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MBA504 : Data Analytics Case Study

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MBA504 : Data Analytics Case Study

1 Download11 Pages / 2,527 Words

Course Code: MBA504
University: Kaplan University

MyAssignmentHelp.com is not sponsored or endorsed by this college or university

Country: United States

Question:
In this assessment, you need to consider an organisation in an industry of your choice and articulate the steps needed to enable data driven decision making. Describe the current mode of operation, possible inefficiencies, available data and how this data may be used to provide efficiencies based on the concepts and techniques covered in the subject.
Answer:

The current mode of operation
The company has numerous customer outlets both on the online platform and physical outlets that lead to the generation of large amount data on the company’s database. The study shows that the data is which is segmented in different tables and spreadsheets which way much confusing for the IT experts in carrying out the data analysis (Storey and Veda, 2017, pp.50-67). This can be the way a big trouble to an expert who has recently joined the company as tracking the trends in data is almost impossible. This is due to the availability of different categories of data for customers’ accounts, storage data, available products in stock data, the sold products and services data, the employees’ data, the stockholders’ data, discontinued operations, non-financial assets data, onerous leases, and the shareholder’s data. Compiling all these categories to identify the trends in the operation of the company has created a number of inefficiencies or created a room for possible inefficiencies in the near future as reiterated by (Geng et al., 2017, p.12). For instance, below is a sample of a section of an annual financial data report.
Figure 1: Sample financial report of Woolworths Company
 
                                                  
From the above, consolidated statement of the other comprehensive income must be read in conjunction with other notes accompanying notes to the consolidated financial statement in order to be understood. Another example of tabulated data that can be very difficult in observation of trends is as shown in the format below.
                                                    
Possible inefficiencies
A number of inefficiencies arise due to the poorly planned and organized data for the company. For example, the organizational inertia of the company would be very difficult with the use of tabular data and the related spreadsheets. Developing the right visual solution is quite compelling and requires collaboration between the finance department and the relevant audience whom the data is to be presented. The parties have to agree on the best visual presentation of data which can yield better insights and faster decision making as the main objectives of the study (Chandy, Rajesh, and Magda, 2017, pp.703-713).
Another inefficiency is the bad data that can be easily generated by use of the spreadsheets and other tabular formats in visualizing the presentation. The chances of data redundancies are very high when the data being handled is quite bulky when using these methods that can heavily affect the decision making processes that duly rely on data samples. Additionally, the problem of ending up with poor design may arise due to possible false positives, underestimation or overestimation of some aspects, poor comparison, distribution, relationships, and compositions in the data sets. This can easily cover up for fraud cases and creation of nuances that if not well addressed and analyzed will largely cause inconveniences in financial data analytics (Vona, 2017, pp.285-310).
 Creating efficiencies using data visualization
In an organization, the stakeholders require a simple way of visualized presentation of data as most of them do not want the trouble of complex information that requires a lot of inputs to be understood. For instance, at the Woolworths Group of Companies, the stakeholders can cut across the investors, the members or customers and can also be employees of the same organization who majorly rely on the data visualization during annual financial reports for their suggestions and decision making during the annual general meetings. This calls for better ways and methods of data visualization for such a group of people to contribute to the organization’s operation in terms of their intellectual abilities acknowledged by (Bach et al., 2017, pp.36-61).
For one to propose a suitable means that would create efficiencies to the company, the factors to be considered among many include; who the audience is, what the data is needed for, and what data-savvy they are. Another aspect to consider is the various users’ needs in terms of interactivity with the data, the mobile device usage, the design and interface requirements, and the level of details required. The desired outcome of the data visualization is also another important item to consider, how it will facilitate decision-making process, user education, or drive better discussions (Alharthi, 2017). For the Woolworths case study, the use tableau visualization method was chosen as the best method to eradicate the inefficiencies brought about by the prior methods of visual presentation.
The study findings identified that the use of the tableau software could enhance quality financial reports and operations as the tableau software has the capabilities to create CFO dashboards (Chief Financial Officers dashboard), financial ratios and Key Performance Indicators (KPI) from the given raw data with the best ways to visualize and present financial data. The data visualization using this method is relatively inexpensive and can also offer great speeds in the operations of the organization. With the use of tableau, data is unified and all of it is analyzed without leaving out some sets of data or basing on assumptions whereby the repeating and scaling of data in the analysis in faster than the prior methods. Additionally, the method is more appealing and it has been proved by those organizations that have already implemented it to be of more insights with interactive analysis and improved communication of insights within the organisation (Jiang, 2017).
The use of Tableau in achieving financial analytics efficiencies
The Tableau software visualizes data in a manner that can be understood by even non-analysts. It employs the use of features like colours, labels, different dimensions, and can create visual masterpieces thus revealing business insights which help in making informed decisions (Huber et al., 2018, pp.1-6).
As the size of data keeps growing due to the increase of new customers and at the Woolworths Company, the tableau software will enable the managers/ directors to effectively manage the trends without the risk of getting overwhelmed with the bulkiness of information. This is possible as the method is able to assemble and connect the various sources of data such as excel files, word documents, texts, database queries among others. The software outlines the data blending, connection using cubes, join types, custom SQL, and the Google Analytics.
There are multiple options of data representation in different views with the application of filters, the drill downs, formatting, the creation of sets or groupings, generation of trend lines, and forecasting performances as illustrated by (Szewranski et al., 2017, pp.337-349). However, the software enables the financial analysts to perform the above by simple clicks and selection of the desired presentation without the trouble of single entry data to generate a chart or a graph for example. The figure below is a snapshot sample of the tableau that enables the analyst to choose and decide the desirable visualization type of their preference. This automatically activates the best views for different measures and dimensions making it very simple to use.
                                                 
Moreover, the software can generate advanced and complex financial data in a very simple way. The additional methods in the software among many includes, funnel charts, Pareto charts and box plots, bump charts, waterfall charts, and histograms in the visualization of financial data (Symons et al., 2017). Below is a visualized data by tableau that is easy to understand at a single glance instead of tedious tabular data in spreadsheets.
 
                                                 
The trends in different sections can easily be mapped out for comparisons and clear conclusions. The use of colours provides an appeal for the stakeholders during the presentations of the reports at the annual general meetings. This instils confidence of the stakeholders to the existing management only through the simple and easy way to understand the analysed information by effective visualization. Some of the benefits are outlined below.
The benefits and consequences of effective data visualization  
Data visualization facilitates business information assimilation. This enables the users to grasp a large amount of information related to the conditions and operations of the business. Large companies such as Woolworths have data characterized with statistical tables and charts thus effective visualization will enable the decision makers to easily see connections of different information sets. Visualization also enables quick access to relevant insights in business. Research has indicated that companies which apply the data visualization tools are 28 per cent more likely to find the relevant information needed for business operation and sustainability than those that do not make use of data visualization tools (Fezarudin et al., 2017, pp.593-604).
Another benefit that comes along with this method is the rapid identification of latest trends. This enables the managers to pinpoint the opportunities that might arise for the company in the volumes of data analyzed. The aspects such as the customer behaviours can be easily identified which are essential for planning and strategizing purposes (Jun and Beung-Pyo, 2017, pp.381-407). Moreover, the analysis of predictive sales is made easier with the aid of real-time data visualization which is very vital for identification of reasons as to why some products are underperforming or lagging. This can lead to the creation of new discussions that can be a roadmap to unveil the development of the performance of the products over time in different geographical locations which could enable the executives to drill down into these specified locations to determine the products performing well and those underperforming in the market.
Lastly, (Lee et al., 2018, p.11) demonstrates how data visualization enables the users to interact with data which helps to expose the changes in a very timely manner. This encourages the analysts to further explore and manipulate the data in order to uncover other factors that could not be identified easily. Moreover, it also helps to omit irrelevant data during presentation making it easier to capture the attention of the executives and better communication.
Conclusion:
In conclusion, the Woolworths Group of Companies and any other large or small enterprises should embrace the use of data visualization techniques that would enable the managers to execute fast decision-making processes and adapt quickly to the ever-changing market dynamics. This will enable means for effective identification of the organisation setbacks that are dragging the business into underperformance and therefore guide the managers in laying in place effective future plans that would lead to business sustainability and winning of markets for their products (Roberts et al., 2018, pp.791-801). In addition, the cases of financial frauds could be identified easily during the organisation’s financial audits within the employees. All these can be achieved through effective data visualization.
Introduction:
In this case study, we focused on the Woolworths Group of Companies with its Head offices in Bella Vista, New South Wales 2153 in Australia. The company deals in a huge range of products including clothing and accessories, foodie gifts, meal ideas, brands, homeware, beauty accessories, among many other things. With all these staffs they deal in, the company has a wide range of data that it stores on its database for retrieval, analysis, and visualization. (Appelbaum et al., 2017, pp.29-44) acknowledges that this processes can be very tedious if suitable measures are not put in place to enable efficient decision-making or it can end up making the data collected less helpful in decision making processes.
The purpose of this case study was to determine the mode of operation that is in place with regard to data analytics of the company annual reports which include financial reports among others. The case study is also anticipated to find out the possible challenges encountered by the company in its analysis processes and examine better modes of operation that could enable the company to handle its data in the most effective visualization ways (Kim et al., 2017, pp.491-500). Moreover, the study further intends to determine the type of data collected and used by the company in its daily operations that are used for the annual report.
References:
Alharthi, N. and Gutub, A., 2017. Data visualization to explore improving decision-making within Hajj services. Sci Modell Res, 2(1), pp.9-18.
Appelbaum, D., Kogan, A., Vasarhelyi, M. and Yan, Z., 2017. Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, pp.29-44.
Bach, B., Dragicevic, P., Archambault, D., Hurter, C. and Carpendale, S., 2017, September. A Descriptive Framework for Temporal Data Visualizations Based on Generalized Space?Time Cubes. In Computer Graphics Forum (Vol. 36, No. 6, pp. 36-61).
Chandy, R., Hassan, M. and Mukherji, P., 2017. Big data for good: insights from emerging markets. Journal of Product Innovation Management, 34(5), pp.703-713.
Fezarudin, F.Z., Tan, M.I.I. and Saeed, F.A.Q., 2017, August. Data Visualization for Human Capital and Halal Training in Halal Industry Using Tableau Desktop. In Asian Simulation Conference (pp. 593-604). Springer, Singapore.
Geng, Y., Lin, Y., Ma, Y., Guo, Z., Gu, C. and Wang, M., 2017, January. Interactive Visualization of DGA Data Based on Multiple Views. In Journal of Physics: Conference Series (Vol. 787, No. 1, p. 012001). IOP Publishing.
Huber, T.C., Krishnaraj, A., Monaghan, D. and Gaskin, C.M., 2018. Developing an Interactive Data Visualization Tool to Assess the Impact of Decision Support on Clinical Operations. Journal of digital imaging, pp.1-6.
Jiang, F., 2017. Data Analytics Helps Business Decision Making.
Jun, S.P. and Park, D.H., 2017. Visualization of brand positioning based on consumer web search information: Using social network analysis. Internet Research, 27(2), pp.381-407.
Kim, N.W., Schweickart, E., Liu, Z., Dontcheva, M., Li, W., Popovic, J. and Pfister, H., 2017. Data-driven guides: Supporting expressive design for information graphics. IEEE transactions on visualization and computer graphics, 23(1), pp.491-500.
Lee, B., Srinivasan, A., Stasko, J., Tory, M. and Setlur, V., 2018, May. Multimodal interaction for data visualization. In Proceedings of the 2018 International Conference on Advanced Visual Interfaces (p. 11). ACM.
Roberts, J.C., Ritsos, P.D., Jackson, J.R. and Headleand, C., 2018. The Explanatory Visualization Framework: An active learning framework for teaching creative computing using explanatory visualizations. IEEE transactions on visualization and computer graphics, 24(1), pp.791-801.
Storey, V.C. and Song, I.Y., 2017. Big data technologies and Management: What conceptual modeling can do. Data & Knowledge Engineering, 108, pp.50-67.
Symons, D., Konczewski, A., Johnston, L.D., Frensko, B. and Kraemer, K., 2017. Enriching Student Learning with Data Visualization.
Szewra?ski, S., Kazak, J., Sylla, M. and ?wi?der, M., 2017. Spatial Data Analysis with the Use of ArcGIS and Tableau Systems. In The Rise of Big Spatial Data (pp. 337-349). Springer, Cham.
Vona, L.W., 2017. Fraud Data Analytics for Financial Statements. Fraud Data Analytics Methodology: The fraud scenario approach to uncovering fraud in core business systems, pp.285-310.
 
 

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