Laurentis Now, enterprise systems are the foundation

Laurentis Crowson

CTS 115

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Fall 2017

1. 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been replaced by a new approach.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 BI, but they have
always considered BI a system that is isolated from other enterprise systems.
Taking a global view, Designed BI performance measures, but before their
effort, measurement and evaluation in the BI field were restricted to proving
the worth and value of BI investment. discussed measuring the effects of BI
systems on the business process, and presented effective methods of
measurement. Lin et al. 11 have also developed a performance evaluation model
for BI systems using ANP, but they have also treated BI 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 BI is the gathering, analysis, and distribution of
information. Second, the objective of BI 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 BI 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 BI 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 BI evolution is that industry leaders are
currently transitioning from operational BI of the past to analytical BI of the
future, which focuses on customers, resources, and capabilities, 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 BI
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 capability of change with business and the current
marketplace.

 

Wu et al. 20 defined BI 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 BI 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 BI 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 BI systems are often implemented as enhancements to widely adopted
enterprise systems, such as ERP systems. The scale of investment in BI systems
reflects its growing strategic importance, highlighting the need for more
attention in research studies 10.

 

In some research, BI 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 BI 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 Table 1, BI definitions are sorted based on three
approaches: a managerial approach a technical approach, and an approach to BI
as an enabler of enterprise s

In this study, we follow the system-enabler approach to
define BI. 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 BI in enterprise systems.

 

Sharma and Dias 4, in their managerial study, stated the
effectiveness of Business Intelligence (BI) tools as enablers of knowledge
sharing between employees in the organization. They expressed that BI does not
stand in isolation from other initiatives for exploiting knowledge to drive
performance, and they concluded that BI tools and capabilities are necessary in
enterprise systems. Their key message to executives was: “We cannot manage what
we do not measure!”

 

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 BI system. They set forth the necessity of
measurement indicators to show the performance of a BI system, but did not
provide the means to evaluate the intelligence of the system.

 

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

 

Elbasvir et al. 10 developed a new concept, based on an
understanding of the characteristics of BI 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 BI 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 BI
capabilities 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. To identify these criteria,
in current research a comprehensive review of relevant literature was conducted
in 2010 and 2011 by authors. 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 BI research. Consequently, the following online journals,
conference databases dissertation databases and textbooks were searched to
provide a comprehensive bibliography of the target literature: ABI/INFORM
database, ACM Digital Library, Emerald Full text, J Stork, IEEE Xplore,
ProQuest Digital Dissertations, Sage, Science Direct, and Web of Science. The
literature search was based on the descriptors, ‘BI capabilities’, ‘decision
support’, ‘decision-support criteria’, ‘BI evaluation criteria’, ‘BI assessment
criteria’, ‘BI requirements’ and ‘intelligent tools capabilities’. The criteria
identified are listed in Table 2 as BI evaluation criteria.

 

 

Methodology of the data collection

 

Following the research objectives, the main targets of the
study were stakeholders in organizations, who were involved in decision making
and were familiar with BI 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. Based on 99,96,
the data-collection method was based on a simple random selection of targets in
the list of Fortune 500 companies.

 

 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 BI
specifications. The complex relationship between these factors and the
satisfaction of managers with the decision-making process should also be
addressed in future research.