Data Analytics and Business Intelligence

This article discusses the importance of data analytics and business intelligence in improving business performance. It explores different options for enterprise-level BI architecture, including doing nothing, data visualization, and reporting. The pros and cons of each option are examined, and a recommendation is made for implementing a reporting BI architecture. The article also discusses the role of stakeholders in ensuring the success of the project and the expected benefits of BI enterprise-level architecture.

Running head: Data Analytics and Business Intelligence
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Executive Summary
In this era of business competition, it is only sensible that we adopt the best business practices
which are particularly suitable to the improvement of business performance. One such practice is
the adoption of Business intelligence architecture system to boost the process of business
analysis.
Objective
To identify a suitable enterprise-level business intelligence architecture as well as propose a
realistic implementation of the system into our business.
Process
In order to identify and recommend the best Business intelligence architecture, we examine three
different options that the business can adopt. In particular, we examine the effect of failing to
adopt any architecture at all, adopting a visualization architecture, and a reporting architecture.
Results and Recommendation
We examined the pros and cons of not adopting a BI, visualization BI architectures and reporting
BI architectures. From our examination, we note that a reporting architecture would be the most
suitable for use due to its ability to utilize visualization and dashboard BI architectures.

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Problem Statement
We purpose to identify a suitable enterprise-level business intelligence architecture as well as,
propose a realistic implementation of the system into our business. This way, we will be able to
seamlessly integrate our business analysis processes whose objectives are mainly to facilitate
business making decisions with other factors such as data collection and data analysis.
Over the past two decades, data has been at the core of business decision making processes thus
forming a backbone in the improvement of business performance. As such, the main concern
among the executive was how to obtain such data. However, the later years of the past decade
saw increased amount of data coming in to businesses, a factor which prompted the idea of
business intelligence, machine learning, data mining, etcetera.
The question no longer lies on how to collect the data but on how to leverage the enormous
amount of data being collected to the benefit of the business through its many data related
aspects.
First, let us explore the concept of business intelligence (BI), enterprise architecture and how this
two interconnect. This way, we will be able to identify the most suitable enterprise-level business
intelligence architecture to adopt and how to implement it.
Business Intelligence
Business intelligence (BI) is generally “…about how to capture, access, understand, analyze and
turn one of the most valuable assets of an enterprise - raw data - into actionable information in
order to improve business performance” (Ong, Siew, & Wong, 2011).

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Enterprise Architecture
In business practices, enterprise architecture provides a business map which comprises
information as well as systems which support it hence, enterprise architecture acts as a, “…
significant diagnostic tool for identifying the source of problems that arise and for planning
improvements” (West, 2011).
Enterprise-level Business Intelligence Architecture
Given the above definitions, we can therefore define enterprise-level business intelligence
architecture as a framework used in data organization, information management alongside
technology components which are applied in the development of business intelligence systems
for reporting and data analytics.
Stakeholders
In practice, a business intelligence dashboard is likely to have anywhere from 20 to thousands of
individuals having access to it all of who probably have different objectives and roles. Ideally, all
these users are likely to have different interpretations of the BI visualizations with different goals
and varying insight requirements.
With regard to our BI architecture, our main stakeholders will include:
i. Business executives
ii. Shareholders
iii. Software designers and developers
iv. External clients
v. Business Community

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Role of stakeholders in ensuring success of the project
Our main objective is to come up with a suitable enterprise-level business intelligence
architecture which all of our business stakeholders can identify with and apply. In this regard, it
is crucial that we include all of the relevant stakeholders throughout the whole identification and
implementation process.
As such, we expect different contributions from the stakeholders and the community as a whole
towards the project. For instance, the business executives are expected to work closely with our
clients and the IT team tasked with the implementation of the BI in design specification and
development of a relevant architecture for the business.
Shareholders are expected to provide funds and approval of the BI architecture specs in the event
that they are required to do so. Different architecture upon implementation tend to require a
number of computer specifications, in order to determine the optimal specs for various BI
systems, we will consult with Software designers and developers. In addition, the BI should be
such that it delivers specific business insights for practical application.
Expected Benefits of BI Enterprise-level Architecture
Upon implementation of the optimum BI, we hope to:
i. Increase the speed through which the business analysis team conducts data analysis,
planning and reporting
ii. Improve the reporting accuracy
iii. Facilitate better business decisions
iv. Improve operational efficiency
v. Augment the firm’s competitive advantages
vi. Increase customer satisfaction

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Solution Options
Generally, given the short time period within which we are expected to deploy our BI, there are a
number of business intelligence tools which we could consider some of which include: data
visualization, data warehousing, dashboards, and reporting. Let us explore the three solutions
that we propose to be considered for adoption by the business executive i.e. doing nothing, data
visualization, and reporting.
Doing Nothing
This forms the easiest option for the business. Ideally, it involves not adopting any BI and
sticking to the traditional methods of business practices.
Advantages.
The only advantage of doing nothing is that the firm gets to cut the costs involved in both
employing BI experts and acquisition of relevant technologies. In addition, the time restriction
imposed will not affect the business operations in terms of shifting sources of decision-making
support.
Disadvantages.
The modern business world is evolving and becoming more competitive than ever. Failure to
adopt new measures which have the potential of facilitating business decision-making in order to
bolster business performance adversely affects the business ability to compete, monitor its
performance, or leverage its ability.

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Data Visualization
Data visualization is often seen as a visual representation of data whereby; visualization is the
transformation of information into visual form for easy observation (inetsoft, 2011). In business
intelligence, one way of implementing visualization is through application of data visualization
concepts and guidelines using embed features in a given BI software.
An example of data visualization business intelligence architecture is that developed by
Microsoft Azure™ and Power BI ™
Figure 1: Enterprise BI Architecture source: https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/data/
enterprise-bi-sqldw
Advantages of Visualization.
Faster Action.
The tendency of the human brain to process visual information faster and easily, makes
visualization among the fastest actionable BI output; given the fact that visualization tools
provide real-time information. This makes it easier for the stakeholders to evaluate and act upon
different aspects of the business as analyzed and presented in the visualizations (Chugh &
Grandhi, 2013).

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Promotes understanding of connections between operations and results.
Given the number of visualizations presented when using visualization BI, it is possible to
explore different relationships between the various business aspects in focus. For instance, using
a line graph, the business executive can be able to track the trend of business performance in
terms of sales etcetera (Inmon, 2018).
Interact with data.
Using data visualization, one can be able to expose different changes in the data in real-time a
factor that differs from the traditional static charts (inetsoft, 2011). In addition, visualization can
enable data manipulation hence enabling the uncovering of factors.
Disadvantages of Visualization.
Cost.
Visualizations software systems are not exactly the cheapest BI systems in the market. High
performance visualization BIs such as tableau a relatively high cost unless we opt for Microsoft’s
Power Bi ™ whose pro version goes for around $9.99 per user per month (Chugh & Grandhi,
2013).
Security.
Another concern when it comes to visualization BI, is the question of data security (Chugh &
Grandhi, 2013). The fact that users of visualization can interact and manipulate data poses
privacy and security concern for the business data, an aspect that the business should consider
before adopting a BI system.

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Reporting
Generally, when we refer to Business intelligence reporting, we can define it as the process of,
“…receiving /providing information or reports to end -users/ organizations /applications through
a BI software /solution” (Feldman & Himmelstein, 2013). However, reporting BI architecture
often use both visualization and dashboards in its reporting.
An example of a reporting Business intelligence system is that developed by Datapine ™ and
Sisense ™
Figure 2: Reporting Business Intelligence Enterprise architecture, source: https://www.researchgate.net/figure/Figure21-
Business-Intelligence-Architecture_fig1_319458909
Advantages of Reporting BI.
Among the key merits of reporting in BI is its role in the collection and presentation of data in a
ready for analysis manner. This way, the stakeholders can readily explore the suggestions
provided by the BI and put it into action (Negash, 2014).

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Other benefits of reporting include:
i. Ability to empower end-users with expertise knowledge in their respective areas of
business
ii. Reporting has the underlying ability to provide back-up for suggested actions through use
of evidence from visualizations and dashboards
iii. Since the reports are somewhat the end product of the BI i.e. the reports are separate from
the original data. Therefore, data in this type of BI is not easy to manipulate, a factor that
boasts the security of data.
Disadvantages.
The main con of reporting BI is the cost of integrating such BI architectures in terms of resources
including human and physical resources as well as probable recurrent expenditure (inetsoft,
2011).

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Recommendations
Following our proposed solutions in the preceding section with regard to the business problem
which required that we identify a suitable enterprise-level business intelligence architecture as
well as, propose a realistic implementation of the system into our business, we make the
following recommendations regarding the best BI and its implementation to our business:
Best Solution
Depending on the approval rate of financing the new architecture, adopting a reporting
enterprise-level business intelligence architecture for use in supporting business analysis process
will enable us beat the tight deadline and enable the implementation and use of the new BI.
This is due to the fact that reporting BI combines both dashboards and visualizations
architectures to provide comprehensive insights to the executive and in as much as reporting
takes longer than visualization, the current deadline will be able to accommodate the integration
of the system. In which case we get to utilize visualization and dashboard aspects of BI system as
a whole.
Implementation
Currently, we use different data collection methods which include direct data entry into business
database, online data scraping from automated sales conducted using the business’s online
trading platforms. The architecture of reporting BI requires a connection to a data source. As
such, we should purchase a suitable reporting BI tool and connect it to our data sources so as to
automate the process of business reporting which in the end will speed up the adoption of
recommended actions.

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Opportunities and Risk
However, upon implementation the architectural shift is prone to conflict owing the fact that
there are both risks involved as well as opportunities. It is therefore crucial that the stakeholders
weigh between the risks and opportunities in order to make an informed decision whether to
adopt or decline the solution.
Risks
Complexity of ETL- according to Passionned Group (2019), “…Extracting and integrating data
from different source systems is difficult. According to a renowned research institute, ETL takes
up 70 to 80 percent of the total (technical) costs involved with building a BI system” (Passionned
Group, 2019). In addition, it is possible that the BI system might be culturally wrong to some
workers hence posing a cultural conflict within the work environment.
Opportunities
As a new concept, BI offers numerous opportunities which can be utilized by the firm. In
adopting the new BI system, we will be able to determine the ROI of our marketing strategy,
derive useful insights from our data, gain understanding what really drives the firm’s revenue.
This way we will be able to personalize various aspects of the business operations including
sales strategies.
References
Chugh, R., & Grandhi, S. (2013). Why Business Intelligence? Significance of Business
Intelligence tools and integrating BI governance with corporate governance.
International Journal of E-Entrepreneurship and Innovation, 4(2), 1-14. Retrieved from
https://www.researchgate.net/publication/273861123_Why_Business_Intelligence_Signif

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e_Governance
Feldman, D., & Himmelstein, J. (2013). Developing Business Intelligence Apps for SharePoint.
California: O'Reilly Media.
inetsoft. (2011, March 12). Introduction to Data Visualization in Business Intelligence. Retrieved
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https://www.inetsoft.com/company/introduction_to_data_visualization_in_business_intel
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Inmon, W. (2018). Untangling the Definition of Unstructured Data. In Big Data & Analytics
Hub (pp. 6-23). New York: IBM.
Negash, S. (2014). Business Intelligence. Communications of the Association for Information
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Ong, I. L., Siew, P. H., & Wong, S. F. (2011). A Five-Layered Business Intelligence.
Communications of the IBIMA, 1-11.
Passionned Group. (2019, January 23). Top 10 BI risks and obstacles to success. Retrieved from
www.passionned.com/top-10-bi-risks-and-obstacles-to-success
West, M. (2011). Data Models and Enterprise Architecture. In Developing High Quality Data
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