IT Infrastructure Management Report: Cloud Computing and Data Analysis

This report delves into the multifaceted realm of IT Infrastructure Management, offering a comprehensive analysis of various critical components. It begins by defining key concepts such as Electronic Records Management, Business Intelligence and Analytics, Data and Text Mining, Big Data Analytics and Data Recovery, Enterprise Architecture, Management Information System, Data Life Cycle and Data Principles, and Cloud Computing. The report then explores the value creation potential of data and text mining within organizations, highlighting its application in threat, compliance, and risk detection, customer engagement, and informed business decision-making. It further examines the challenges and solutions associated with cloud computing, focusing on issues like insider threats and data breaches. The report also investigates the design, implementation, and issues of Executive Information Systems (EIS), along with the benefits of the new IT architecture and data governance. Finally, the report underscores the importance of POS data processing, customer experience strategies, trusted data, the Black Book Model, and its strategic advantages, providing valuable insights into data management and its impact on business operations.

Running head: IT INFRASTRUCTURE MANAGEMENT
IT Infrastructure Management
Name of the Student
Name of the University
Author’s Note:

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Table of Contents
Question 1..................................................................................................................................2
Question 2..................................................................................................................................3
Question 3..................................................................................................................................4
Question 4..................................................................................................................................5
Question 5..................................................................................................................................6
References..................................................................................................................................8

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Question 1
i) Electronic Records Management: The complete set of computerized programs,
which are designed to store and to track various records, is known as ERM. This is even used
to create as well maintain record.
ii) Business Intelligence and Analytics: The amalgamation of several strategies and
technologies, used by almost all enterprises for analyzing any type of information and data
related to their business (Fernando, Loke & Rahayu, 2013).
All types of technologies, skills and practices to continuously investigate and explore
the existing business performance are known as business analytics. This is mainly utilized to
gain insight and also to drive the business plan.
iii) Data and Text Mining: The practice to test bulk amount of data in the existing
databases to properly generate new information is known as data mining (Witten et al., 2016).
The procedure to obtain high quality of data or information by extracting from text is
known as text mining. The information or data could be extracted by several ways such as
statistical pattern learning.
iv) Big Data Analytics and Data Recovery: Big data analytics can be defined as the
process to test bulk and variable sets of big data to uncover all the concealed patterns,
preference of customers and all types of significant information(Chang, 2014).
The process to retrieve all kinds of corrupted, damaged, lost, inaccessible and
formatted data from any secondary storage or removable media is known as data recovery.
This type of data recovery is usually done when data is not retrieved or accessed under
normal circumstances.

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v) Enterprise Architecture: The proper practice to properly conduct the designing of
an enterprise with proper analysis, planning and implementation is known as enterprise
architecture (Hutchings, Smith & James, 2013). This is done to develop and execute any
particular strategy.
vi) Management Information System: The systematic processing of data or
information to manage and support any managerial decision is known as MIS. This is done
by any computer system or such devices.
vii) Data Life Cycle and Data Principles: The significant approach that is utilized to
manage the overall data flow in any information system is known as data life cycle.
There are eight significant principles, which could be utilized to govern and protect
private and personal information. These principles are known as data protection principles.
viii) Cloud Computing: The utilization of network as well as servers for the purpose
of any operation in data through Internet connectivity is known as cloud computing (Chang,
2014).
Question 2
Data mining and text mining create significant value to any particular organization or
business. There are various ways to create value in any business by data and text mining. The
methods are given below:
i) Threat, Compliance and Risk Detection: Each and every industry should analyze
risk, threats and compliance in their organization for reducing the chance of data loss (Witten
et al., 2016). This type of threat mainly happens in all types of financial organizations and
thus text mining would be used there for the successful detection of problems related to
potential compliance. This even prevents any type of fraud activities within the company.

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ii) Engagement of Customers: CRM is the most important factor in any business and
this is solely managed by data mining and text mining with the help of various techniques
like processing of natural language (Wu et al., 2013).
iii) Better Business Decisions: The organizations get more accurate data or
information and is capable of taking better business decisions by data and also text mining.
Thus, data mining and text mining can easily create business value and importance.
Question 3
Cloud computing can be simply defined as the most utilized way to transfer all types
of information through the connection of Internet (Arora, Parashar & Transforming, 2013) .
Although, there are several advantages in cloud computing, few problems are present as well.
The major issues of cloud computing are given below:
i) Hacking
ii) Data Breaching
iii) Insider Threats
iv) Data Migration
v) Privacy and Security
The two significant examples of the cloud computing issues are insider threats and
data breaching.
i) Insider Threats: This is the most nefarious activity in cloud computing (Dinh et al.,
2013). The insider threat is very common in information technology companies and occurs
when the staffs or members or the employees of the organizations unintentionally or
deliberately causes major loss to the company by breaching its confidential data.

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The simple solution for this particular problem is proper training to the staffs or
employees of the company. This training is extremely significant for the personnel to
understand the security measures, clauses and importance with respect to cloud computing.
ii) Data Breaching: The second issue is data breaching (Hashem et al., 2015). In any
type of information system related to cloud, data is often lost and cannot be recovered. The
hackers or the intruders take the opportunity to breach the confidential data and thus cloud
sometimes become extremely threatening. Moreover, after data breaching, data recovery is
very tough and time consuming.
The best procedure to solve any problem of data breaching in cloud computing is
simply by securing the physical space and implementing various measures of safety.
Question 4
i) Reason To Design and Implement EIS: Executive Information System was solely
designed as well as implemented to properly provide every senior manager with several
internal and external data as well as even the KPIs or the key performance indicators. The
KPIs are absolutely relevant to the needs.
ii) Problems or Issues with EIS: The most important issue with the EIS was the half
of the data was properly available via it. The other major issue with the EIS was all the data,
which were needed was unavailable all the time. Furthermore, the SBUs reported revenue of
sales in different timeframes.
iii) Reasons for problems in EIS: The most significant reason for the failure of EIS
was the entire designing part of the architecture was never done for customized report and
rather was driven only by financial reporting. The complex user interface was the second
problem in EIS and thus it was impossible to review key performance indicators.

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iv) CIO Improvised EIS: A brand new EIS was designed and implemented by CIO of
this company and several policies for governance of data were implemented to standardize
data formats.
v) Advantages of the new IT Architecture: The main benefit of the new IT
architecture was that it was easy in modification of the reports and is also extremely faster.
vi) Advantages of Governance of Data: Data management and improvising data
quality are the two major advantages of data governance.
Question 5
i) Importance of POS Data Processing: POS data should be processed by Coca Cola
organization, as simple analysis is possible in this particular kind of data ad perfect support is
obtained for forecasting within supply chain.
ii) Attempt to Make Favourable Customer Experience: The organization of Coca
Cola creates favourable experience of customers with the help of various strategies such as
undertaking feedback from customers, creating vision of customer experience, knowing their
customers and many others
iii) Importance or Significance of Trusted Data: The main importance of trusted
viewing of data is that the data is extremely easily manageable and cost effective.
iv) Black Book Model: The main aim of this model was to reduce the complexities of
the decision model that is utilized for orange juice. Moreover, it helps in quantifying the
relation amongst the variables.

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v) Strategic Advantage or Benefit of the Black Book Model: This model mixes up
the data of about six hundred different flavours that involves orange making, customer
preferences, sweetness and acidity measurements, weather and many more.

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References
Arora, R., Parashar, A., & Transforming, C. C. I. (2013). Secure user data in cloud computing
using encryption algorithms. International journal of engineering research and
applications, 3(4), 1922-1926.
Chang, V. (2014). The business intelligence as a service in the cloud. Future Generation
Computer Systems, 37, 512-534.
Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013). A survey of mobile cloud computing:
architecture, applications, and approaches. Wireless communications and mobile
computing, 13(18), 1587-1611.
Fernando, N., Loke, S. W., & Rahayu, W. (2013). Mobile cloud computing: A survey. Future
generation computer systems, 29(1), 84-106.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The
rise of “big data” on cloud computing: Review and open research issues. Information
Systems, 47, 98-115.
Hutchings, A., Smith, R. G., & James, L. (2013). Cloud computing for small business:
Criminal and security threats and prevention measures. Trends and Issues in Crime
and Criminal Justice, (456), 1.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE
transactions on knowledge and data engineering, 26(1), 97-107.