BCOM 301 Business Communications

BCOM 301 Business Communications

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BCOM 301 Business Communications

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BCOM 301 Business Communications

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Course Code: BCOM301
University: Roosevelt University

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Country: United States

Question:

Keil Cronauer Answer to Professor Post
In my career of manufacturing, it’s important to know daily output trends that are occurring to ensure there are no negative aspects of the data. We need to assure the customer that our ability to produce on time deliveries is at the highest level. We currently use a program called PRISM, that enables me to go into and see daily completions. The only issue with this data most of the time is that it’s messy data by definition. Sure if I export the data to excel I can see which units completed each day, but that doesn’t help me dig (pun intended) into what is actually happening throughout my factory. Even if my daily output is where it needs to be, it doesn’t mean that I’m maximizing our assembly line and not able to make improvements in the process. A way to clean this data is separating the data much like our book does with the births and donations. I like to separate the data to see who are my top performers in the factory, how long each operation is taking, and where my bottle necks are throughout the factory. The raw data of completions are only telling a smart part of the story, if I separate like the book explains, then I’m able to receive the entire story and maximize the efficiency of the factory I manage.
The steps of cleaning this data are pretty straight forward, and much like our book describes. Luckily through the PRISM software I’m able to export my raw data to excel. Since I’m new to R Language, I would not yet convert this data to import into R, but I’m getting close to understanding how efficient that would be once I’m comfortable with R. Then I would separate my completions by employee number to see who is completing the most units. Also I’m able to see the time difference between operation completions. This is going to show me, the best person to put in each position throughout the factory. That way I can ensure the proper training is given to those that may need it and increase my overall efficiency no matter which person is there. The great part about this data is that I’m not worried about accuracy due to the source point of the data. This is where all of the work within the factory is done, and accuracy is very high if not perfect.
Another data set that may present as “messy data” would be the defects presented on Aircraft. During my enlistment as a Marine, my occupation was Avionics on F/18s. After each flight, a Marine from my shop would go get the maintenance card and insert it into a computer that would take the raw data from the flight then create maintenance discrepancies based on codes that appeared during the flight. The issue is the specifics of the data is unclear. It would help if there was more to the story of the data, for example time of the flight where the code existed, how many times the code popped, other flight characteristics that could aid in fixing the discrepancy. All of this data is available, but would need to be cleaned. The process of cleaning this data is a lot more intensive than my first example. Simply because we’re gathering all of this data from two sources, the flight card and the maintenance card. Each of these cards would need to be inserted into the same computer and then the discrepancy codes could be paired with the flight data for each time of the event. This would greatly increase the efficiency of the maintainers in repairing the jet to ensure mission readiness. Accuracy can be an issue when combining two different sources, proper import / export would need to be tested when extracting the data from these computer cards.
In summary, there are plenty of situations where messy data is given, and needs to be cleaned. There are many possibilities to use this messy data to conclude better decision making processes and many other advantages to clean data analysis.
Identify data situations from your own experience. You need to answer of the given post

Answer:

Dear Keil,
I immensely enjoyed reading your post. The two examples you discussed were very fascinating. Messy data being unstructured is by no means lacking in information, it is simply like you explained in your example of daily completion report data, in a format which is difficult to read and analyse. You correctly underlined the key requirement of how to make use of this data, which is to come up with a way to segregate and arrange the information in a way so as to reduce redundancy and only keep the relevant part in a structured way (Alekseev et al., 2016). I had not known about the software PRISM. I will surely look into it the details of its pros and cons later. Software of the same nature are now thankfully being made available having too been deigned to suit the specific requirements of every field where messy data is of importance, which caters to the different nature of the data. I had pointed out how EMR is specialized software for dealing with the issue in Medical field (Singer et al., 2015). Winpure , you may already know is yet another popular data cleaning program (Wickham, 2014). noSQL is a database which is capable to storing in unstructured data and accessing it in meaningful manner .(Mutz, Pemantle & Pham, 2017). You may want to consider the specifics of this program for any possible applicability for your issues. Now, aviation data I something that I found to be of particular interest. Though I do know how important and crucial a role data has to play in the field, it was an eye-opener to read about the nature of the data and the specifics of some of the issue.
References
Alekseev, A. A., Osipova, V. V., Ivanov, M. A., Klimentov, A., Grigorieva, N. V., & Nalamwar, H. S. (2016). Efficient data management tools for the heterogeneous big data warehouse. Physics of Particles and Nuclei Letters, 13(5), 689-692.
Mutz, D. C., Pemantle, R., & Pham, P. (2017). Forthcoming in The American Statistician The perils of balance testing in experimental design: Messy analyses of clean data.
Singer, J. M., André, C. D., Rocha, F. M., &Zerbini, T. (2015). Fitting non-linear mixed models to messy longitudinal data: an example.
Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(10), 1-23.Halloran, K. M., Murdoch, J. D., & Becker, M. S. (2015). Applying computer?aided photo?identification to messy datasets: a case study of T hornicroft’s giraffe (G iraffacamelopardalisthornicrofti). African Journal of Ecology, 53(2), 147-155.

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