Module 7 Project Two Lab

Project Two: Analyzing Databases Introduction to Structured Database Environment Project Two Project Two Scenario The product manager of Quantigration has asked your data analytics team for a report summarizing your analysis of the return merchandise authorizations (RMAs) that have been received. These are the same data sets that you have already been working with. Your report should focus on summarizing the analysis and presenting your findings to the product manager. Directions RMA Report In your report on the findings of your analysis, respond to the manager’s requests to summarize the data you have been working with to identify key information that will be of value to the company when developing future business strategies to streamline operations. Remember, not everyone who reviews this report will have a technical background. 1. Begin by writing SQL commands to capture usable data (which you have preloaded into Codio) for your analysis. Specifically, the product manager wants you to investigate and analyze the following: o Analyze the frequency of returns by state, and describe your findings in your report. The state that has the most returns is Massachusetts with 972 returns, followed by Arkansas with 844, then Oregon with 840. Statistically, these numbers are not 2. In your report, summarize your analysis of the data you’ve captured. Include screenshots of the results of each query. When summarizing results, you may want to consider the following questions: o How does the data provide the product manager with usable information? The data we have could help the company determine why the return rates on their products are so high. We can see that their sales numbers appear to be great, but when you look at the fact that up to 20 percent of them are returned could pose a problem for the company. We can see by the data below that 39% of the returns are because the product was “incorrect”, 37% was due to “defective” products, and 24% was “other”. The manager should begin with this information and try to determine why these percentages are so high. o What are the potential flaws in the data that has been presented? Many flaws could be present in the data, the biggest could be that the numbers could be inaccurate. This database has thousands of records, there is the possibility that there were mistakes when entering data. I think the biggest flaw in the data is the fact that we do not know the real reason why these products were returned. The return reasons are very vague, the company should do a deeper dive into the reason is these products were returned. Incorrect could mean that the customer ordered the wrong product, or it could mean that the product advertised wasn’t what it claimed to be. Damaged could be that the product was damaged by the manufacturer or it could have been damaged in shipping. Taking a deeper dive into the reasons the products were returned could help the company remedy its issues. o Are there any limitations on your conclusions, or any angles you haven’t considered? The only limitation I see is that the reasons for returns are not very descriptive. They should take a deeper dive into what their return categories “incorrect”, “defective”, and “other” mean. If they can find out the reasons behind the returns, they may be able to correct these issues. 3. Clearly communicate your findings to stakeholders. o Make sure that all parts of your report are written in a way that very clearly explains the necessary information. When we combine the Orders table and the RMA table to compare the number of orders and the number of returns, we see that the best selling product is the ENT48-10 F, however, it is also the most returned product with over 98% of these products being returned. If we look at the sales and return percentages of the topselling products, for the top 9 products the return percentage is over 97%. The company should be looking into why the return rate of these products is so high.