Comprehensive Analysis: IT Infrastructure Management PG Report

This report provides a comprehensive overview of IT infrastructure management, covering various concepts and technologies. It begins with definitions of key terms such as electronic record management, business intelligence analytics, data mining, text mining, big data analytics, data discovery, enterprise architecture, management information system, data life cycle, and cloud computing. The report then explores the applications of data and text mining in analyzing customer behavior and market competition, highlighting their impact on business value. It further discusses the challenges associated with cloud computing, including data security concerns and vendor selection, along with potential solutions. The report also examines the failure of an Enterprise Information System (EIS) and the subsequent implementation of a new Enterprise Architecture (EA), detailing the benefits of data governance. Finally, the report analyzes the use of data in Coca-Cola's operations, including point-of-sale (POS) data and the Black Book model, illustrating how data analytics supports strategic business planning and enhances customer experience. The report concludes with a list of references used.

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

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IT INFRASTRUCTURE MANAGEMENT PG
Table of Contents
Question 1............................................................................................................................2
Question 2............................................................................................................................3
Question 3............................................................................................................................3
Question 4............................................................................................................................4
Question 5............................................................................................................................5
References............................................................................................................................7

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Question 1
 Electronic record management is a program or a set of programs that is designed
to store records digitally. Generally a software is used in creation and
maintenance of records.
 Business Intelligence analytics refers to use of different technologies, application
and practices for collection, integration, analysis and proper presentation of
business information (Chen, Chiang & Storey, 2012).
 Data mining deals with the examination of one or more pre existing database
gathering certain required information (Wu et al., 2014). Text mining on the other
hand refers to the process of derivation of quality information from text or
unstructured information.
 Big data analytics refers to the idea and strategy of investigating large and
different or varied types of data sets, which helps in uncovering the hidden
patterns and unknown correlations (Kambatla et al., ). Data discovery is related to
business analytics and deals with the collection of data from different databases.
 Enterprise architecture can be defined as a conceptual blueprint for defining the
structure and operations of a particular organization (Lapalme, 2012). It examines
how an organization will be able to achieve its goals, both current and future.
 Management information system can be defined as a computerized database for
organizing and managing the financial operations and information in an organized
manner (Laudon & Laudon, 2016). It is capable of producing regular reports
associated with the operations of the management in every level.

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 Data life cycle can be defined as a sequence of stages that gives an overview of a
data from its initial generation to its archival or deletion (Chen & Zhao, 2012).
There are six major stages in a data life cycle, which are generation or data
capture, maintenance, active use, publication, archiving and purging.
 Cloud computing is a process and use of a network based on remote that are
hosted over internet that helps in managing, storing and processing the data
instead of using a local server. It can be also termed as delivery of computing
services over internet (Dinh et al., 2013).
Question 2
Data and text mining helps in analyzing the customers’ behavior and market competition
from the set of collected data. This not only improves competitive advantage in business, but
also helps in identifying the latest trends in the market place (Witten, et al., 2016). Since text
mining deals with examination of unstructured data as well, it helps an organization in risk and
threat detection. Furthermore, it increases customer engagement and helps in taking better
business decisions, thus creating business value for the organization.
Question 3
There are certain issues that are applicable to cloud computing. The different risks are as
follows (Zissis & Lekkas, 2012)-
1. There is a high chance of data breach and unauthorized access to the customer and
business data stored over an insecure business network. Therefore data security concern is one of
the major problems associated with cloud computing.

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2. Cloud computing includes dependency on service providers. For ensuring
uninterrupted services, it is essential to acquire a vendor service. Selection of a proper vendor is
another major problem associated with cloud computing.
The problems associated with cloud computing can be solved by implementing proper
security mechanisms such as encryption and authentication to prevent breach of the data stored
in cloud computing (Arora, Parashar & Transforming, 2013). The problem associated with
vendor selection can however be mitigated by the maintaining a legal agreement between the
vendor and the client. The cloud computing vendor should be trusted enough to allow data access
(Jadeja & Modi, 2012).
Question 4
 The EIS was developed and implemented to equip the senior managers a proper
knowledge of data resources (internal and external) including the key
performance indicators (KPIs). The KPIs are associated with their specific
requirements.
 The EIS was a complete failure. The problems that executives found with the EIS
was that only half of the data provided by the EIS implemented was associated to
the decision making and analysis of the employees at the corporate level.
Furthermore, the data was not accessible or available when needed.
 The two main reasons of the problem are as follows-
1. The IT architecture of the EIS was not designed for customized reporting as it
was based on financial accounting rules.

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2. The user interface was quite complicated and therefore, the executives could
not easily review the KPI’s.
 In order to make the EIS more efficient, the CIO worked with the task force for
designing and implementing completely new EA. Proper policies of data
governance were implemented in order to standardize the different data formats.
 The benefits or advantages of the new IT architecture were that, its process was
more business driven instead of financial reporting and therefore it was easier to
make changes or modify. Furthermore, there was need of fewer IT resources for
the system maintenance.
 The main benefit of data governance is that it eliminates the costly and time
consuming ad hoc analysis.
Question 5
 POS captures data from retail channels and uses it to create customer profiles. In
Coca-Cola, huge volume of data is analyzed in order to make the departments
more and better time sensitive. POS data can be analyzed and can be used for
supporting the collaborative planning and forecasting and therefore, it is
important for Coca-cola to process the important data in real time.
 Coca-cola attempts to create favorable customers’ experience by implementing
data governance program. It further makes use of the Big data to understand the
need and preferences of the customers.
 Having a trusted view of the data helps in strategic business planning, which
further increases the profit margin of the company along with the enhancement of
the customers’ experience.

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 Black Book model brings together the detailed data of the 600+ flavors that is
needed for preparing the orange juice for creating a consistent taste. The model
helps in specifying the process of creating a consistent taste.
 The strategic benefit of Black book model is that it defines the specific
relationship between the variable that reduces the uncertainty associated with a
business process.

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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.
Chen, D., & Zhao, H. (2012, March). Data security and privacy protection issues in cloud
computing. In Computer Science and Electronics Engineering (ICCSEE), 2012
International Conference on (Vol. 1, pp. 647-651). IEEE.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big
data to big impact. MIS quarterly, 1165-1188.
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.
Jadeja, Y., & Modi, K. (2012, March). Cloud computing-concepts, architecture and challenges.
In Computing, Electronics and Electrical Technologies (ICCEET), 2012 International
Conference on (pp. 877-880). IEEE.
Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2014). Trends in big data analytics. Journal
of Parallel and Distributed Computing, 74(7), 2561-2573.
Lapalme, J. (2012). Three schools of thought on enterprise architecture. IT professional, 14(6),
37-43.
Laudon, K. C., & Laudon, J. P. (2016). Management information system. Pearson Education
India.

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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.
Zissis, D., & Lekkas, D. (2012). Addressing cloud computing security issues. Future Generation
computer systems, 28(3), 583-592.