BUS105 - Business Information System: Data Management Project Report

This report analyzes a business information system assignment focusing on the data life cycle and its application to a business problem. The assignment begins by defining the data life cycle phases: generation, collection, storage, visualization, analysis, and action. It then presents a business problem statement for a South Australian shop that sells personalized goods, emphasizing the need for accurate stock selection based on name popularity. The report explores storage options for a large group of files, comparing cloud storage and physical hard drives. Data visualization techniques, including tables and graphs, are used to compare name popularity trends between 1945 and 2005, revealing a decrease in popularity over time. The report includes recommendations for the shop manager, such as adjusting stock based on name popularity trends and considering customer data sources for future analysis. The report concludes with a bibliography of cited sources.

Business Information System
Name:
Subject Code: BUS105
Subject Name: Business Information System

Business Information System
Task 1: Data Life Cycle and Business Problem Statement
We are living in a world where data alone will not help an organization achieve its goals unless
someone puts meaning into it (Bauman 1998). Therefore, it has to go through a data life cycle
which involves the following phases;
Phase 1: Generation
This is where data developed through our daily activities. In other words, anything we do in life
becomes the source of new data (Rouse 2018).
Phase 2: Collection
This is where data is gathered together from the different sources in form of surveys, medical
records, financial records or any other forms (Bottcher 2018).
Phase 3: Storage
In this phase, data is kept at a place that is considered safe either on physical hard disks, file
folders, human memory or the cloud.
Phase 4: Visualization
This phase involves organizing data diagrammatically to help reveal the patterns and
relationships between data sets (SAS 2019).
Phase 5: Analysis
In this phase, useful meaning is derived from the patterns revealed during visualization.
Phase 6: Action
Depending on the results obtained during analysis, data is used for the benefit of the
organization.

Business Information System
Business Problem Statement
South Australian Shop buys personalized goods and its success depends on its ability to select
goods with the most popular names. There is great need for accurate selection of goods that will
sell.
Task 2: Storage Options for a Large Group of Files
The storage options include;
Cloud Storage
Cloud storage is a virtual space used to store information and it is accessed through the internet
(Wang et al. 2009). The physical storage facility is owned and managed by the cloud service
providers such as IBM, Amazon, and Microsoft. Wu et al. ( 2010). Believes that cloud storage
technology substantially reduces maintenance cost and hence allowing the organization to focus
on other important things
Physical Hard Drives
This is the regular physical storage on the physical hard drives on the computers and the servers.
Unlike cloud, the physical storage facility is maintained by the data owners (Singh, Korupolu
and Mohapatra 2008). An organization has to buy, set up and maintain the storage which makes
it costly.
Task 3: Visualization
Sample Male Names
1945 2005
Names Amount(frequency
)
Position Names Amount(frequency
)
Position
John 545 1 JACK 198 1
Robert 465 2 JOSHUA 166 2
Peter 397 3 THOMAS 152 3
David 269 4 LACHLAN 150 4
Trevor 244 5 ETHAN 134 5
Graham 203 6 WILLIAM 134 5

Business Information System
BRIAN 192 7 RYAN 128 7
IAN 164 8 JAMES 125 8
BARRY 156 9 LIAM 115 9
WILLIAM 141 10 COOPER 112 10
KEVIN 125 11 SAMUEL 110 11
MICHAEL 125 11 RILEY 108 12
RICHARD 125 11 JACOB 95 13
MALCOLM 121 14 DANIEL 94 14
BRIAN 192 15 BENJAMIN 93 15
The above tables show the comparison of the number of names between the name records for the
year 2005 and 1945 (South Australian Government Data Directory, Attorney General’s
Department, 2013).
From the above analysis we can see that there is total decrease of the names as the years goes by.
For instance, there numbers of names recorded in 2005 are less than those recorded in 1945 for
all sampled female and male names. Additionally, there is a total drop in the rank in all the
sampled records (South Australian Government Data Directory, Attorney General’s Department,
2013).

Business Information System
EMILY
CHLOE
ELLA
CHARLOTTE
OLIVIA
JESSICA
ISABELLA
LILY
MIA
SOPHIE
JASMINE
AMELIA
CAITLIN
GEORGIA
HANNAH
0 20 40 60 80 100 120 140 160
Frequency 0f the top Female names in 2005
From the above charts Margaret Hellen and Judith were at the top in popularity in 1945. In 2005,
all the three names did not appear among the most popular names. It is a clear indication that a
name loses popularity as time goes by.
John Robert Peter David Trevor Graham
0
100
200
300
400
500
600
Comparison of the Frequency of The Male Names
1945 Vs 2005
Amount in 1945 Amount in 2005
The above bar graph shows comparison of the frequency of the sample male names in 1945
versus 2005. We can see that there is a total decrease and some close to extinction.

Business Information System
Task 4:
Year Margaret Helen Judith Christin
e
Year John Robert Peter David
1945 322 252 184 180 1945 545 465 397 272
1955 196 206 170 262 1955 397 414 619 455
1965 52 113 42 113 1965 308 273 440 643
1975 5 47 9 38 1975 114 132 144 306
1985 5 21 3 17 1985 91 114 99 249
1995 4 4 1 8 1995 58 36 53 88
2005 1 0 0 4 2005 19 16 22 21
Margaret Helen Judith Christine
0
50
100
150
200
250
300
350
Graph Visualizing The Change Of Popularity Of The Female
Names From 1945 To 2005
Amount in 1945 Amount in 1955 Amount in 1965 Amount in 1975
Amount in 1985 Amount in 1995 Amount in 2005

Business Information System
From the analysis above, there is a constant decrease in the popularity of the first 3 names;
Margaret, Helen and Judith. There is increase in popularity for the name ‘Christine’ in 1955 and
a constant decrease since then (South Australian Government Data Directory, Attorney General’s
Department, 2013).
Among of male names, there is a constant decrease in popularity for the first two names; John
and Robert. There is an increase in popularity of the name ‘Peter’ and a continuous decrease
from the next year. The popularity of the name ‘David’ increased in two years. It increased in
1955 and further increased in 1965 but decreased from the next year (South Australian
Government Data Directory, Attorney General’s Department, 2013). However, there is
substantial decrease in popularity for the most popular names over the years.
Other Analysis that could be performed
1. Analysis of the least popular names
2. Analysis of the female against male names to establish the most popular.
Task 5: Recommendation on the buying of stock by South Australian Shop
1. The manager should decrease the stock of the products with the most popular names over
the years since popularity has been observed to eventually decrease.
2. The manager should determine the products that are mostly bought by either females or
male. This is to ensure the products are bought for the right projected population.
John Robert Peter David
0
100
200
300
400
500
600
700
Graph Visualizing the Change of Popularity of the Male Names
From 1945 to 2005
Amount in 1945 Amount in 1955 Amount in 1965 Amount in 1975 Amount in 1985
Amount in 1995 Amount in 2005

Business Information System
3. The manager should watch out for the extinction of the names especially among the
females. (South Australian Government Data Directory, Attorney General’s Department,
2013) Extinction was not observed in the male names.
Task 6: Sources of Customer Data That South Australian Shop May Require For Future
Analysis.
1. Social Media User accounts records for example Facebook, twitter and Instagram
2. Schools Student Names Records
3. Hospital Birth Records.

Business Information System
Bibliography
Baumann, H. (1998) Life Cycle Assessment and Decision Making — Theories and Practices.
Ph.D thesis, Chalmers University of Technology, Gothenburg, Sweden
Bottcher Visuals (2018) Understanding the Data Life Cycle with DataBrew, Online Video,
Available From: https://www.youtube.com/watch?v=5I2bYqeFQy4 [Accessed 10 May 2019]
Rouse, M. (2019) ‘Data Life Cycle’, Available From:
https://whatis.techtarget.com/definition/data-life-cycle [Accessed 11 May 2019]
SAS (2019) Data Visualization, Available From:
https://www.sas.com/en_us/insights/big-data/data-visualization.html [Accessed 11 May 2019]
Singh, A, Korupolu, M, and Mohapatra, D. (2008) Server Storage Virtualization: integration and
load balancing in data centers, IEEE Press Piscataway, NJ, USA.
South Australian Government Data Directory, Attorney General’s Department (2013) Popular
Baby Names [online], Available from: https://data.sa.gov.au/data/dataset/popular-baby-names
[Accessed 10 May 2019]
Wang, C., Wang, Q., Ren, K., and Luo, W. (2009) Enabling public verifiability and data
dynamics for storage security in cloud computing, Saint Malo, France.
Wu, J., Ping, L., Ge, X., Wang, Y. and Fu, J. (2010) Cloud Storage as the Infrastructure of
Cloud Computing, IEEE International conference on Communication, Kuala Lumpur, Malaysia