The current assignment is based on the analysing the elements of graphical depiction of the quantitative data that assist in offering sustain to the skills of the researcher. The graphical representation is not only concerned with helping the illustration of concise and clear vision it is also engaged in evaluating the notions of multifaceted data in report. The depiction can be of different types namely, bar charts, pie charts, column charts, line charts graphical depiction with tabular and picture based representation of data (Weissgerber et al. 2015). Furthermore, it is vital in determining the effectiveness of the visual reflections which ultimately helps in communicating with the audience whereas enhancing the understanding as well. The report is also concerned with the assessment of what needs to be done and what does not needs to be done in the visual depiction of the annual report of Telstra in order to demonstrate the practical implications.
The criteria for evaluation can be established unless the visual representation provides information and provides sufficient implication to the statistics highlighted with no attraction concerning stakeholders in monetary reports. Consequently, a series of criteria is developed to express the foundations upon which analysis of visual representation can be applied;
Positive and negative bars:
The below stated figure reflects the amount of revenue earned by the BHP Billiton from its operations. All the bars are reflects both the quantitative and qualitative data. Under the given case both the positive and negative values is illustrated in efficient manner. The position of the charts is reflected effectively (Stone et al. 2016). It can be depicted that the variables and figures are not coinciding with each other. The below stated bar is the correct example of negative and positive bar chart.
Bar and column Chart:
The below stated figure represents the earnings before interest and tax derived by the BHP Billiton. The graphical representation of bars is in discrete nature for the amount earned during a financial year by BHP Billiton (Song et al. 2015). The criteria in the charts are represented separately and they are presented in different colour reflecting an effective visualisation.
Relationship information:
The below stated graph portrays the relationship of two variables with the amount of social investment in the last five financial year. In the below stated figure, it can be understood that both the lines of chart and bar graph has been efficiently used (Thorvaldsdóttir, Robinson and Mesirov 2013). Here, the total amount of social investment is represented in the X axis and growth has been depicted in the Y axis and other variable is represented in the secondary Y axis.
This fulfils the criteria 2, which state the relationship of more than two variables;
Multiple sets of Data and Labelling:
The below stated figure helps in making the multiple use of data sets and there are two types of variables that contributes to the BHP Billiton supported by charities and cash expenditure. This can be regarded as the multiple set of data and it can be stated that the graphs have been labelled appropriately with all the variables being represented with the help of different set of colour.
Use of keys:
The below stated annual report of BHP Billiton helps in reflecting an appropriate use of keys as the table represents the amount of income tax and royalties that is related to the income amount of taxation paid (Yau 2013). The graph further reflects the usage of particular information with the help of tabular representation of information. Under the given scenario, the use of keys reflects the total amount of tax benefit derived by the company.
Presentation of tables and layouts:
BHP Billiton has properly presented the tables in their annual report to enable the users of financial statement to play an important part in it. The company has given due recognition to all the different tables from the below stated figure by segmenting the relevant information under different rows (Klein 2014). The presentations of tables are used with the help of different colours with comparison for the past two years have been made.
Functional variable graphs:
The below stated graphs helps in reflecting the fluctuations of Iron ore, Metallurgical coal, energy and crude oil over the stated period of time. The green line in the graph reflects fluctuation in the price of crude oil. Therefore, it should be noted that the charts in the annual report has been effectively used by the organisation.
Customization and determination of Geometry:
The below stated graph helps in illustrating the average number of employees and contractors employed during the year. From the below stated graph it is understood that the graphs is customised with appropriate level of geometry has been used (Ryan 2015). The graph reflects that the three meters with two variables have represented different colours. The graph represented are user friendly for readers to understand the determinants of geometry.
Positioniong:
The below stated graph reflects the number of injury as reported by the BHP Billiton. The colours used in the graph are discrete in nature and represents the different colour that is effective in visualisation (Rollinson 2014). The graph is easily understandable by the readers and fulfils the criteria of relationship between two variables.
Doughnut chart:
The below stated doughnut chart helps in reflecting the total amount employees contracted along with the contractors. The organisation has made the use blue and light blue colour along with the tables and other forms of graphs (Shukla and Dhir 2016). It can be understood that the size of the charts is also balanced in regard to teh total numbers of employees contracted during the year. The representation of employees is reflected in the right hand side and the contractors on the left hand side.
Conclusion:
To conclude with, BHP Billiton has successfully met the twenty criteria in their particular annual report for the financial year of 2016. Along with this, the presence of adequate number of graphs and charts has been stated in the report based on the essence of the presentation of the financial report. The study has helped in understanding the relevance and significance of the entire data process concerning visualisation. However, it is recommended that the organisation can still improve the different studies which is based on the above stated criteria.
Reference list:
BHP Billiton. 2017. BHP Billiton | Annual Reporting 2016. [online] Available at: https://www.bhpbilliton.com/investor-centre/annual-reporting-2016 [Accessed 12 Apr. 2017].
Ferres, L., Lindgaard, G., Sumegi, L. and Tsuji, B., 2013. Evaluating a tool for improving accessibility to charts and graphs. ACM Transactions on Computer-Human Interaction (TOCHI), 20(5), p.28.
Healy, K. and Moody, J., 2014. Data visualization in sociology. Annual review of sociology, 40, pp.105-128.
Klein, T.L., 2014. Communicating quantitative information: tables vs graphs.
Robinson, I., Webber, J. and Eifrem, E., 2015. Graph databases: new opportunities for connected data. ” O’Reilly Media, Inc.”.
Rollinson, H.R., 2014. Using geochemical data: evaluation, presentation, interpretation. Routledge.
Ryan, J., 2015, September. Communicating research via data visualization. In National Data Integrity Conference-2015. Colorado State University. Libraries.
Shukla, A. and Dhir, S., 2016. Tools for Data Visualization in Business Intelligence: Case Study Using the Tool Qlikview. In Information Systems Design and Intelligent Applications (pp. 319-326). Springer India.
Song, W., Wu, D., Wong, R., Fong, S. and Cho, K., 2015, October. A real-time interactive data mining and visualization system using parallel computing. In Digital Information Management (ICDIM), 2015 Tenth International Conference on (pp. 10-13). IEEE.
Stone, E.R., Bruin, W., Wilkins, A.M., Boker, E.M. and MacDonald Gibson, J., 2016. Designing graphs to communicate risks: understanding how the choice of graphical format influences decision making. Risk analysis.
Thorvaldsdóttir, H., Robinson, J.T. and Mesirov, J.P., 2013. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Briefings in bioinformatics, 14(2), pp.178-192.
Weissgerber, T.L., Milic, N.M., Winham, S.J. and Garovic, V.D., 2015. Beyond bar and line graphs: time for a new data presentation paradigm. PLoS biology, 13(4), p.e1002128.
Yau, N., 2013. Data points: Visualization that means something. John Wiley & Sons.
Zeng, K., Yang, J., Wang, H., Shao, B. and Wang, Z., 2013, February. A distributed graph engine for web scale RDF data. In Proceedings of the VLDB Endowment (Vol. 6, No. 4, pp. 265-276). VLDB Endowment.
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