IT Infrastructure Management Assignment

The assignment provides definitions for various IT infrastructure management concepts, including 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 principle, and cloud computing. It also includes case studies on how data quality can determine success and failure, and a study on Coca-Cola's use of technology to manage complex data for decision-making.

Running head: IT INFRASTRUCTURE MANAGEMENT
IT INFRASTRUCTURE MANAGEMENT
Name of Student
Name of University
Author note

1IT INFRASTRUCTURE MANAGEMENT
Question 1: Definitions
 Electronic Records Management (ERM) – ERM can be defined as the set of specific
activities necessary for controlling the various business activities such as distribution,
maintenance, creation and record disposition in a systematic manner (Laudon & Laudon,
2016).
 Business Intelligence (BI) and analytics – The technological approaches that are
undertook to analysis the business information is known as BI (IşıK, Jones & Sidorova,
2013). It serves the purpose of critical decision making by critically analyzing the all the
business factors.
 Data and text mining – Data mining can be defined as the technology used to gain
information from structured data such as databases and text mining can be defined as the
technology used to gain information from unstructured data such as articles, websites,
blogs.
 Big data analytics and data recovery – Data discovery can be referred to as the business
analytics tool which allow the business persons to explore the more about the big data
findings and data analytics can be referred to as the process with the help of which data
can be examined which in turn is helpful in drawing conclusion for the business.
 Enterprise architecture (EA) – EA is defined as the set of well-defined approaches with
the incorporation of which the proceedings such as design, planning and execution of a
project are analyzed.
 Management information system (MIS) - It is a database which stores information
regarding related to finance. The information is programmed in such a manner that it can
produce the reports regarding each level of management operation regularly.

2IT INFRASTRUCTURE MANAGEMENT
 Data life cycle and data principle – It is a set of well-defined stages which a unit of data
has to encounter from the initial generation till it gets deleted.
 Cloud computing – It can be defined as the number of computing services such as
networking, storage, analytics which are delivered over the internet (Pearson, 2013).
Question 2: Data and text mining in business value
Data and text mining is of immense importance for the business point of view. In order to
perform proper business analysis, analysts require accurate data which in turn will help them in
providing more appropriate insights (Witten et al., 2016). Thus, incorporation of the data and
text mining is essential for quality decision making. Text mining can help to forecast the various
compliance issues, thus reducing the chances of malpractices or fraud.
Question 3: Problems related to cloud computing
 The following are the problems in relation to the cloud computing:
 Cloud computing is exposed to the threats of the security. Cloud server stores
huge amount of data over the internet. Thus, there might rise situation where the
hackers get access to those data (Rittinghouse & Ransome, 2016).
 Although data can be accessed in any point of time from the cloud but there lies
equal chances that the system might suffer from technical dysfunctions (Apostu et
al., 2013).
 Examples- flooding attack, insider attack
 Possible solution includes
 Cloud encryption
 Not sharing the encryption keys with anyone,

3IT INFRASTRUCTURE MANAGEMENT
 Reducing the access points
Question 4: Case study on how data quality can determine success and failure
 EIS was designed to keep the senior management team well updated regarding the
internal as well as the external data and the performance indicators that are relevant
considering their requirements are concerned.
 As per the executive only 50 % of the data derived from the EIS was relevant in context
of their decision making. Most of the required data were not present. Apart from this, the
derived data were not in the required format.
 The two reason which led to the EIS failures are as follows:
 The architecture was not sufficient to perform customized reporting.
Reports regarding the unplanned data require a flexible format (Guptill &
Morrison, 2013). Considering the case background, the executives could
not generate the non financial reports such as the comparison of the
turnovers of the products.
 The user interface was designed in a complicated manner. The KPIs that
the report generated were not easily understood instead the executives have
to sort the data from the list of irrelevant data, which in turn delayed their
service building up cost.
 In order to improve the existing EIS structure, the CIO implemented a newly developed
EA. Policies regarding data governance were also implemented in the process to
standardize the formats.
 The new IT architecture was business-oriented unlike that of the previous report-oriented
structure. The consequence of this new architecture has turned out to be beneficial. This

4IT INFRASTRUCTURE MANAGEMENT
architecture made report modification much simpler, thus eliminating time-consuming
process of data analysis.
 Data governance helped in the standardization of the data formats thus eliminating the
inconsistencies of the data; this in turn increased the reliability of the KPI reports.
Question 5: Case study of Coca-Cola
 Considering the number of sales and profits that Coca cola gains it is essential for the
company to handle such huge amount of data in real time. Disruption in maintain the data
would cause delays of the sales report.
 Coca cola has been using improved technology along with business ethics and
sustainability for focusing on the customer’s needs and requirements and it helps in
creating the favorable customer experience for the company (Habib & Aslam, 2014). The
implication of the data management strategy would help in entrusting the deployment of
the safe working environment, healthy living, and supporting active operations.
 The trusted view of data would help Coca cola for developing improved strategy
development for the company. The data management would also help centralizing the
data storage in single platform for making it accessible from varied places. The searches
have become faster for the customers as it does not need to search various resources and
it had helped in improving the integrity of the data storage.
 The black book is a decision model that combines the detailed data of a number of flavors
for Coca cola for calculating the orange, customer preference, weather, cost pressures,
crop yields, sweetness or acidity of the drink. Coke’s black book juice is the most
complex type of business analytics that requires evaluating 1 quintillion variable for
decision making.

5IT INFRASTRUCTURE MANAGEMENT
 The black book combines with big data for providing Coca cola with the benefit of
overcoming the problems of disruptions during supplying the products to the customers.
The implication of the models would help in overcoming even the issue of re-planning of
the business strategies even in case of emergencies. Doug Bippert had pointes that they
can re-plan all their strategies in 5-10 minutes of time with the help of their technology
and black book.

6IT INFRASTRUCTURE MANAGEMENT
References
Apostu, A., Puican, F., Ularu, G. E. A. N. I. N. A., Suciu, G., & Todoran, G. (2013). Study on
advantages and disadvantages of Cloud Computing–the advantages of Telemetry
Applications in the Cloud. Recent Advances in Applied Computer Science and Digital
Services, 118.
Guptill, S. C., & Morrison, J. L. (Eds.). (2013). Elements of spatial data quality. Elsevier.
Habib, S., & Aslam, S. (2014). Influence of brand loyalty on consumer repurchase intentions of
Coca-Cola. European Journal of Business and Management, 6(14), 168-174.
IşıK, Ö., Jones, M. C., & Sidorova, A. (2013). Business intelligence success: The roles of BI
capabilities and decision environments. Information & Management, 50(1), 13-23.
Laudon, K. C., & Laudon, J. P. (2016). Management information system. Pearson Education
India.
Pearson, S. (2013). Privacy, security and trust in cloud computing. In Privacy and Security for
Cloud Computing (pp. 3-42). Springer, London.
Rittinghouse, J. W., & Ransome, J. F. (2016). Cloud computing: implementation, management,
and security. CRC press.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann.