Running Head: BI AND ANALYTICS 1

Case Study #3: Business Intelligence and Analytics

Yvonne DiMatteo

Notre Dame de Namur University

BUS4200 Enterprise Information Management Systems

July 2017

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Case Study #3: Business Intelligence and Analytics

Problem Statement

“The use of business intelligence in the Cloud is a game changer” (Heisterberg & Verma,

2014, p. 135). Using business intelligence (BI) in the Cloud enables organizations to have faster

access to important decision-making information without the need to invest heavily in

information technology (IT) infrastructure (Heisterberg & Verma, 2014). However, challenges

accompany the migration from on premise BI to cloud BI. According to Heisterberg and Verma

(2014), IT leaders should carefully consider the trustworthiness of data, if a hybrid model is an

appropriate solution, security levels cloud-based BI solutions, building the right infrastructure,

and facilitating change management when evaluating cloud BI.

Challenges & Opportunities

Information is powerful. Information tells organizations how their current operations are

performing and information analytics help business leaders estimate and strategize how future

operations might perform (Baltzan, 2015). Organizations refer to the collection and analyzing of

information as Business intelligence (BI) (Baltzan, 2015). Organizations collect information

from multiple sources including, but not limited to suppliers, customers, competitors, partners,

and industries and analyze patterns and trends for strategic decision-making (Baltzan, 2015).

Baltzan (2015) categorizes information into two types, transactional and analytical. (See

figure 1.) Transactional information includes all “information contained within a single business

process” and the primary purpose of transactional information “is to support daily operational

tasks” (Baltzan, 2015, p. 85). For example, operations managers might use transactional

information such as sales reports and production schedules to make decisions regarding how

much inventory to carry (Baltzan, 2015). Analytical information includes all organizational

information and the primary purpose of analytical information “is to support the performing of

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managerial analysis tasks” (Baltzan, 2015, p. 86).

Figure 1. Transactional versus Analytical Information (Baltzan, 2015, p. 87)

Business intelligence needs a database to maintain information and a database

management system (DBMS) to control access and security as well as create, read, update, and

delete information in a database (Baltzan, 2015). Employees of an organization send requests to

the DBMS and the DBMS would actually perform the manipulation of the information in the

database to answer the employees’ requests (Baltzan, 2015). (See figure 2.) Some popular

DBMS include Microsoft Access, SQL server, and Oracle (Baltzan, 2015).

Figure 2. Relationship of Database, DBMS, and User (Baltzan, 2015, p. 92)

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In the 1990s, organizations found that the individual databases for their traditional

operational systems were not adequate and could not provide timely or appropriate information

for business analysis (Baltzan, 2015). The desire to have a place to store relevant information to

produce strategic reports for management led to the development of data warehouses (Baltzan,

2015). According to Bose (2009), the data warehouse is the core of a well-developed BI

program. Baltzan (2015) defines data warehouses as “a logical collection of information

gathered from many different operational databases that supports business analysis activities and

decision-making tasks” (p. 105). Both databases and data warehouses store information, but the

key difference is that a data warehouse stores the information in an aggregate form more

appropriately suited to support decision-making tasks (Baltzan, 2015).

For many years, databases and data warehouses required significant investment in on

premise IT infrastructure (Heisterberg & Verma, 2014). The development of the Cloud “broke

the barrier of physical infrastructure” and now “enables employees of an organization to carry

out most business functions remotely without being physically present on the job site”

(Heisterberg & Verma, 2014, p. 113).

Business Solution

The managerial view of BI is getting the right information to the right people at the right

time for decision-making purposes (Bose, 2009). The technical view of BI is the applications

and technologies used to gather, store, analyze, and access the information to make better

decisions (Bose, 2009). In today’s highly competitive environment, organizations around the

world rely on “their ability to make accurate, timely, and effective decisions at all levels,

operational, tactical, and strategic, to address their customers’ preferences and priorities” (Bose,

2009, p. 155). Cloud-based software application packages for business intelligence, business

analytics, and predictive analytics have become the way of the future by offering integrates

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Enterprise Information Management Systems (EIMS) solutions.

Business Analytics

Data analytics is a science and technology that emphasizes three functions, examining

information, summarizing information, and drawing conclusions from information (Sun, Strang,

& Firmin, 2017). Sun, Strang, and Firmin (2017) list the fundamentals of data analytics to

consist of mathematics, statistics, engineering, human interface, and computer science. Data

analytics also includes both software applications and management techniques (Sun et al., 2017).

Data analytics requires data mining (DM) from data warehouses to discover, model,

predict, and communicate information for decision-making purposes (Sun et al., 2017). Sun et

al. (2017) summarizes different types of data analytics into an ontology (a set of categories) of

business analytics and uses figure 3 to illustrate the ontology.

Figure 3. An Ontology of Business Analytics (Sun et al., 2017, p. 171)

Data analysis is at the bottom of the ontology because data analysis is the most general concept.

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Sun et at. (2017) considers all other analytics (i.e., web analytics, information analytics,

knowledge analytics, and big data analytics) as the generalized and applied form of data

analytics and supports business analytics at the top of the ontology. “Therefore, information can

be generalized from data, while knowledge can be generalized from information” (Sun et al.,

2017, p. 171).

According to Sun et al. (2017), data analytics facilitates the development of EIMS.

EIMS include analytical applications that organizations use to evaluate business performance

(Sun et al., 2017). “The [EIMS] and business analytics share some common tools to support

business decision making and improve the business performance of [organizations]” (Sun et.al,

2017, p. 173). Similar to Heisterberg and Verma (2014), Sun et al. (2017) believe four out of the

five technology pillars (i.e., cloud services, mobile services, big data services, and social

networking services) are becoming an inevitable component of any EIMS. Data analytics

services support cloud services, mobile services, big data services, and social networking

services as shown in figure 4.

Figure 4. Analytics Services Support EIMS

Predictive Analytics

An increasing number of organizations are turning toward predictive analytics (also

known as advanced analytics) to gain control over daily decisions because predictive analytics

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provides a 360 degrees view of operations and customers (Bose, 2009). Predictive analytics is a

general term, which means applying advanced analytic techniques to information to answer

questions or solve problems (Bose, 2009). Predictive analytics “is not a technology in and of

itself, but rather, a group of tools that are used in combination with one another [to gather and

analyze information and then predict outcomes]. Data integration and data mining are the basis

for [predictive] analytics. The more information gathered and integrated allows for more pattern

recognition and relationship identification” (Bose, 2009, p. 156). Figure 5 illustrates the stages

in the BI evolution.

Figure 5. BI Evolution (Bose, 2009, p. 157)

For many years, BI was similar to online analytical processing (OLAP) query-and-

reporting tools that only provided historical information (Bose, 2009). Organizations needed

more robust and comprehensive solutions to gain competitive advantage thus leading to

predictive analytics applications (Bose, 2009). “[Predictive analytics] applications are typically

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powered either by business rules engines (which apply logical conditions to determine how a

certain case should be handled) or predictive models (which probabilistically identify the action

most likely to achieve the desired results) (Bose, 2009, p. 157).

A recent advancement in predictive analytics technology has led to the ability to conduct

text mining and web mining, which “adds a richness and depth to the patterns already uncovered

through [an organization’s] data mining efforts. Text mining applies the same analytical

functions of data mining to the domain of textual information, relying on sophisticated, text

analysis techniques that distill information from free-text documents” (Dorre et al., 1999;

Oliveira et al., 2004 as cited in Bose, 2009, p. 156). Web mining discovers patterns by

conducting data mining over the web (Bose, 2009). Bose (2009) lists web content mining, web

structure mining, and web usage mining as the categories of web mining.

According to Bose (2009), “a successful BI infrastructure must be able to transform

disparate data and systems into an efficient flow of information, analyze data with a forward-

looking view, and deliver key information to decision makers on demand” (p. 158). Figure 6

depicts an infrastructural framework for BI using predictive analytics.

Figure 6. Framework for BI using predictive analytics (Bose, 2009, p. 159)

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Lessons Learned/Business Case

Business intelligence, business analytics, and predictive analytics are all components of

enterprise information management systems, which ultimately help organizations with their day-

to-day and strategic decision-making. The applications and tools that organizations use to

conduct the analytics to turn data into information and information into knowledge are moving

from on premise infrastructure to the Cloud. Cloud-based solutions for BI and analytics can save

organizations money, but more importantly, the solutions can help organizations gain a

competitive advantage by providing accurate and timely information to organization decision-

makers.

Why I Care

Business intelligence helps build alliances between chief marketing officers and chief

information officers, which can lead to alliances among other organizational leaders (Heisterberg

& Verma, 2014). The alliances and bonds between organizational leaders will provide the

organization with a united team of decision-makers. Organizational leader will more likely

accept and support cloud-based business intelligence and analytics solutions that can then help

the leaders make better decisions to achieve the organization’s goals.

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References

Baltzan, P. (2015). Business driven technology (6th ed.). New York, NY: McGraw Hill

Education.

Bose, R. (2009). Advanced analytics: Opportunities and challenges. Industrial Management &

Data Systems, 109(2), 155-172. doi:http://dx.doi.org/10.1108/02635570910930073

Heisterberg, R., & Verma, A. (2014). Creating business agility: How convergence of cloud,

social, mobile, video, and big data enables competitive advantage. Hoboken, NJ: John

Wiley & Sons, Inc.

Sun, Z., Strang, K., & Firmin, S. (2017). Business analytics-based enterprise information

systems. The Journal of Computer Information Systems, 57(2), 169-178.

doi:http://dx.doi.org/10.1080/08874417.2016.1183977