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MIS770A Foundation Skills In Data Analysis
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MIS770A Foundation Skills In Data Analysis
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Course Code: MIS770A
University: Deakin Business School
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Country: Australia
Question:
Part A: Develop Evaluation Criteria
Your evaluation criteria need to be objective, in that they stipulate the rules you would use in establishing whether a Chart, Table or Graph utilised in your selected Report, is appropriate for the data being presented. You need to make sure that each of your criterion is written in such a manner that a reasonable person can readily understand, or appreciate, the message being conveyed. You are expected to address approximately 20 criteria.
You should also group your criteria by visualisation type. For example, you might choose “Common”, “Chart”, “Table” and “Graph”, or some other suitable grouping. You are not restricted to these specific headings. It’s also important that you number each separate criterion as you will need to refer to its specific number when undertaking Task 2.An example of an appropriate criterion:
Line Charts should be used when analysing trends over time. To be effective, the time series should be placed on the x?axis with the measure on the y?axis.An example of an unacceptable criterion:Line Charts should have nice colours. This example fails on two counts: it has an inadequate description (or explanation); and, it is subjective (i.e. the authors opinion), whereas it should be objective.
Part B: Report Evaluation
Select 12 to 15 diverse Charts, Tables or Graphs from your chosen Report (but not including any “Infographics”) and evaluate these visualisations using your criterion developed in Part A. It would not be unusual for an individual visualisation from your report to refer to multiple criteria.Further, any given visualisation you select may conform to some of your criterion but be in contravention of others, and this would need to be reported.
Answer:
Introduction
One of the leading financial institutions in Australia is the Commonwealth Bank. This institution was founded under the Commonwealth Bank Act in 1911 and begun its operations officially in 1912. The bank’s mandate is to conduct general banking business and savings. Currently, the bank has over 800,000 shareholders and has employed 52,000 people. In this report, I will focus on developing a set of criteria in coming up with data representation techniques. I will also show how the criteria developed has been applied in Commonwealth Bank of Australia. I will achieve this by comparing the data presentation techniques with those of the Commonwealth Bank.
Part A: developing evaluation criteria
My evaluation criteria will cover bar graphs, pie charts, and tables. The following are the criteria for each.
Bar graphs
Bar graphs are used for displaying categorical and qualitative data(Katal, Wazid, and Goudar, 2013, pp.406). They can also be used to display quantitative data in situations whereby the data set is small.
Bar graphs can be presented both vertically and horizontally. There should be a gap between the bars.
The graph should have two axes(Grimmer and Stewart, 2013, pp.272). The horizontal line is the x-axis and the vertical line is the y-axis.
The values of the y-axis more often than not begin with 0 at the bottom on the left side of the graph, moving upwards(Wang, et al., 2013, pp.300). The axis is labeled with the data they represent.
The data in the bar graphs should be arranged in a meaningful way, from greatest to the least value. The data placed should only be that which can be compared.
The graph should have a rich data comparison. The difference between the variables should be well outlined.
The x-axis and the y-axis should each be labeled with the data they represent.
Pie charts
A pie chart is a circle which is divided into slices. Each slice should represent part of the whole circle.
The slices in the pie chart should be sorted from the largest to the least(Lewis, 2015, pp.480). The left side of the largest slice should begin at the top, at 0 degrees.
The area of each slice should represent relative size(Lazer, et al., 2014, pp.1204). It is better to display the values in terms of percentage in relation to the whole degree of the circle which is 360 degrees
The properties of the data should be first considered when using the pie chart(Yang, Wang, and Sun, 2015, pp.25). Pie charts are excellent at comparing categories in the pie slices especially when the categories are near 25 or 50 percent.
For visual appearance, each slice in a pie chart may be colored differently and a key developed to explain the colors in terms of the values.
Pie charts should be used for a few variables (Shuman, et al., 2013, pp.95). If the slices are more than seven, it becomes difficult to read and interpret.
. Pie charts only represent data with positive values(Kuang, et al., 2014, pp.287). Negative values cannot be captured in the slices.
Labeling of the smaller slices should be done outside of the whole circle, especially if the labels are long.
Tables
Tables should be organized in rows and columns (Sandryhaila and Moura, 2014, pp.88). They can also accompany other data representation techniques, for example, the graph.
A good table should display data in a way that is easy to look things up. It should capture all aspects of the values.
In a table, units should be included in the labels when dealing with numerical data.
The data in a table should be formatted consistently to compliment with the units.
In grouping data in classes, the range must be well defined(Engström, et al., 2013, pp.1185). The classes should be at least 5 and not more than 15.
Company
Change
P/E (TTM)
Suncorp Group Ltd.
+0.95%
17.10
Australia & New Zealand Banking Group Ltd.
+1.16%
11.29
National Australia Bank Ltd.
+1.05%
13.22
Westpac Banking Corp.
+1.23%
11.17
The table shows the competitors of the Commonwealth Bank of Australia. The table has been arranged in rows and column as per criterion number 16. The unit of percentage has been used under the label change. This correlates with the criterion number 18. However, the table fails to capture all aspects of the values as required by criterion number 17. This is because the labels have not been properly explained. The change label should have been compared directly to the CBA to show the difference with the other banks.
The graph show dividend through the years from 2007 to 2015. The graph fails to follow criterion number 2 as it does not have the y-axis outlined. The labels are not outlined and explained which contradicts the criterion number 7. However, the values can be well compared through the years showing the difference between the dividend values. This affirms the criterion number 6 of the bar graphs. The graph also shows a good comparison between the interim and final percentage of the results. The company has placed the data which can only be compared and therefore, correlates with criterion point number 5.
The pie chart shows the market share of the different banks in Australia. The pie chart has used colors to differentiate between the values, as explained by criterion number 12. The values are also 5 in number making it easier to compare them. This has been addressed under criterion number 13. The values are presented as a percentage of the whole, therefore making it easy for observers to deduce comparisons. This is in line with the point number 10 of part A. the values of the pie chart are positive and this supports the criterion number 14.
The pie chart considers the criterion number 15 which stipulates that the labels should be made outside of the whole circle. The chart, however, fails to consider criterion number 11 which entails that the value should be near to 25 percent. Some of the values are very minimal and would have been best compared with a bar graph. The slices, however, present the parts of the circle as proposed by the criterion number 8, in representing values in regards to the whole. The pie chart also fails to consider point number 9 which stipulates that the values should be arranged from the largest to the smallest.
The above bar graph has applied the following points in relations to the criteria discussed. The y-axis has its lowest value starting at 0 as explained by criterion number 4. The values in each axis have been labeled in percentage and bank names respectively. This has been discussed in criterion number 7. There are gaps between the bars differentiating the values of each bank. This helps in giving the bar graphs a presentable appeal to the readers. The gaps between the bars have been discussed in criterion number 2. The data presented is qualitative as explained also in criterion number 1.
CBA BANK 62.7%
RESERVE BANK
OTHERS
Unallocated
(Commbank.com.au, 2018)
13.8%
21.2%
2.3%
In the above chart, the values have been well represented in percentage. This has been explained well by criterion number 10. The labeling of the chart has been done outside the chart with a definite key, explaining the color each value stands for. Criterion number 15 supports this by stating that labeling should be done outside the circle especially in a situation where the labels are long. The area of the slices has been well articulated in terms of relative size. The value from the largest to the smallest can be well compared in the chart. This account has been discussed in criterion number 10.
The above pie chart has no definite x-axis or y-axis. This contravenes criterion number 3 of part A. The classes have also not been arranged well for comparison. The bars are supposed to be arranged vertically, with each bar standing independently, and not collective as one bar. This is well explained in criterion 6. The comparison is therefore not well outlined. The graph also fails to give significant labels for the values as explained by criterion 7. The y-axis has not been labeled as proposed by criterion 7. The graph fails to display the range between the least value, 0, and the highest value. This is captured in criterion number 4.
The values of the y-axis and x-axis have been labeled as explained in criterion number 7. The gaps between individual bars have been well considered making it easy for comparison of the bars. The least value of the y-axis is 0. This has been addressed in the criterion number 4. The key to the graph properly explains the color represented by the variables. Time value has also been properly situated at the x-axis. However, ambiguity arises from the values situated on the right side of the vertical axis. The values of the new market share and new business premiums do not correlate.
Segment Name Revenue Profits Assets
Australia
2,455,900
N/A
1,062,600
Other
150,300
N/A
61,200
Unallocated
62,000
188,800
1,014,100
Total Revenue
2,668,200
188,800
2,137,900
Balance Date: 31 March, 2018
Profit Definition: Profit from Ordinary Activities Before Tax
The above table has been well labeled with the values having their rows and columns as explained in criterion number 16. The information displayed is easily understandable and the aspects have been captured. This is in line with the criterion explained in number 17. The units of the numerical value are also included in the labels making it easier for comparison. This has been noted in criterion number 18. The table has also computed all the values as per the labels making it much easier for the reader to deduce information. However, the relationship between the variables has not been emphasized.
Notes, coins and cash at banks (1)
17,002 5,895
14,836 8,281
15,586 5,765
12,782 8,167
Money at short call
Securities purchased under agreements to resell
13,520
22,733
12,230
21,865
Total cash and liquid assets
36,417
45,850
33,581
42,814
The above data presentation table focuses on explaining the cash and liquid assets of the Commonwealth Bank of Australia in the year 2018. The table has well-defined rows and column as it is the case of criterion number 16. For effective comparisons, the rows and columns have been labeled to enable the reader to understand the data they represent. The table conforms with criterion 19 due to the presence of the units in terms of dollars. This table, therefore, gives first-hand information to the reader and can be easily compared. It is flexible enough for additional information to be included.
The pie chart above has solid colors with each representing the variables. This is in line with the criterion explained in number 12. The values have also been labeled outside the chart and a key developed to explain the values as captured in criterion 15. The number of slices met the requirements of the criterion number 13, making it easier for comparisons and understanding the values. The circle is divided into slices representing part of the whole. Each slice has a significant value. This has been captured in criterion 8. The pie chart has considered the properties of the data and presented the data effectively.
The use of the pie chart in the above diagram is effective because the comparison is between a few variables. This gives the charts the visual appeal and enables the viewer to easily interpret the values. The properties of the values have been well considered in each chart as it is the case with criterion 11. The data values are positive as explained in criterion 14. Negative values cannot be captured in a pie chart presentation. The chart, however, does not observe criterion 9 which states that the values should be arranged from the largest to the least value.
Conclusion.
Data presentation tools ensure that information is understood by the viewers. The viewers can easily grasp the information when such data is presented visually and diagrammatically. The foregone is the criteria of developing an effective data presentation tools and ways of evaluating the techniques used in the diagrams to pass information. Without such data presentation tools, information is meaningless.
Bibliography
Commbank.com.au. (2018). [online] Available at: https://www.commbank.com.au/content/dam/commbank/about-us/shareholders/pdfs/results/FY15/30-june-2015-basel-III-pillar-3-asx.pdf [Accessed 30 Oct. 2018].
Commbank.com.au. (2018). [online] Available at: https://www.commbank.com.au/content/dam/commbank-assets/about-us/2018-08/CBA-Assessed-Emissions-Report-FY17.pdf [Accessed 29 Oct. 2018].
Commbank.com.au. (2018). Presentation and speeches – CommBank. [online] Available at: https://www.commbank.com.au/about-us/investors/presentation-speeches.html [Accessed 29 Oct. 2018].
Engström, P.G., Steijger, T., Sipos, B., Grant, G.R., Kahles, A., Alioto, T., Behr, J., Bertone, P., Bohnert, R., Campagna, D. and Davis, C.A., 2013. Systematic evaluation of spliced alignment programs for RNA-seq data. Nature methods, 10(12), p.1185.
Google.com. (2018). commonwealth Bank of australia annual reports images – Google Search. [online] Available at: https://www.google.com/search?q=commonwealth+Bank+of+australia+annual+reports+images&tbm=isch&tbo=u&source=univ&sa=X&ved=2ahUKEwi86_OchKzeAhXHwAIHHc-MCdgQ7Al6BAgAEA8&biw=1455&bih=707#imgdii=VqaBVzkzF5b4VM:&imgrc=6Yy2sPpSnYQ56M: [Accessed 29 Oct. 2018].
Grimmer, J. and Stewart, B.M., 2013. Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political analysis, 21(3), pp.267-297.
Katal, A., Wazid, M. and Goudar, R.H., 2013, August. Big data: issues, challenges, tools and good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference on , 12(2), (pp. 404-409). IEEE.
Kuang, L., Hao, F., Yang, L.T., Lin, M., Luo, C. and Min, G., 2014. A tensor-based approach for big data representation and dimensionality reduction. IEEE transactions on emerging topics in computing, 2(3), pp.280-291.
Lazer, D., Kennedy, R., King, G. and Vespignani, A., 2014. The parable of Google Flu: traps in big data analysis. Science, 343(6176), pp.1203-1205.
Lewis, S., 2015. Qualitative inquiry and research design: Choosing among five approaches. Health promotion practice, 16(4), pp.473-475.
Quotes.wsj.com. (2018). CBA.AU Stock Price & News – Commonwealth Bank of Australia – Wall Street Journal. [online] Available at: https://quotes.wsj.com/AU/XASX/CBA [Accessed 29 Oct. 2018].
Sandryhaila, A. and Moura, J.M., 2014. Big data analysis with signal processing on graphs: Representation and processing of massive data sets with irregular structure. IEEE Signal Processing Magazine, 31(5), pp.80-90.
Sec.gov. (2018). COMMONWEALTH BANK OF AUSTRALIA. [online] Available at: https://www.sec.gov/Archives/edgar/data/8565/000095012303008662/y88760ke6vk.htm [Accessed 29 Oct. 2018].
Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A. and Vandergheynst, P., 2013. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Processing Magazine, 30(3), pp.83-98.
Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P. and Keogh, E., 2013. Experimental comparison of representation methods and distance measures for time series data. Data Mining and Knowledge Discovery, 26(2), pp.275-309.
Yang, W., Wang, Z. and Sun, C., 2015. A collaborative representation based projections method for feature extraction. Pattern Recognition, 48(1), pp.20-27.
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