ITC540 IT Management: Case Study on AstraZeneca's Outsourcing Contract

This assignment includes a case study analyzing AstraZeneca's termination of a $1.4B outsourcing contract with IBM, identifying mistakes made by both companies and exploring the reasons for the contract's failure. It discusses the role of outsourcing contracts, the importance of Service Level Agreements (SLAs), and the preference for arbitration over lawsuits. Additionally, the assignment presents a report on database, data warehousing, and data mining, covering their applications, benefits, and techniques used in various industries. The report highlights the importance of data management, business intelligence, and decision-making processes supported by these technologies, emphasizing their role in organizational growth and competitiveness.

ITC540
IT Infrastructure Management
Assessment 3
Case study and IT Research
Student Name: Shubham Chauhan
Student ID: 11619922
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Contents
Task A..............................................................................................................................................3
Task B..............................................................................................................................................7
Task C............................................................................................................................................11
References......................................................................................................................................12
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Task A
AstraZeneca Terminates $1.4B Outsourcing
Contract with IBM is the provided case
study that needs to be analyzed and after the
study, various questions are raised which
needs to be answered by properly examining
the entire case thoroughly that is mentioned
below:
1. What mistakes did AstraZeneca
make?
AstraZeneca is the leading
biopharmaceutical company in the world
which concentrates on discovery,
development, and commercialization of
several prescribed medicine for almost 6
different healthcare areas. There are some
mistakes that are made by AstraZeneca
which are explained below:
 AstraZeneca has an MSA (Master
Service Agreement) that comprises of 32
schedules and 90 clauses which direct
several conditions of IT-based
infrastructure services.
 In case of analyzing different exit based
obligations applied on IBM in addition
to MSA termination is entirely
imprecise.
 AstraZeneca works with specifications
which entirely depend on outcome
achieved by dealing with IBM which
results to contract failure. The contract
though plans to utilize these
specifications in order to encourage
vendor based innovations and to imagine
a groundbreaking contract model which
at last results into great failure as
AstraZeneca business keeps on changing
very quickly whereas the contract based
design doesn’t comprise of such a pace.
2. What mistakes did IBM make?
IBM also made several mistakes which are
described below:
 IBM for almost 12 months has delivered
different services that are shared. IBM
deliver these services to AstraZeneca by
infrastructure utilization that is not
delivered to another supplier, not even
AstraZeneca itself.
 For many provisions related to services
of termination were proposed by IBM
that was rejected later by the court.
 For fees fixation provision after getting
IT transfer plans proposed by IBM that
was rejected later by the court.
3. Why are outsourcing contracts for
five or more years?
The contracts of outsourcing are nearly of 5
or more years or in other words, they are of
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long-term because of the difficulty they face
during the modifications and changes need
to be made so that the vendor can grab
maximum benefits or profit from the
provided deal. Several large outsourcing
deals facilitate vendors to grab major
investments in early two years because this
time allows services to get set and
customized and in next 3 or 2 years vendors
assume profit at its maximum making the
deals of outsourcing of more than 5 years or
more.
4. Why do you think two major
corporations could make such
mistakes?
The following mistakes described above
occur due to the discrepancy between two
companies in case they don’t approve of
several things:
 What is required to be found as
infrastructure shared by two companies?
 What is the established proposed length
that is exact for the assistance of
termination period?
 After getting IT transfer plans identify
whether the fixed fees provision is
conditional?
5. Do you think the 2007 SLA was
doomed to fail? Explain your answer.
Yes, I believe that 2007 SLA project
was completely doomed to its failure
because of following reasons:
 The 2007 SLA comprises of 32
schedules and 90 clauses which direct
several conditions of IT-based
infrastructure services in almost 60
defined countries.
 In case of analyzing different exit based
obligations applied on IBM in addition
to MSA termination is entirely
imprecise.
 AstraZeneca depends entirely on
different capabilities of IT and its R&D
as they both are crucial for it.
 AstraZeneca works with specifications
which entirely depend on outcome
achieved by dealing with IBM which
results to contract failure.AstraZeneca
business keeps on changing very quickly
whereas the contract based design
doesn’t comprise of such a pace.
6. What provisions in the 2001 SLAs
protect AstraZeneca and the vendors?
In 2001 SLA based provision is taking
place so as to protect AstraZeneca and
vendors that are explained below:
 SLA is designed for the protection of
both AstraZeneca and vendors along
with service providers. They only protect
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consumers when they play an
determined role actively in several
defined parameters.
 SLA used to protect and secure both
parties by spreading awareness among
them regarding its duties and when they
became liable to live up to provided
duties.
 SLA prevents strongly several
disruptions and dangers which occurs at
the time of cloud sourcing and
migration.
 Many provisional deals act as companies
protections in the case when various
terms are met even after the achievement
of arrangement termination.
 SLA never permits both AstraZeneca
and vendors to sign a deal without its
legal detailed review.
 SLA doesn’tcontain solution and cloud-
based template of a vendor that needs to
be entirely unique.
 If all vendors of SLA take their details
lightly this will prove that indicators
determine less vendor accountability.
 If migration of cloud is refused by the
vendor, improvement of SLA sourcing
and negotiation important point then that
vendor is not been deliberated.
7. Why would parties prefer to use an
arbitrator instead of filing a lawsuit in
court?
Parties normally prefer to apply
arbitrator except lawsuits because:
 To establish a positive and strong
relationship with the vendor during
their first selection company needs to
be very selective. If a company
chooses incorrect vendors that play a
role in deals then the failure chances
of that software, application, and
implementation are likely to fail.
These failures are caused due to
lawsuits and can’t be resolved by any
of the vendors.
 Arbitrator usability points to stability
but on a different side, instability is
occurred due to lawsuits.
 To decrease both technical conflicts
and interpersonal conflicts with
different vendors of IT and also
some requirements of business needs
deep research by vendors.
 It becomes important to put up
different questions depending on
vendor provided services and
product (Turban, Volonino, &
Wood, 2015).
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Task B
The selected topic is Database, Data
Warehouse, and Data Mining.
Abstract
The collection of gathered data or
information that is organized which can be
edited, accomplished and updated easily is
called Database. The data in the database is
managed in form of tables that have rows
and columns format. For database system
management data mining comes out to be a
crucial part to organize and control the flow
of information in any system. On regular
basis, data is transferred in large quantity
from several information industries and to
grant this flow of data a data mining
structure is required. Data mining is used to
extract data that is useful from the
information provided. For efficient and
effective use of data both small and big
organization implies the data mining
process. When the entire data of any big
organization is centralized is called as Data
Warehousing. This report contains various
techniques and tools for both data
warehousing and data mining along with its
industrial application and its benefits. It
determines process overview like the use of
data warehousing and data mining
particularly for managing data flow of an
organization (Connolly & Begg, 2005).
Index terms
The index terms of this project explain
database, data warehouse, and data mining.
It explains techniques used in data mining
and warehousing. It even explains the
applications of all three. It even describes
several key terms used in data mining
process. I have selected different topics that
examine infrastructure related to IT along
with several trending organization.
Introduction
The entire growth of an organization can be
described well-using a database, data
warehouse and data mining tools and
techniques. The information granted can be
effectively utilized and can lead to
improvement in the business process. By
gathering all data from the developed
database initiates the process of data mining
of the data selected. Through correct
planning, information is extracted and the
extracted data is used. The stored data is
later measured and examined keeping
requirements of the organization in point.
Subtopics and Supporting Arguments
Database
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The database is the collection of multiple
data or information which is properly
organized for retrieval of computer
information and for fast searching. The
collection of gathered data or information
that is organized which can be edited,
accomplished and updated easily is called
Database.The data in the database is
managed in form of tables that have rows
and columns format. During the new data
added in the former database, the previously
stored data gets deleted, expanded or either
updated according to users choice (Özsu &
Valduriez, 2011).
Data Warehousing
When the entire data of any big organization
is centralized is called as Data Warehousing.
The data or information here is called
centralized because the user can access it
from all locations of the entire organization.
For example, the information can be
transferred to several company branches just
by headquarters. The set of all warehouse
data is defined as non-volatile data form
which comprises of subject-oriented and
time variant delivery of information over
different organizational channels for
communication purpose. This warehousing
of data helps in the management of the
organization to take good and sound
decisions along with business value
enhancement (Inmon, 2005).
Data mining
This method comes out to be effective as it
determines the returns on investment that
identifies development based cost related to
needs of the project. The process here
initiates or starts different organizational
opportunities. It enables different companies
to promote their business to both domestic
and international consumers. Data mining
terminology is used to examine the data
usefulness, weaknesses of an organization
and deliver its solutions depending on
business growth. Several business solutions
comprise of better IT strategies planning,
development, using advertisement and
ultimately enhance operations related to
business organizations. The data gathered
during data mining process enables in
creating crucial and better decisions related
to business using various marketing based
policies (Hand, 2007).
Applications of Database
The database plays an important role in
storing the data and is used in almost all
organizations. Few of its applications are:
 Universities and colleges: Nowadays all
examinations are generally conducted
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online so entire student data is required
to be managed and maintained properly
through a database.
 Banking: Making transaction of money
through home or any other place is
possible only because all bank and
account details are recorded effectively
in an entire database created for
customers.
 Library management: Library contains
several books which become difficult for
a librarian to memorize so a database is
created keeping entire details of library
regarding books and their shelves.
 Railway reservation: To maintain all
records related to ticket booking, trains
arrival and departure time, and regularly
updated regarding the train delays are
managed using a database development
(Lee, Moon, Park, Kim, & Kim, 2008).
Applications of Data Warehousing
Data warehousing proves to be useful for
different organizations and defines many
applications that are:
 Service-based sectors: Warehousing of
data proves to be crucial in delivering
services in the sector of maintenance,
records of financial data, customer
outlining and resource management
along with human resources.
 Aims of Government: Various
warehouse data are used by the
Government to manage taxation,
examine them in a better way along with
examining policies of health and its
providers (Wu, Diamos, Wang,
Cadambi, Yalamanchili & Chakradhar,
2012).
Application of Data Mining
Data Mining is used in several organizations
like:
 Manufacturing: Several manufacturing
supply chains regularly demands to be
updated with its products and services.
Data mining then enables different
manufacturers to examine the desires of
the customer. It even analyzes recent
market IT assets based on statistics. It
also controls major information about
retailers, clients, and suppliers that are
regularly maintained.
 Communication: Through
communication, customer reviews can
be examined regarding the purchased
services or products. Business analyst
skills come out to be useful in several
cases. Data mining examines profits
financially made by the company.
 Insurance: Companies dealing with
insurance have a lot of data that is stored
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in their database including client
application, details of newly formed
accounts, changes in the current account
and other required information.
 Retail: Data mining methods are crucial
for retailers as it boosts up the
relationship of customers for better and
good results and reliable and effective
details for both retailers and customers.
The aim is to decrease the price for
provided services and products without
reducing the quality (Kohavi & Provost,
2001).
Used techniques in these processes
The concepts of data mining are used to
measure efficiency and effectiveness of big
data in a database system. So for better
description techniques
 Clustering: It permits the user to
classify different attributes in several
items in a way to arrange it in a certain
sequence according to its requirements.
It defines the management of data
structure with the database system. The
information flow in clustering approach
is non-volatile and reasonable.
 Classification: Classification of data is
performed according to the objects based
information collection in the system of
the database. It manages the data in
respect of several groups connected with
social entities and different single
entities.
 Association: This method describes the
mining of data for the correlation
between entities of items in the database.
The software techniques used while
accessing the database regarding the
flow of information that is detected for
better understanding regarding the
relationships among different items like
products and customers (Berkhin, 2006).
Conclusion
This report describes that data mining is
extremely more than running queries of data
that are both hard and complex and a certain
database stores it. The database should work
with data provided. It reformats the database
using software of SQL, or Hadoop. The
format of information needs gets identified
depending on technical analysis that needs
to be performed. Several techniques are used
for structure outlining and to fulfill the
information format.
This report describes the importance of
database, data warehousing, and data
mining. The report describes its usefulness
and applications along with techniques used.
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Task C
Turnitin Report Analysis
According to several similarity indexes few
bunches of question is answered
a). Are any of the bold, colored text
matches in my self-check report missing
in-text references?
No, such segments of text are defined in the
following report.
b). Do any of the bold, colored text
matches in my self-check report include
more than three words in a row copied
from the original source without
quotation marks?
In Turnitin document provided colored
matches are not found and neither they are
quoted. The information is accumulated
through internet which is properly marked
according to its quote and correctly
referenced through the analyses of Turnitin.
c). Do direct quotations take up more
than 10% of the essay?
The essay topic is Data Warehouse,
Database and Data Mining which is written
entirely by me taking internet source help in
own words, not any copied material.
d). Are any of the bold, colored text
matches in my originality report purely
coincidental?
These documents not consist of matches like
that and make it necessary to extract text or
data from a source of the internet which
need to be referenced correctly.
e). Do any of the short strings of matching
text indicate that my attempts at
paraphrasing were not completely
successful?
The following report of Turnitin defines
clearly that the text chosen gathered right
from the internet. I have paraphrased the
collected information in my words after
complete understanding.
f). Have I synthesized all of the sources’
ideas into my essay by introducing each
piece of source information with a signal
phrase and by adding my own comments
or interpretation to it in the following
sentence?
Depending on personal knowledge and
gained learning’s I have completed this
essay writing that comprises of self-thoughts
on basis of the database, data warehousing,
and data mining.
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References
 Turban, E., & Volonino, L. & Wood, G.
(2015). Information Technology for
Management: Digital strategies for
Insight, Action, and Sustainable
Performance, 10th edition. USA: John
Wiley and Sons.
 Özsu, M. T., & Valduriez, P.
(2011). Principles of distributed
database systems. Springer Science &
Business Media. Retrieved from
http://lib.sgu.edu.vn:84/dspace/bitstream
/TTHLDHSG/2980/1/Principles%20Of
%20Distributed%20Database
%20Systems%20%20-%20%20M.
%20Tamer%20Ozsu%2C%20Patrick
%20Valduriez.pdf
 Connolly, T. M., & Begg, C. E.
(2005). Database systems: a practical
approach to design, implementation, and
management. Pearson Education.
Retrieved from
https://pdfs.semanticscholar.org/31d1/b6
58345afd022ac3d187a26bb3bd3e3276e0
.pdf
 Inmon, W. H. (2005). Building the data
warehouse. John wiley & sons.
Retrieved from
http://inmoncif.com/inmoncif-old/www/l
ibrary/whiteprs/ttbuild.pdf
 Hand, D. J. (2007). Principles of data
mining. Drug safety, 30(7), 621-622.
Retrieved from
https://sites.google.com/site/riyuniza/MI
T-PrinciplesofDataMining.pdf
 Lee, S. W., Moon, B., Park, C., Kim, J.
M., & Kim, S. W. (2008, June). A case
for flash memory ssd in enterprise
database applications. In Proceedings of
the 2008 ACM SIGMOD international
conference on Management of data (pp.
1075-1086). ACM. Retrieved from
http://s1.downloadmienphi.net/file/down
loadfile2/169/1398648.pdf
 Wu, H., Diamos, G., Wang, J., Cadambi,
S., Yalamanchili, S., & Chakradhar, S.
(2012, May). Optimizing data
warehousing applications for GPUs
using kernel fusion/fission. In Parallel
and Distributed Processing Symposium
Workshops & PhD Forum (IPDPSW),
2012 IEEE 26th International (pp. 2433-
2442). IEEE. Retrieved from
http://gpuocelot.gatech.edu/wp-content/u
ploads/optimizing-data-warehousing-
applications-GPUs.pdf
 Kohavi, R., & Provost, F. (2001).
Applications of data mining to electronic
commerce. Data mining and knowledge
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discovery, 5(1-2), 5-10. Retrieved from
https://arxiv.org/pdf/cs/0010006
 Berkhin, P. (2006). A survey of
clustering data mining techniques.
In Grouping multidimensional data (pp.
25-71). Springer, Berlin, Heidelberg.
Retrieved from
https://pdfs.semanticscholar.org/26f1/78
dbb00630ce19cccb9840ea12dbe31801be
.pdf
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