Strategic Audit Report for company: Samsung

Prepare and explain an EFAS table for Samsung. Data presented in tables do not speak for themselves. Whenever you present the results of data analysis, you must interpret the data. What does it indicate? What does it mean? What are the implications?Should look similar to the following table:Prepare and explain an IFAS table for Samsung. Data presented in tables do not speak for themselves. Whenever you present the results of data analysis, you must interpret the data. What does it indicate? What does it mean? What are the implications?Should look similar to the following table:

Preparing the tables: EFAS, IFAS, SFAS, and TOWSData is often presented in tables, and sometimes students assume that the tables of datawill speak for themselves. They do not; data needs to be interpreted. It is a major mistaketo assume that the reader will perceive the same thing you do in the data. Whenever youpresent the results of data analysis, you must interpret the data. What does it indicate?What does it mean? What are the implications?When you prepare the EFAS, IFAS, SFAS, and TOWS tables, you need to add explanations.Whenever you prepare any table you also need commentary explaining the meaningbehind the numbers. Explaining how the score is derived can be a good start, but that isnot sufficient. What does the score indicate about the company’s position in the industry?What does it indicate relative to the company’s strategy? Is this a good, bad, or indifferentscore? Why?The reason for emphasizing the explanation is that people who read your reports are neveras immersed in the data as you are, nor should you expect them to be. They have notstudied the issues in depth as much as you have. Your reports (with tables, figures, andillustrations) should explain things so less-informed readers will know what it all means,and enable them can make informed decisions.There are three keys to using and understanding the EFAS and IFAS tables. First, there isthe issue of identifying the most relevant factors. Second, there is the issue of how toassign accurate weightings. Both of these call for subjective judgment. However, oncedetermined, the third key issue is to objectively use the data generated, regardless of howyou feel about the company emotionally. Or, as one of my mentors used to say, “Call it likeit is, not what you would like it to be.”