University Name: INFRASTRUCTURE MANAGEMENT OF IT Assignment Solution

This assignment solution addresses various aspects of IT Infrastructure Management. It begins by defining key concepts such as Electronic Records Management (ERM) systems, Business Intelligence (BI), Data mining, Data discovery, Enterprise Architecture (EA), Management Information Systems (MIS), Data Life Cycle Management, and Cloud Computing. The solution then explores the significance of data and text mining in research and commercial sectors, highlighting its value in extracting insights from data for informed decision-making. It also discusses the challenges associated with cloud computing, particularly concerning security measures. Further, the assignment analyzes the evolution and functionality of Executive Information Systems (EIS), including the problems faced by executives and the solutions implemented. Finally, it examines Coca-Cola's use of data analytics in its supply chain, customer experience, and the application of the Black Book model for product consistency, emphasizing the importance of data integrity and strategic benefits. The solution is a helpful resource for students studying IT infrastructure management.

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

1INFRASTRUCTURE MANAGEMENT OF IT
1. Question 1
1.1 - An Electronic Records Management (ERM) system could be defined as a form of
computer program that are mainly designed for tracking and storing of records (Svärd, 2013).
1.2 – Business Intelligence (BI) and Analytics could be defined as a broad set of
applications based on the analysis of data that would include querying and analytics, processing
of online analytics, enterprise reporting and various other functions.
1.3 – Data mining could be defined as the different processes of sorting of the larger sets
of data for the purpose of identification and establishing different kinds of relationships for the
purpose of solving problems with the help of data analysis. Hence data analysis could allow the
enterprises for predicting the future trends (Witten et al., 2016).
Text Mining is defined as the vast process of exploring and thus analyzing the large
forms of unstructured form of text data that would be aided with high quality software.
1.4 – Data discovery and big data analytics could be defined as the process of enabling of
iterative data that would be scalable that would be guided with the help of data mining and
advanced analytics (Gandomi & Haider, 2015).
1.5 – Enterprise Architecture (EA) could be defined as the conceptual form of blueprint
that would be able to provide a definition to the structure and the operations within the business
processes. The EA is meant for the determination the performance of an organization based on
current and future based objectives.
1.6 – A Management Information System (MIS) could be defined as a form of
computerized database based on organized financial information. It is programmed in such a

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manner for producing regular reports based on several operations for each of the level of
management (Laudon & Laudon, 2016).
1.7 – Data Life Cycle Management could be defined as an approach based on policy in
order to manage the flow of information throughout the life cycle of the data.
1.8 – Cloud Computing could be defined as a pool of services that would be sharable
within the configuration of resources within the computer system. These form of computing
services make use of storage databases and servers.
2. Question 2
The data and text mining are defined as the automated forms of techniques based on
analytics. This sector is becoming increasingly important for different forms of research sectors.
At the commercial level, the data and text mining provides several value for extracting value
from the gathered data, which could provide various value for businesses. Hence it allows
businesses for making proactive decisions based on vast form of knowledge (Mostafa, 2013).
3. Question 3
Some of the problems that are associated with the use of cloud computing are based on
security measures and standards. Storing of data without the lack of proper form of encryption or
the lack of multi-factor authentication in order to access information to services.
The problem of encryption could be solved with the implementation of proper form of
cryptographic standards within the cloud computing environment (Hashizume et al., 2013).

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4. Question 4
4.1 – An EIS system was mainly designed for the purpose of gathering and storing
information for producing specific forms of reports for workers. This system was integrated
within industries for producing an aggregation of reports based on managers. This system is able
to provide a tool for providing direct access to any kind of relevant information in a navigable
and useful format.
4.2 – The executives had discovered that half of their data that was generated from the
EIS was mainly irrelevant for the basis of corporate level of decisions that were concerned on
Strategic Business Units. Another form of problem was based on relevant data that was used for
crucial forms of decisions and which was not available in the exact manner as needed.
4.3 – The user interface was extremely complicated in order to retrieve information.
Another problem was that the architecture application was not properly designed for the
generation of customized report.
4.4 – The CIO had put a dedicated team for redesigning and thus redeveloping a newer
form of system for building a new form of architecture.
4.5 – The newer system would be able to provide reliable form of KPI reports based on
the inventory turns, profit margins and cycle times. Hence the EIS that was used by executives
was improved as they had got a newer form of reliable data based on the system.
4.6 – Data governance helps in the control over the data quality and thus helps in
providing a consistent and trusted form of data that could be depended upon by the businesses.
Data governance also helps in the consistency of data (Thompson, Ravindran & Nicosia, 2015).

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5. Question 5
5.1 – Coca-Cola supports forecasts, plans and various replenishment processes, which
would require POS data in real time. The analysis of data is extremely crucial for the supply
chain and for supporting critical decisions based on marketing and distribution of their products.
5.2 - Coca-Cola creates different types of favorable forms of rich customer experience
with the help of big data, data modeling, and data warehousing and social media in order to
respond to various forms of challenges and thus introducing newer products within the market.
They make use of these kinds of factors for adjusting with the preferences of customers and thus
prepare their products.
5.3 – The integrity of data is referred as the data quality. Having a trusted view of the
proper data would be helpful in ensuring the consistency and accuracy of the data. A trusted
view of vital data would be helpful for making vital business decisions and other purposes.
5.4 – The Black Book model is an algorithm, which comprises of detailed data regarding
the myriad flavors that would be preferable by the consumers. This model includes various forms
of sets of data such as satellite imagery, weather date, regional customer preference, pressures of
cost and many other factors (Anthony, 2017).
5.5 – The strategic benefits of the Black Book model is that they specify the processes of
blending of orange juice in order to create a consistent form of taste throughout the year. This
would also be meant for making the juice readily available at all parts of the world despite
having a peak season.

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6. References
Anthony, S. D. (2017). The Little Black Book of Innovation, With a New Preface: How It Works,
How to Do It. Harvard Business Review Press.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and
analytics. International Journal of Information Management, 35(2), 137-144.
Hashizume, K., Rosado, D. G., Fernández-Medina, E., & Fernandez, E. B. (2013). An analysis of
security issues for cloud computing. Journal of internet services and applications, 4(1),
5.
Laudon, K. C., & Laudon, J. P. (2016). Management information system. Pearson Education
India.
Mostafa, M. M. (2013). More than words: Social networks’ text mining for consumer brand
sentiments. Expert Systems with Applications, 40(10), 4241-4251.
Svärd, P. (2013). Enterprise Content Management and the Records Continuum Model as
strategies for long-term preservation of digital information. Records Management
Journal, 23(3), 159-176.
Thompson, N., Ravindran, R., & Nicosia, S. (2015). Government data does not mean data
governance: Lessons learned from a public sector application audit. Government
information quarterly, 32(3), 316-322.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann.