LaurentisCrowsonCTS115Fall2017IntroductionNowadays,information and knowledge represent the fundamental wealth of an organization.Enterprises try to utilize this wealth to gain competitive advantage whenmaking important decisions. Enterprise software and systems include EnterpriseResource Planning, Customer Relationship Management, and Supply ChainManagement systems.
These systems convert and store the data in theirdatabases; therefore, they can be used as a pool of data to support decisionsand explore applicable knowledge. With the potential to gain competitiveadvantage when making important decisions, it is vital to integrate decisionsupport into the environment of their enterprise and work systems. Business intelligencecan be embedded in these enterprise systems to obtain this competitiveadvantage. Inthe past, Decision-Support Systems were independent systems within anorganization and had a weak relationship with other systems i.
e. island systems.Now, enterprise systems are the foundation of an organization, andpractitioners design and may implement business intelligence as an umbrellaconcept to create a comprehensive decision-support environment for management.Based on the ideas of Alter, and the research carried out on the non-functionalrequirements of enterprise software and systems by Jadhav and Sonar, today’sapproach to decision support as a separate, individual system, such as DSS, hasbeen replaced by a new approach.
This new approach creates an integrateddecision-support environment, and takes the intelligence requirements ofenterprise systems into consideration. Ka have also discussed the roles ofintelligence techniques to obtain a successful business strategy in enterpriseinformation systems. Theevaluation of enterprise software and business systems requires models andapproaches that consider intelligence criteria, as well as the enterprisetraditional functional and non-functional requirements and criteria. There havebeen some limited efforts to evaluate BUSINESS INTELLIGENCE, but they havealways considered BUSINESS INTELLIGENCE a system that is isolated from other enterprisesystems. Taking a global view, Designed performance measures, but before theireffort, measurement and evaluation in the BUSINESS INTELLIGENCE field wererestricted to proving the worth and value of investment. discussed measuringthe effects of BUSINESS INTELLIGENCE systems on the business process, andpresented effective methods of measurement. Lin et al. 11 have also developeda performance evaluation model for BUSINESS INTELLIGENCE systems using ANP, butthey have also treated as a separate system.
Arecent research review 6, which reports a systematic review of publishedpapers about evaluating and selecting software packages and enterprise systems,concludes that there is no comprehensive list of criteria for this evaluation.Past research has paid little attention to intelligence criteria and has notcreated models to evaluate these criteria. Our current research addresses theseneeds in the field of evaluation of the intelligence of enterprise software andsystems. However,in the overall view, there are two important issues.
First, the core of BUSINESSINTELLIGENCE is the gathering, analysis, and distribution of information.Second, the objective of BUSINESS INTELLIGENCE is to support the strategicdecision-making process. Bystrategic decisions, we mean decisions related to implementation and evaluationof organizational vision, mission, goals, and objectives with medium tolong-term impact on the organization, as opposed to operational decisions,which are day-to-day in nature and more related to execution 17. Bose18 also describes the managerial view of BUSINESS INTELLIGENCE as a processto get the right information to the right people at the right time, so they canmake decisions that ultimately improve the performance of the enterprise. Thetechnical view of BUSINESS INTELLIGENCE usually centers on the processes orapplications and technologies for gathering, storing, and analyzing data, andfor providing access to data to help management make better business decisions.Another important observation in BUSINESS INTELLIGENCE evolution is thatindustry leaders are currently transitioning from operational BUSINESS INTELLIGENCEof the past to analytical BUSINESS INTELLIGENCE of the future, which focuses oncustomers, resources, and CAPA Business intelligence, to influence newdecisions on an everyday basis.
They have implemented one or more forms ofadvanced analytics for meeting these business needs. Ranjan 19 considers BUSINESSINTELLIGENCE as the conscious methodical transformation of data from all datasources into new forms to provide information that is business-driven andresults-oriented. It often encompasses a mixture of tools, databases, andvendors, to deliver an infrastructure that not only delivers the initial solution,but also incorporates the CAPA Business intelligence of change with businessand the current marketplace. Wuet al.
20 defined BUSINESS INTELLIGENCE as a business management term used todescribe applications and technologies that are used to gather, provide accessto, and analyses data and information about the organization to help managementmake better business decisions. In other words, the purpose of BUSINESS INTELLIGENCEis to provide business systems with actionable, decision-support technologies,including traditional data warehousing technologies, reporting, ad hoc queryingand OLAP. Elbasviret al.
10 refer to BUSINESS INTELLIGENCE systems as an important group ofsystems for data analysis and reporting, which supports managers at differentlevels of the organization with timely, relevant, and trouble-free ways to useinformation, enabling them to make better decisions. They explain that BUSINESSINTELLIGENCE systems are often implemented as enhancements to widely adoptedenterprise systems, such as ERP systems. The scale of investment in BUSINESS INTELLIGENCEsystems reflects its growing strategic importance, highlighting the need formore attention in research studies 10. Insome research, BUSINESS INTELLIGENCE is concerned with the integration andconsolidation of raw data into key performance indicators (KPIs). KPIsrepresent an essential basis for business decisions in the context of processexecution.
Therefore, operational processes provide the context for dataanalysis, information interpretation, and the appropriate action to be taken21. Recently,Jalon and Lindquist 3 wrote that BUSINESS INTELLIGENCE generates analyses andreports on trends in the business environment and on internal organizationalmatters. They explained that analyses may be produced systematically andregularly, or they may be ad-hoc, related to a specific decision-makingcontext. Decision makers at different organizational levels employ thisknowledge. The process results in the generation of both numerical and textualinformationInthis study, we follow the system-enabler approach to define BUSINESS INTELLIGENCE.Organizations would have a better decision-support environment if they were toenhance their enterprise systems with value-added features and functionalities.
Following is a review of limited efforts in the past to study the evaluation ofBUSINESS INTELLIGENCE in enterprise systems.Inresearch, stated the effectiveness of Business Intelligence tools as enablersof knowledge sharing between employees in the organization. They expressed thatBUSINESS INTELLIGENCE does not stand in isolation from other initiatives forexploiting knowledge to drive performance, and they concluded that BUSINESS INTELLIGENCEtools and CAPA Business intelligence laities are necessary in enterprisesystems. Linet al. 11 designed a performance assessment model, and concluded that the accuracyof the output, its conformity to requirements and its support of organizationalefficiency are the most critical factors in gauging the effectiveness of a BUSINESSINTELLIGENCE system.
They set forth the necessity of measurement indicators toshow the performance of a BUSINESS INTELLIGENCE system, but did not provide themeans to evaluate the intelligence of the system. Lindquistand Portyanki 5 discussed BUSINESS INTELLIGENCE as a set of support processesand stated that most literature focuses on justifying the value of BUSINESS INTELLIGENCE.This is an important issue when the usefulness of BUSINESS INTELLIGENCE isunder initial consideration, and later when there is a need to determine if BUSINESSINTELLIGENCE continues to provide valuable results.
They encouragedpractitioners and researchers to start applying the measurement of BUSINESS INTELLIGENCEto their work. Elbasviret al. 10 developed a new concept, based on an understanding of thecharacteristics of BUSINESS INTELLIGENCE systems in a process-orientedframework.
They examined the relationship between the performance of businessprocess and organizational performance, finding significant differences in thestrength of their relationship in different industrial sectors. They concludedby stressing the need for a better understanding of BUSINESS INTELLIGENCEsystems through evaluation. Karamaet al. 9 discussed the roles of intelligence techniques in enterpriseinformation systems, to obtain a successful business strategy. Intelligencetechniques are rapidly emerging as new tools in information management systems.They stressed that intelligence techniques can be used in the decision processof enterprise information systems. They concluded that hybrid systems thatcontain two or more intelligence techniques would be used more in future;therefore, organizations need to take a sophisticated approach to theevaluation of the intelligence of their information systems.
Consideringrecent literature and related work described above, organizations need modelsand approaches to evaluate and assess the BUSINESS INTELLIGENCE CAPA Businessintelligence lities and competencies of their work systems, to achievecompetitive advantage by making the right decisions at the right time. In thisresearch, we have identified the relevant evaluation criteria and have createdan approach to evaluate the intelligence of enterprise systems. Articles fromjournals, conference proceedings, doctoral dissertations and textbooks wereidentified, analyzed, and classified. It was also necessary to search through awide range of studies from different disciplines, since numerous criteria arerelated to the intelligence of a system and to decision support. Therefore, thescope of the search was not limited to specific journals, conferenceproceedings, doctoral dissertations, and textbooks. Management, IT, computingand IS are some common academic disciplines in BUSINESS INTELLIGENCE research.
Consequently, the following online journals, conference databases dissertationdatabases and textbooks were searched to provide a comprehensive Businessintelligence biography of the target literature: ABUSINESS INTELLIGENCE /INFORMdatabase, ACM Digital Library, Emerald Full text, J Stork, IEEE Xplore,ProQuest Digital Dissertations, Sage, Science Direct, and Web of Science. Methodologyof the data collection Themain targets of the study were stakeholders in organizations, who were involvedin decision making and were familiar with BUSINESS INTELLIGENCE and IT tools.Therefore, the main targets of the sampling were CIOs (Chief InformationOfficers), IT Managers, and IT Project Managers, who are involved in IT effortsand decision making. Empirical results and analysisDatacollection Theresearch targets were CIOs (Chief Information Officers), IT Managers and ITProject Managers. The number of questionnaires sent out was 420 and the numberreturned was 185, which showed a return rate of 44.
04%. Of the returnedquestionnaires, twenty-six were incomplete and thus discarded, making the numberof valid questionnaires 176, or 41.90% of the total number sent out. .ConclusionIbelieve that this research will enable organizations to make better decisionsfor designing, selecting, evaluating, and buying enterprise systems, usingcriteria that help them to create a better decision-support environment intheir work systems. The main limitations of this research include thelocalization of interviewees, differences between the functionalities ofenterprise systems and the novelty of Business concepts in industry.
Of course,further research is needed. One important topic for the future is the design ofexpert systems (tools) to compare vendor products. Another is application ofthe criteria and factors that we have identified and defined in an MCDMframework, to select and rank enterprise systems based on BUSINESS INTELLIGENCEspecifications. The complex relationship between these factors and thesatisfaction of managers with the decision-making process should also beaddressed in future research.