Closing the Gap between Data Mining and Business Users of Business Intelligence Systems

2012 ◽  
Vol 3 (4) ◽  
pp. 14-53 ◽  
Author(s):  
Ana Azevedo ◽  
Manuel Filipe Santos
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Easy Access ◽  
Complete Knowledge ◽  
Business Intelligence Systems ◽  
Closing The Gap

Since Lunh first used the term Business Intelligence (BI) in 1958, major transformations happened in the field of information systems and technologies, especially in the area of decision support systems. BI systems are widely used in organizations and their importance is recognized. These systems present themselves as essential parts of a complete knowledge of business and an irreplaceable tool in the support to decision making. The dissemination of data mining (DM) tools is increasing in the BI field, as well as the acknowledgment of the relevance of its usage in enterprise BI systems. BI tools are friendly, iterative, and interactive, allowing business users an easy access. The user can manipulate directly data, having the ability to extract all the value contained into that business data. Problems noted in the use of DM in the field of BI is related to the fact that DM models are complex in order to be directly manipulated by business users, not including BI tools. The nonexistence of BI tools allowing business users the direct manipulation of DM models was identified as the problem. More of these issues, possible solutions and conclusions are presented in this article.

scholarly journals The Effect of Perceived Factors of Decision Support Systems on Actual Usage: Behavioral Intention of Using Decision Support Systems as a Mediating Variable: “An Empirical Study of Jordanian Higher Education Institutions”

2016 ◽  
Vol 12 (1) ◽  
pp. 201
Author(s):  
Bilal Mohammed Salem Al-Momani
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Behavioral Intention ◽  
Support Systems ◽  
Higher Education Institutions ◽  
Quality Of Information

Decision support systems (DSS) are interactive computer-based systems that provide information, modeling, and manipulation of data. DSS are clearly knowledge-based information systems to capture, Processing and analysis of information affecting or aims to influence the decision making process, performed by people in scope professional job appointed by a user. Hence, this study describes briefly the key concepts of decision support systems such as perceived factors with a focus on quality  of information systems and quality of information variables, behavioral intention of using DSS, and actual DSS use by adopting and extending the technology acceptance model (TAM) of Davis (1989); and Davis, Bagozzi and Warshaw (1989).There are two main goals, which stimulate the study. The first goal is to combine Perceived DSS factors and behavioral intention to use DSS from both the social perspective and a technology perspective with regard to actual DSS usage, and an experimental test of relations provide strategic locations to organizations and providing indicators that should help them manage their DSS effectiveness. Managers face the dilemma in choosing and focusing on most important factors which contributing to the positive behavioral intention of use DSS by the decision makers, which, in turn, could contribute positively in the actual DSS usage by them and other users to effectively solve organizational problems. Hence, this study presents a model which should provide the useful tool for top management in the higher education institutions- in particular-to understand the factors that determine using behaviors for designing proactive interventions and to motivate the acceptance of TAM in order to use the DSS in a way that contributes to the higher education decision-making plan and IT policy.To accomplish or attain the above mentioned objectives, the researcher developed a research instrument (questionnaire) and distributed it amongst the higher education institutions in Jordan to collect data in order to empirically study hypothesis testing (related to the objectives of study). 341 questionnaires were returned from the study respondents. Data were analyzed by utilizing both SPSS (conducted descriptive analysis) and AMOS (conducting structural equation modelling).Findings of the study indicate that some hypotheses were supported while the others were not. Contributions of the study were presented. In addition, the researcher presented some recommendations. Finally, this study has identified opportunities for further study which has progressed greatly advanced understanding constantly of DSS usage, that can help formulate powerful strategies Involving differentiation between DSS perceived factors.

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.

Risks Evaluation and IT Audit Aspects of Business Intelligence Solutions

2010 ◽  
pp. 17-40
Author(s):  
Andrea Ko
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Information Systems ◽  
Business Intelligence ◽  
Decision Making Process ◽  
Future Trends ◽  
Security Risks ◽  
Business Intelligence Systems ◽  
Audit Approach ◽  
It Audit

Many organizations are struggling with a vast amount of data in order to gain valuable insights and get support in their decision-making process. Decision-making quality depends increasingly on information and the systems that deliver this information. These services are vulnerable and risky from security aspects, and they have to satisfy several requirements, like transparency, availability, accessibility, convenience, and compliance. IT environments are more and more complex and fragmented, which means additional security risks. Business intelligence solutions provide assistance in these complex business situations. Their main goal is to assist organizations to make better decisions. Better decisions means that these solutions support the management of risks, and they have a key role in raising revenue and in reducing cost. The objectives of this chapter are to give an overview of the business intelligence field and its future trends, to demonstrate the most important business intelligence solutions, meanwhile highlighting their risks, business continuity challenges, and IT audit issues. In spite of the fact that this chapter focuses on the business intelligence solutions and their specialities, risk management and the related IT audit approach can be applied for other categories of information systems. IT audit guidelines, best practices, and standards are presented as well, because they give effective tools in controlling process of business intelligence systems.

Tailorable Technologies for Improving Business Intelligence Systems

2021 ◽  
pp. 560-575
Author(s):  
Shah J. Miah
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Conceptual Framework ◽  
Business Intelligence ◽  
Cost Effective ◽  
Business Owners ◽  
Business Intelligence Systems ◽  
Effective Operation

The Australian farm-based businesses can be benefited from specially designed applications for cost-effective operation while maximizing profits to survive in economic and environmental crises. For decision support, existing business intelligence systems (BIS) approaches scarcely deal with specific user's provisions to adjust changing situations in decision making, without extra technical exertions. In this chapter, the authors describe a conceptual framework of tailorable BIS solution that is based on case study findings in that the highlighted requirements are relevant to address changing situations through enhancing end user's engagement. The activities of end user's engagement supported through the use of tailorable features that reinforce a shift from the traditional BIS process to a new provision where business owners can actively involve in adjusting their features to their decision support.

Group Support Systems as Tools for HR Decision Making

2011 ◽  
pp. 1087-1095
Author(s):  
James Yao ◽  
John Wang
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Human Resource ◽  
Financial Management ◽  
Support Systems ◽  
Group Support Systems ◽  
Group Support ◽  
Soft Data

In the late 1960s, a new type of information system came about: model-oriented DSS or management decision systems. By the late 1970s, a number of researchers and companies had developed interactive information systems that used data and models to help managers analyze semistructured problems. These diverse systems were all called decision support systems (DSS). From those early days, it was recognized that DSS could be designed to support decision-makers at any level in an organization. DSS could support operations, financial management, and strategic decision making. Group decision support systems (GDSS) which aim at increasing some of the benefits of collaboration and reducing the inherent losses are interactive information technology-based environments that support concerted and coordinated group efforts toward completion of joint tasks (Dennis, George, Jessup, Nunamaker, & Vogel, 1998). The term group support systems (GSS) was coined at the start of the 1990s to replace the term GDSS. The reason for this is that the role of collaborative computing was expanded to more than just supporting decision making (Patrick & Garrick, 2006). For the avoidance of any ambiguities, the latter term shall be used in the discussion throughout this article. Human resources (HR) are rarely expected like other business functional areas to use synthesized data because HR groups have been primarily connected with transactional processing of getting data into the system and on record for reporting and historical purposes (Dudley, 2007). For them soft data do not win at the table; hard data do. Recently, many quantitative or qualitative techniques have been developed to support human resource management (HRM) activities, classified as management sciences/operations research, multiattribute utility theory, multicriteria decision making, ad hoc approaches, and human resource information systems (HRIS) (Byun, 2003). More importantly, HRIS can include the three systems of expert systems (ES), decision support systems (DSS), and executive information systems (EIS) in addition to transaction processing systems (TPS) and management information systems (MIS) which are conventionally accepted as an HRIS. As decision support systems, GSS are able to facilitate HR groups to gauge users’ opinions, readiness, satisfaction, and so forth, increase their HRM activity quality, and generate better group collaborations and decision makings with current or planned HRIS services. Consequently, GSS can help HR professionals exploit and make intelligent use of soft data and act tough in their decision-making process.

Tailorable Technologies for Improving Business Intelligence Systems

2019 ◽  
pp. 238-253
Author(s):  
Shah J. Miah
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Conceptual Framework ◽  
Business Intelligence ◽  
Cost Effective ◽  
Business Owners ◽  
Business Intelligence Systems ◽  
Effective Operation

The Australian farm-based businesses can be benefited from specially designed applications for cost-effective operation while maximizing profits to survive in economic and environmental crises. For decision support, existing business intelligence systems (BIS) approaches scarcely deal with specific user's provisions to adjust changing situations in decision making, without extra technical exertions. In this chapter, the authors describe a conceptual framework of tailorable BIS solution that is based on case study findings in that the highlighted requirements are relevant to address changing situations through enhancing end user's engagement. The activities of end user's engagement supported through the use of tailorable features that reinforce a shift from the traditional BIS process to a new provision where business owners can actively involve in adjusting their features to their decision support.

Risks Evaluation and IT Audit Aspects of Business Intelligence Solutions

2010 ◽  
pp. 1686-1709
Author(s):  
Andrea Ko
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Information Systems ◽  
Business Intelligence ◽  
Decision Making Process ◽  
Future Trends ◽  
Security Risks ◽  
Business Intelligence Systems ◽  
Audit Approach ◽  
It Audit

Many organizations are struggling with a vast amount of data in order to gain valuable insights and get support in their decision-making process. Decision-making quality depends increasingly on information and the systems that deliver this information. These services are vulnerable and risky from security aspects, and they have to satisfy several requirements, like transparency, availability, accessibility, convenience, and compliance. IT environments are more and more complex and fragmented, which means additional security risks. Business intelligence solutions provide assistance in these complex business situations. Their main goal is to assist organizations to make better decisions. Better decisions means that these solutions support the management of risks, and they have a key role in raising revenue and in reducing cost. The objectives of this chapter are to give an overview of the business intelligence field and its future trends, to demonstrate the most important business intelligence solutions, meanwhile highlighting their risks, business continuity challenges, and IT audit issues. In spite of the fact that this chapter focuses on the business intelligence solutions and their specialities, risk management and the related IT audit approach can be applied for other categories of information systems. IT audit guidelines, best practices, and standards are presented as well, because they give effective tools in controlling process of business intelligence systems.

A Perspective on Data Mining Integration with Business Intelligence

Data Mining ◽  
2013 ◽  
pp. 1873-1892
Author(s):  
Ana Azevedo ◽  
Manuel Filipe Santos
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Support Systems ◽  
Underlying Structure ◽  
The Past ◽  
Definition Of

Business Intelligence (BI) is an emergent area of the Decision Support Systems (DSS) discipline. Over the past years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. The purpose of this chapter is to discuss the relevance of DM integration with BI, and its importance to business users. From the literature review, it was observed that the definition of an underlying structure for BI is missing, and therefore a framework is presented. It was also observed that some efforts are being done that seek the establishment of standards in the DM field, both by academics and by people in the industry. Supported by those findings, this chapter introduces an architecture that can conduct to an effective usage of DM in BI. This architecture includes a DM language that is iterative and interactive in nature. This chapter suggests that the effective usage of DM in BI can be achieved by making DM models accessible to business users, through the use of the presented DM language.

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