ◽  
Business Intelligence ◽  
Support Functions ◽  
Term Operation ◽  
Success Criteria ◽  
The World ◽  
Depth Interviews ◽  
Insight Into

A vállalatok működése szempontjából a döntéstámogató funkció folyamatos fejlesztése, monitorozása kiemelt jelentőségű, hiszen az vezetést támogató eszközként segíti a menedzsmentfeladatok ellátását. Az üzleti intelligencia (business intelligence, BI) olyan infokommunikációs megoldás, mely a vállalati rendszerekből különböző adatforrásokat felhasználva képes az adatok összekapcsolására és elemzésére. A napi üzletmenet gördülékeny biztosítása céljából alkalmazott tranzakciós rendszerektől eltérően a BI-eszközök beszámolás orientáltak, a fókusz a döntéstámogatásra helyeződik. A kutatás a fogalmak tisztázását követően képet ad a legfrissebb üzleti intelligencia trendekről. A tanulmány szakmai mélyinterjúk elemzésén keresztül betekintést nyújt az üzleti intelligencia megoldások világába. A kutatás eredményeként az olvasó képet kaphat a BI bevezetésétől várt eredményekről, az implementáció és a hosszú távú működtetés sikerkritériumait illetően. --- Gergely GORCSI - Gergo BARTA - Zsuzsanna SZELES Success criteria for the application of business intelligence solutions In the running of any given company, continuous improvement and monitoring of decision support functions is crucial for such activities to serve as tools to support management tasks. Business Intelligence (BI) is an infocommunication tool that connects and analyses data from corporate systems using varied data sources. Unlike transactional systems that are used to ensure the sound operation of day-to-day business, BI tools are report-oriented, and focus on decision support. Reviewing related concepts, this research gives an overview of the latest business intelligence trends. Our study sets out to provide an insight into the world of business intelligence solutions by analysing professional, in-depth interviews. Through our research, one will become familiar with the results expected from the introduction of BI, in relation to the success criteria of its implementation and long-term operation.

Firm Performance Effects in Relation to the Implementation and Use of Enterprise Resource Planning Systems

2004 ◽  
Vol 18 (2) ◽  
pp. 79-105 ◽  
Author(s):  
Andreas I. Nicolaou
Keyword(s):  
Financial Performance ◽  
Enterprise Resource Planning ◽  
Enterprise Systems ◽  
Resource Planning ◽  
Planning System ◽  
Future Research ◽  
Control Group ◽  
Planning Systems ◽  
Research Issues ◽  
Performance Effects

Research indicates that successful adoption of information technology to support business strategy can help organizations gain superior financial performance. The recent wave of enterprise-wide resource planning systems adoptions is a significant commitment of resources and may affect almost all business processes. This study examines the effect of adoption of enterprise systems on a firm's long-term financial performance. A large-scale data identification and collection method compared the financial data of 247 firms adopting enterprise wide systems with a matched control group of firms cross-sectionally and longitudinally before and after adoption. A number of implementation characteristics were also measured and their effects assessed. The results show that firms adopting enterprise systems exhibit higher differential performance only after two years of continued use. Furthermore, controlling for implementation characteristics as vendor choice, implementation goal, modules implemented, and implementation time period, helped explain the financial performance effects of enterprise resource planning system use. These results provide important insights that complement extant research findings and also raise future research issues.

Business Intelligence and Data Mining

2013 ◽  
pp. 57-70
Author(s):  
Zsolt T. Kardkovács
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Business Processes ◽  
Support Systems ◽  
Decision Makers ◽  
Key Factors ◽  
Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

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