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International Management Review
Vol. 11 No. 2 2015
Business Intelligence Technology, Applications, and Trends
Muhammad Obeidat, Max North, Ronny Richardson, and Vebol Rattanak
Management and Entrepreneurship Department
Information Systems Department
Coles College of Business
Kennesaw State University, Kennesaw, GA 30144, USA
Max@kennesaw.edu
Sarah North
Computer Science Department
College of Computing and Software Engineering
Kennesaw State University, Kennesaw, GA 30144, USA
[Abstract] Enterprises are considering substantial investment in Business Intelligence (BI) theories and
technologies to maintain their competitive advantages. BI allows massive diverse data collected from
virus sources to be transformed into useful information, allowing more effective and efficient production.
This paper briefly and broadly explores the business intelligence technology, applications and trends
while provides a few stimulating and innovate theories and practices. The authors also explore several
contemporary studies related to the future of BI and surrounding fields.
[Keywords] Business Intelligence, Competitive Intelligence, Data Warehousing, Data Mining, Cloud
Computing, Data Exploration and Visualization
Introduction
Data is growing at a rapid rate. Enterprises are turning to Business Intelligence (BI) theories and
technologies in order to extract the maximum amount of information from this data in order to allow their
employees to make better data-driven business decisions. BI transforms the raw, massive data collected
by various sources into useful information. This information supports business operations, ultimately
providing long-term stability for the firm (Rud, 2009). Additionally, as enterprises grow, there is an
overwhelming need to analyze historical business data in order to predict future trends and improve
business forecasting. A broader definition of BI is presented by Evelson (2008): “[BI] is a set of
methodologies, processes, architectures, and technologies that transform raw data into meaningful and
useful information.” Evalson builds on this, “[BI] allows business users to make informed business
decisions with real-time data that can put a company ahead of its competitors.”
In a recent article, Chaudhuri, Umeshwar, and Narasayya (2011) provided a broad overview of
current BI technologies, and the manner in which they interact. The specific technologies addressed
include extract transform load tools, complex event processing engines, relational database management
systems, map-reduced paradigms, online analytic processing servers, reporting servers, enterprise search
engines, data mining, and text analytic engines. The typical BI architecture is outlined as data moves
through data sources, streaming engines, data warehouse servers, mid-tier servers, and front end
applications. The article addresses the insights of reduced cost of data acquisition and storage as well as
the resulting increased use by businesses acquiring large volumes of data to promote competitive
advantages. Chaudhur et al. discuss new massively parallel data architectures and analytic tools, which
are superior to traditional parallel SQL data warehouses and OLAP engines, and the need to shorten lag
between data acquisition and decision making.
The general goal of this paper is briefly and broadly to explore the BI technology, applications and
trends while provides stimulating and innovate theories and practices. We explore several contemporary
studies related to the future of BI and surrounding fields.
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International Management Review
Vol. 11 No. 2 2015
Competitive Intelligence
It is important to understand that Competitive Intelligence (CI) is a term sometimes used as a synonym
for business intelligence; however, CI is more accurately a sub-discipline of BI widely used for larger
business clusters, focusing on textual reports prepared from public resources to help decision makers
understand competitive environments. Consequently, Nemrava, Ralbovsky, Kliegr, Splichal, Svatek, and
Vejlupek (2008) describe business clusters as geographic concentrations of interconnected businesses,
suppliers, and any other companies in an associated field. The goal is to use semantic structures and
business maps to enhance CI reports for easier retrieval of information and lucid presentation of complex
information to support decision-makers’ strategies. Nemrava et al. conducted a study with a group of
three hundred students who were trained to collect information for CI reports to address 3 fields:
packaging, glass, and information industries. They designed core CI ontology and used Porter’s Five
Forces as the underlying CI model. They also used two software tools, Ontopoly and Tovek Topic Matter
(TTM), to better display and edit the ontology. Since this project was likely the first attempt to link CI
reports with semantic technologies, specifically in large business clusters, the researchers suggest
additional future work is needed.
Diverse Business Intelligence Applications
Business Intelligence applications are sporadically used in a majority of search-based applications within
a variety of fields, such as Business, Security, Finance, Marketing, Law, Education, Visualization, Science,
Engineering, Medicine, Bioinformatics, Health Informatics, Humanities, Retailing, and
Telecommunications, just to list a few. While BI is widely used in Enterprises (private or public entities)
for both standard business and e-business, BI applications are growing in many diverse fields. For
instance, in the areas of Mobile Device Fraud Detection, Health Care Informatics, and even in Chronic
Disease Management, studies are beginning to describe the advantages of BI applications.
Mobile Device Fraud Detection
Nguyen, Schiefer, and Tjoa (2005) reported on the use of real-time analytics to detect fraud of business
process and operation. By providing real-time monitoring of processes, businesses were able to capitalize
quickly on time-sensitive business opportunities. The sample of mobile phone fraud detection was used to
gather events and was analyzed to detect usage patterns for normal or fraudulent behavior.
Health Care Informatics
Zheeng, Zhang, and Li (2014) addressed the lack of BI applications in Healthcare Informatics. They
described BI and healthcare analytics as emerging technologies that can improve industry service quality,
reduce cost, and manage risks. They note, however, that analytics healthcare data processing is mostly
missing from current healthcare information technology (HIT) programs. Their paper conducted an
analysis of how BI technologies can be incorporated into an HIT program. A general framework and
several strategies were presented; the authors conclude by stating they will expand their investigation onto
a national level to improve the framework. It is their hope that more HIT programs will recognize the
importance of healthcare BI.
Chronic Disease Management
Wickramasinghe, Alahakoon, Georgeff, Schattner, De Silva, Alahakoon, Adaji, Jones, and Piterman (2011)
investigated BI use for chronic disease management. They identified chronic disease management as one
area of healthcare in which health knowledge management can have a positive effect. Their research
presented a new BI module that will analyze, visualize, and extract knowledge from the chronic disease
management network (cdmNet). Their aim was to facilitate short- and long-term decision making and
improve the ability to understand care models, policy models, and economic models which are part of
chronic disease management. Their paper contained results which obtained by applying this model to the
data. The module consisted of three sub-modules: pre-processing, dashboard, and data mining. Pre48
International Management Review
Vol. 11 No. 2 2015
processing converts cdmNet data to a suitable form, the dashboard provides an interface, and data mining
extracts patterns which can potentially provide solutions to questions concerning chronic disease
management.
Assorted Features of Business Intelligence
Although a good number of features of BI theory and practice exist, we will discuss here the most
prominent and well-researched. There are several research thrusts related to assorted aspects of BI worthy
of exploration: Data Integration, Real-Time Analytics, Balanced Efficiency and Effectiveness, and
Collaboration and Teamwork.
Data Integration
Dayal, Castellanos, Simitsis, and Wilkinson (2009) analyzed and described the requirements necessary for
data integration flows in the “next generation” of operational BI systems, the limitations of current
technologies, challenges, and a framework to address these challenges. Their goal was to facilitate the
design and use of optimal flows to meet new and evolving business requirements. Their paper
investigated the traditional BI architecture and compared it to next generation architecture. Their solution
was a layered methodology for data integration flow life cycles. Metrics and tradeoffs were discussed,
and the pros were shown to outweigh the cons. They concluded that with the more complex integration
flow designs, it is important to create automated or semi-automated techniques to help practitioners deal
with the complexity.
Real-Time Analytics
Nguyen, Schiefer, and Tjoa (2005) proposed an event-driven information technology infrastructure for
operating BI applications to enable real-time analytics over business processes and operations. A “sense
and response service architecture” called SARESA provided real-time monitoring of processes and
allowed businesses to quickly capitalize on time-sensitive business opportunities. The real-time analysis
requirements of a BI system, which are not a part of the traditional BI system, included data freshness,
continuous data integration, analysis and active decision engines, high availability, and scalability. As
mentioned earlier, the sample of mobile phone fraud detection was used to walk through the architecture’s
approach. Call Detail Records (CDRs) are gathered as events and analyzed to detect usage patterns for
normal or fraudulent behavior. This was a prototype of the SARESA system, and it will continue to be
developed to support time-sensitive BI platforms.
Balanced Efficiency and Effectiveness
Finneran and Russell (2011) presented an article on Balanced Business Intelligence arguing that
companies may be better served by concentrating on capability instead of maturity. The article was
broken down in sections that would help with the balance, starting with Managed BI growth, Evaluating
BI capacity, scope of delivery, information delivery capability curve, and levels of BI. Managed BI
growth can be linked with BI capability, meaning that at any stage it is significant to operational, tactical
or strategic perspective. For example, if a good is going to be made for one vendor, they may ask, “What
is the most cost-effective way to manage our people and process to produce a product for our customer?”
Next, they moved on to describing identifying and building capability, optimizing the architecture, and
controlling the flow of information, focusing on areas defining organizational BI needs. For each category,
authors identified needs to conceive and compose. The identification and building of capacity requires
performance business-sustaining processes and generation of operational and managerial reporting
capability.
To optimize, businesses need to measure and manage through the creation of standard measures and
tracking history to perform trend analysis for lines of business. Lastly, controlling the flow of information
was segmented into govern and protect, meaning a continued framework for data governance to enable
stewardship and improve corporate data confidence across the enterprise and the protection of
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information delivered internally to the enterprise. The scope of delivery was described as the importance
of getting information to the people that need it, when they need it. Using this helps BI to be effective by
defining the audience and the manner for delivery as well as the method of access across the organization.
Lastly, the levels of BI, which are described as stepping stones to success are described: Operational
reporting, Tactical reporting, Strategic Reporting (History and Trending), Performance and Improvement,
Highly Available and Highly Trusted, Highly Focused, and Highly Administered. To conclude the article
they state, “The balance of both efficiency and effectiveness enables a well-rounded intelligence program
in any organization.”
Collaboration and Teamwork
Berthold, Wortmann, Carenini, Campbell, Bisson, Strohmaier, and Zollep (2010) strived to create a
system which would be highly scalable and flexible for gaining collaborative, ad hoc BI. The common
shortcomings with organizations are the lack of business context information for analytical data, with too
little emphasis on data from strong collaboration and a lack of integrating external or unstructured
information in an effective and timely way. The BI platform proposed allows business users to shape their
strategies in a collaborative manner, putting information acquisition back into the business user’s hands. It
is accomplished with a flexible data model, scalable data store, a business configuration methodology, an
information self-service environment, and an integrated collaboration environment (for instance,
“Collaboration Rooms”). By using these methods, business users have the architecture for ad hoc and
collaborative decision making.
Furthermore, Lovell at el. (2014) stated that plenty of vendors promise to solve all business users’ or
technical teams’ problems with their tool sets and methodologies. With the mounting pressure on BI teams
(whether embedded in organizations or those of consultancies) to deliver on time and meet expectations,
it is no wonder that the allure of agile BI has cast its net on unsuspecting teams desperate for success. It is
possible to learn from an execution and delivery methodology crafted around the concept of the “team”
rather than the “individual.” This article looked at how teams can implement the agile mindset in building
data output applications. It explained the concepts and how they relate to BI projects, rather than the
typical data input applications managed through the software delivery life cycles commonly associated
with the term “agile.”
Data Storage and Technology
As computer technology advances, larger volume of data are acquired and stored at much lower cost. Any
classification of transaction in business, including e-business, RFID tags, Web sites, emails, blogs, and
many more produces new data to be tracked. Authors briefly provide most important aspects of data
storage and technology below, beginning with Data Type (Structured and Unstructured), Data
Warehousing, Data Mining, and Data in Clouds.
Data Type (Structured and Unstructured)
In a broad context, there are two types of data—structured and unstructured—to be incorporated in BI
phases. Park and Song (2011) introduced structured and unstructured data by stating that as the amount of
data grows very fast inside and outside of an enterprise, it becomes important to seamlessly analyze both
of categories to establish robust BI. Particularly as most valuable business information is encoded in the
unstructured text documents, including Internet web pages, specialized Text OLAP solutions are needed
to perform multi-dimensional analysis on text documents in the same way as on structured relational data.
Since text mining and information retrieval are major technologies for handling text data, authors first
review the representative works selected for demonstrating how they can be applied for Text OLAP. Then
authors conduct a survey of the representative works selected for demonstrating how analysts can
associate and consolidate both unstructured text documents and structured relation data for obtaining total
BI. Finally, the authors present the architecture for a total BI platform incorporating structured and
unstructured data. It is expected that the proposed architecture, which integrates information retrieval, text
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mining, and information extraction technologies alongside relational OLAP technologies, would make an
effective platform toward total BI.
Data Warehousing
One of the main sources of data provided for BI applications is collected from data warehouses. Data
acquisition is becoming cheaper and easier, while the size of the data are getting larger, within range of
tens to hundreds of terabytes. Farooq and Sarwar (2010) examine real-time data warehousing (RTDW)
and highlight the advantages of using semi- structured multidimensional modeling (DMM), such as XML,
in RTDW versus traditional DMM, such as relational. The two are compared on four characteristics,
including heterogeneous data integration, types of measures supported, aggregate query processing, and
incremental maintenance. The authors also provide explanations as to why semi-structured DMM is better
than structured DMM. In their article, they used the RTDW framework as an example for a
telecommunication company. Their experiment showed that if a delay is caused in incremental
maintenance of DMM, there is no ETL technology that can help in real-time BI. They conclude that semistructured XML-DMM is more capable for incorporating real-time data updates from operation sources.
Not only does it reduces query response time, but also increases real-time BI.
In an article, Goeke and Faley (2007) wrote how data warehouse flexibility affects its use. In the
beginning, background knowledge is given before the research is done. A data warehouse enables the
collection and storage of vast amounts of data extracted and analyzed by end users. Now the research,
which was done in a form of a survey including the original TAM items adapted to fit a data warehousing
environment, was sent to managerial-level data warehouse users in a number of major Midwest U.S.
Corporations. The survey also obtained other information, including the industry and size of the user’s
company, position and department, the amount and type of system-related training the user had, what
system support was most useful to the user, and the amount of experience the user had with the data
warehouse. The research used various scales to get to the results. The results that they achieved were well
in line with previous studies conducted. In conclusion, they made recommendations for increasing data
warehouse usage by leveraging its flexibility. The extent to which the data warehouse is perceived to
enhance job performance is the most important determinant of its usage. Flexibility is not a major
determinant of usage, and users will not use a data warehouse because it is flexible. Lastly, system
flexibility is embedded within the features of the data warehouse, meaning that sophisticated users are
more likely to leverage system flexibility, because they are savvy enough to know where the flexibility
exists in the data warehouse.
Data Mining
In simple terms, data mining provides extensive and complex analysis of historical and current data,
allowing the building of predictive models. An article by Grossman, Hornick, and Meyer (2002)
described Data Mining in great detail, starting with established and emerging standards that address
various aspects of data mining, including Models, Attributes, Interfaces, Settings, Process, and Remote
and Distributed Data. After a brief description of the aspects of data mining, authors move into the
different standards of data mining and break them up into three major categories XML Standards,
Standard API’s, and other standard efforts. In XML standard there was a group known as the Data Mining
Group that deve …
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