Laurentis
Crowson

CTS
115

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Fall
2017

Introduction

Nowadays,
information and knowledge represent the fundamental wealth of an organization.
Enterprises try to utilize this wealth to gain competitive advantage when
making important decisions. Enterprise software and systems include Enterprise
Resource Planning, Customer Relationship Management, and Supply Chain
Management systems. These systems convert and store the data in their
databases; therefore, they can be used as a pool of data to support decisions
and explore applicable knowledge. With the potential to gain competitive
advantage when making important decisions, it is vital to integrate decision
support into the environment of their enterprise and work systems. Business intelligence
can be embedded in these enterprise systems to obtain this competitive
advantage.

 

In
the past, Decision-Support Systems were independent systems within an
organization and had a weak relationship with other systems i.e. island systems.
Now, enterprise systems are the foundation of an organization, and
practitioners design and may implement business intelligence as an umbrella
concept to create a comprehensive decision-support environment for management.
Based on the ideas of Alter, and the research carried out on the non-functional
requirements of enterprise software and systems by Jadhav and Sonar, today’s
approach to decision support as a separate, individual system, such as DSS, has
been replaced by a new approach. This new approach creates an integrated
decision-support environment, and takes the intelligence requirements of
enterprise systems into consideration. Ka have also discussed the roles of
intelligence techniques to obtain a successful business strategy in enterprise
information systems.

 

The
evaluation of enterprise software and business systems requires models and
approaches that consider intelligence criteria, as well as the enterprise
traditional functional and non-functional requirements and criteria. There have
been some limited efforts to evaluate BUSINESS INTELLIGENCE, but they have
always considered BUSINESS INTELLIGENCE a system that is isolated from other enterprise
systems. Taking a global view, Designed performance measures, but before their
effort, measurement and evaluation in the BUSINESS INTELLIGENCE field were
restricted to proving the worth and value of investment. discussed measuring
the effects of BUSINESS INTELLIGENCE systems on the business process, and
presented effective methods of measurement. Lin et al. 11 have also developed
a performance evaluation model for BUSINESS INTELLIGENCE systems using ANP, but
they have also treated as a separate system.

 

A
recent research review 6, which reports a systematic review of published
papers 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 not
created models to evaluate these criteria. Our current research addresses these
needs in the field of evaluation of the intelligence of enterprise software and
systems.

 

However,
in the overall view, there are two important issues. First, the core of BUSINESS
INTELLIGENCE is the gathering, analysis, and distribution of information.
Second, the objective of BUSINESS INTELLIGENCE is to support the strategic
decision-making process.

 

By
strategic decisions, we mean decisions related to implementation and evaluation
of organizational vision, mission, goals, and objectives with medium to
long-term impact on the organization, as opposed to operational decisions,
which are day-to-day in nature and more related to execution 17.

 

Bose
18 also describes the managerial view of BUSINESS INTELLIGENCE as a process
to get the right information to the right people at the right time, so they can
make decisions that ultimately improve the performance of the enterprise.

 

The
technical view of BUSINESS INTELLIGENCE usually centers on the processes or
applications and technologies for gathering, storing, and analyzing data, and
for providing access to data to help management make better business decisions.
Another important observation in BUSINESS INTELLIGENCE evolution is that
industry leaders are currently transitioning from operational BUSINESS INTELLIGENCE
of the past to analytical BUSINESS INTELLIGENCE of the future, which focuses on
customers, resources, and CAPA Business intelligence, to influence new
decisions on an everyday basis. They have implemented one or more forms of
advanced analytics for meeting these business needs. Ranjan 19 considers BUSINESS
INTELLIGENCE as the conscious methodical transformation of data from all data
sources into new forms to provide information that is business-driven and
results-oriented. It often encompasses a mixture of tools, databases, and
vendors, to deliver an infrastructure that not only delivers the initial solution,
but also incorporates the CAPA Business intelligence of change with business
and the current marketplace.

 

Wu
et al. 20 defined BUSINESS INTELLIGENCE as a business management term used to
describe applications and technologies that are used to gather, provide access
to, and analyses data and information about the organization to help management
make better business decisions. In other words, the purpose of BUSINESS INTELLIGENCE
is to provide business systems with actionable, decision-support technologies,
including traditional data warehousing technologies, reporting, ad hoc querying
and OLAP.

 

Elbasvir
et al. 10 refer to BUSINESS INTELLIGENCE systems as an important group of
systems for data analysis and reporting, which supports managers at different
levels of the organization with timely, relevant, and trouble-free ways to use
information, enabling them to make better decisions. They explain that BUSINESS
INTELLIGENCE systems are often implemented as enhancements to widely adopted
enterprise systems, such as ERP systems. The scale of investment in BUSINESS INTELLIGENCE
systems reflects its growing strategic importance, highlighting the need for
more attention in research studies 10.

 

In
some research, BUSINESS INTELLIGENCE is concerned with the integration and
consolidation of raw data into key performance indicators (KPIs). KPIs
represent an essential basis for business decisions in the context of process
execution. Therefore, operational processes provide the context for data
analysis, information interpretation, and the appropriate action to be taken
21.

 

Recently,
Jalon and Lindquist 3 wrote that BUSINESS INTELLIGENCE generates analyses and
reports on trends in the business environment and on internal organizational
matters. They explained that analyses may be produced systematically and
regularly, or they may be ad-hoc, related to a specific decision-making
context. Decision makers at different organizational levels employ this
knowledge. The process results in the generation of both numerical and textual
information

In
this study, we follow the system-enabler approach to define BUSINESS INTELLIGENCE.
Organizations would have a better decision-support environment if they were to
enhance their enterprise systems with value-added features and functionalities.
Following is a review of limited efforts in the past to study the evaluation of
BUSINESS INTELLIGENCE in enterprise systems.

In
research, stated the effectiveness of Business Intelligence tools as enablers
of knowledge sharing between employees in the organization. They expressed that
BUSINESS INTELLIGENCE does not stand in isolation from other initiatives for
exploiting knowledge to drive performance, and they concluded that BUSINESS INTELLIGENCE
tools and CAPA Business intelligence laities are necessary in enterprise
systems.

 

Lin
et al. 11 designed a performance assessment model, and concluded that the accuracy
of the output, its conformity to requirements and its support of organizational
efficiency are the most critical factors in gauging the effectiveness of a BUSINESS
INTELLIGENCE system. They set forth the necessity of measurement indicators to
show the performance of a BUSINESS INTELLIGENCE system, but did not provide the
means to evaluate the intelligence of the system.

 

Lindquist
and Portyanki 5 discussed BUSINESS INTELLIGENCE as a set of support processes
and stated that most literature focuses on justifying the value of BUSINESS INTELLIGENCE.
This is an important issue when the usefulness of BUSINESS INTELLIGENCE is
under initial consideration, and later when there is a need to determine if BUSINESS
INTELLIGENCE continues to provide valuable results. They encouraged
practitioners and researchers to start applying the measurement of BUSINESS INTELLIGENCE
to their work.

 

Elbasvir
et al. 10 developed a new concept, based on an understanding of the
characteristics of BUSINESS INTELLIGENCE systems in a process-oriented
framework. They examined the relationship between the performance of business
process and organizational performance, finding significant differences in the
strength of their relationship in different industrial sectors. They concluded
by stressing the need for a better understanding of BUSINESS INTELLIGENCE
systems through evaluation.

 

Karama
et al. 9 discussed the roles of intelligence techniques in enterprise
information systems, to obtain a successful business strategy. Intelligence
techniques are rapidly emerging as new tools in information management systems.
They stressed that intelligence techniques can be used in the decision process
of enterprise information systems. They concluded that hybrid systems that
contain two or more intelligence techniques would be used more in future;
therefore, organizations need to take a sophisticated approach to the
evaluation of the intelligence of their information systems.

 

Considering
recent literature and related work described above, organizations need models
and approaches to evaluate and assess the BUSINESS INTELLIGENCE CAPA Business
intelligence lities and competencies of their work systems, to achieve
competitive advantage by making the right decisions at the right time. In this
research, we have identified the relevant evaluation criteria and have created
an approach to evaluate the intelligence of enterprise systems. Articles from
journals, conference proceedings, doctoral dissertations and textbooks were
identified, analyzed, and classified. It was also necessary to search through a
wide range of studies from different disciplines, since numerous criteria are
related to the intelligence of a system and to decision support. Therefore, the
scope of the search was not limited to specific journals, conference
proceedings, doctoral dissertations, and textbooks. Management, IT, computing
and IS are some common academic disciplines in BUSINESS INTELLIGENCE research.
Consequently, the following online journals, conference databases dissertation
databases and textbooks were searched to provide a comprehensive Business
intelligence biography of the target literature: ABUSINESS INTELLIGENCE /INFORM
database, ACM Digital Library, Emerald Full text, J Stork, IEEE Xplore,
ProQuest Digital Dissertations, Sage, Science Direct, and Web of Science.

 

 

Methodology
of the data collection

 

The
main targets of the study were stakeholders in organizations, who were involved
in decision making and were familiar with BUSINESS INTELLIGENCE and IT tools.
Therefore, the main targets of the sampling were CIOs (Chief Information
Officers), IT Managers, and IT Project Managers, who are involved in IT efforts
and decision making.

 

 Empirical results and analysis

Data
collection

 

The
research targets were CIOs (Chief Information Officers), IT Managers and IT
Project Managers. The number of questionnaires sent out was 420 and the number
returned was 185, which showed a return rate of 44.04%. Of the returned
questionnaires, twenty-six were incomplete and thus discarded, making the number
of valid questionnaires 176, or 41.90% of the total number sent out.

 

 

.
Conclusion

I
believe that this research will enable organizations to make better decisions
for designing, selecting, evaluating, and buying enterprise systems, using
criteria that help them to create a better decision-support environment in
their work systems. The main limitations of this research include the
localization of interviewees, differences between the functionalities of
enterprise systems and the novelty of Business concepts in industry. Of course,
further research is needed. One important topic for the future is the design of
expert systems (tools) to compare vendor products. Another is application of
the criteria and factors that we have identified and defined in an MCDM
framework, to select and rank enterprise systems based on BUSINESS INTELLIGENCE
specifications. The complex relationship between these factors and the
satisfaction of managers with the decision-making process should also be
addressed in future research.