IT Infrastructure Management

This article discusses various topics related to IT Infrastructure Management such as Electronic Records Management, Business Intelligence, Data and Text Mining, Cloud Computing, and more. It also covers how data and text mining creates business value, problems associated with cloud computing, and how data quality determines system success and failure. Additionally, it explores how Coca-Cola manages at the point that makes a difference.

Running Head: IT INFRASTRUCTURE MANAGEMENT 1
IT Infrastructure Management
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Course
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IT INFRASTRUCTURE MANAGEMENT 2
Question 1
 Electronic Records Management (ERM): This refers to the process of controlling or
handling records using electronic systems. It enhances availability and accessibility,
therefore; enabling management to access information such as financial data when the
need arises (McLeod, Childs & Hardiman, 2011).
 Business Intelligence (BI) and analytics: This can be defined as the process of gathering
data, and consequently, making important decisions through analyzing collected data
using technologies, therefore; increasing productivity. Through BI and analytics, some
information about performance are obtained, therefore; enabling an organization to
prepare for the future (Chen, Chiang & Storey, 2012).
 Data and Text Mining: This is referred to a process of analyzing unstructured data, and
consequently, obtaining useful information. The entire process is carried out using
statistical analysis, therefore; information about trends is identified (Linoff & Berry,
2011).
 Big data analytics and data discovery: Data discovery can be defined as a process of
consolidating large amount of data from several databases into one source, thus easing
evaluation. Big data analytics involves analyzing huge volumes of data aiming at
obtaining important information such as market trends. Apart from market trends,
customer preferences are also established. This can be achieved using statistical software
such as SPSS (Russom, 2011).
 Enterprise architecture: This is referred to as a blueprint that can be used to define some
organizational structures. It is made up of different components, for example,

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organizational and technological architecture, therefore; enabling companies to plan and
implement their systems effectively (Bernard, 2012).
 Management Information Systems: This can be defined as computer systems, which can
be used to manage different departments of an organization. These systems are
automated, therefore; generating reports regularly especially relating to finance. Also, it
enhances coordination between different departments (Laudon, K. C. & Laudon, J. P.,
2015).
 Data life cycle and data principles: Data life cycle is referred to as steps involved to
obtain information from unstructured data. Data principles are concerned with guidelines
that are supposed to be followed while using data within an organization such as
maintaining confidentiality (Malik, 2013).
 Cloud computing: This can be defined as the process of storing and managing data over
the internet. It uses some security concepts such as encryption, therefore; enhancing data
security. It’s provided by cloud service providers such as Amazon and Oracle (Armbrust
et al., 2010).
Question Two: How Data and Text Mining Creates Business Value
There exist different ways on which data and text mining creates business value. For
instance, detecting risks is one of the ways. Many companies are affected with issues related to
fraud. Besides, medical centers are susceptible to dangerous issues, therefore; data and text
mining detects risks prior to occurrence, and consequently, providing a warning thus enabling
management to take precautions. Apart from detection of risks, engaging customers also creates
business value. For instance, data and text mining involves collection of texts and data from
customers by using online platforms such as social media. A business value is in turn created as

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management can determine new products required by the customers. Moreover, data and text
mining enhance better decision-making, thus creating business value (Linoff & Berry, 2011).
Question Three: Problems that are Associated with Cloud Computing
Problems associated with cloud computing are issues that can corrupt data, which has
been stored. For instance, the following are some of them; difficulty in maintaining data
integrity, thus making it vulnerable to modifications and inaccessibility of data in case there is
low internet connection, therefore; affecting smooth running of business operations. However,
there are solutions for solving above problems. For instance, data encryption can solve issues
related to integrity. It conceals data, therefore; can’t be accessed by the third parties such as
hackers. Apart from data encryption, data backups can also solve cloud computing problems.
Therefore, data can be accessed and used incase the one stored by using cloud computing has
been corrupted (Armbrust et al., 2010).
Question Four: Data Quality Determines Systems Success and Failure
 Reason why EIS was designed and implemented: To provide top managers with relevant
internal and external data and key performance indicators (KPIs) depending on specific
needs.
 Problems that executives have with EIS: Data unavailability. For instance, required data
were not readily available when the need arises due to the following reasons; different
formats, varying time of reporting sales by SBUs such as daily and monthly and delays
for generating reports. Also, reviewing KPIs was difficult for executives as there were
more irrelevant data.

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 Reasons for EIS problems: Complexity in user interphase, hence unable to provide
information that was required directly instead executives had to obtain it from large
volume of irrelevant data and designing IT architecture by using rules in accordance with
financial accounting.
 How CIO improve EIS: By cooperating with dedicated team for development of new
enterprise architecture that was designed using customized reporting rather than using
rules related to financial accounting.
 Benefits of new IT architecture: Reduced time of analyzing reports, easy ways of
changing reports, improve data reliability, business driven rather than financial and less
resources for system maintenance.
 Benefits of data governance: Standardization of data formats and enhancement of data
consistency.
Question Five: Coca-Cola Manages at the Point That Makes a Difference
 Importance of processing POS data in near real time by Coca-Cola: To assist in
collaborative planning as well as forecasting since change is inevitable as far as business
and market trends are concerned. Also, there is analysis of large amounts of data to aid
making extremely important decisions regarding production.
 How does Coca-Cola attempt to create favorable customer experiences?
By using different methodologies, for example, data warehousing, data modelling, big
data and social media aiming at providing response to activities that are carried out by its
competitors. Apart from competitors’ activities, Coca-Cola also responds to some
changes in the market as well as customer preferences in real time.

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 Importance of trusted view of the data in Coca-Cola: To provide a response rapidly
concerning changes in market conditions.
 Definition of Black Book model: This is a decision model in relation to orange juice that
aims in maintaining taste of orange juice regardless of different qualities of oranges,
which are supplied.
 Strategic benefit of the Black Book model: Reducing uncertainties, combining data on
more than 600 flavors and specifying ways on which orange is blended, therefore;
ensuring that the corporation has maintained its customers, thus competing effectively
with its competitors.

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References
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., & Zaharia, M.
(2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
Bernard, S. A. (2012). An introduction to enterprise architecture. AuthorHouse.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big
data to big impact. MIS quarterly, 1165-1188.
Laudon, K. C., & Laudon, J. P. (2015). Management information systems (Vol. 8). Prentice Hall.
Linoff, G. S., & Berry, M. J. (2011). Data mining techniques: for marketing, sales, and customer
relationship management. John Wiley & Sons.
Malik, P. (2013). Governing big data: principles and practices. IBM Journal of Research and
Development, 57(3/4), 1-1.
McLeod, J., Childs, S., & Hardiman, R. (2011). Accelerating positive change in electronic
records management: headline findings from a major research project. Archives and
Manuscripts, 39(2), 66-94.
Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19(4), 1-34.