reply to students response to question in 150 words min . Be sure to answer in detail and refer to specific concepts and provide 1 reference
Think of a problem or opportunity that would require data to analyze in your own company. As director of your organization’s research and analytics department, where would you be gathering the data and what would you be looking for in terms of patterns, predictive analytics, etc.?
I used to work at a large pharmaceutical company called Danaher that manufactured medical devices at one of their sister companies. I was responsible for testing hundreds of HPLC column devices for quality control purposes. After each column was tested, I documented and reported the results to my manager. As the director of the company’s research and analytics department, the data would be visible from a software called Chromeleon 7 where the data testing results were inputted for each column. The director would be looking for any faulty or failing columns, determine exactly why they failed, narrow down the source of the problem, and then evaluate for repetitions or patterns that have been occurring. He would then determine the most effective way to reduce failure and increase passing rates. If there were frequent failures, he would cross-reference the data to see if there was a parameter that the failing columns had in common. Depending on the parameter, the director could determine the source of the problem. For instance, if a batch of columns were packed with bad or the wrong kind of media, flushed with the wrong solvent, tested with the wrong mobile phase, or injected with the wrong standard, the analysis of the data could produce these conclusions. Based on these evaluations, the director could conclude the best recipes for the columns to be packed in the future, as well as be aware of the most common reasons the columns tend to fail and warn the production team of such mistake possibilities. Companies are working harder to spot problems in machinery that could result in defects and quality control has become more streamlined as firms have become more aware of the common issues and have found ways to avoid them from showing up in the future (Mathews, 2019).
Mathews, K. (2019). How big data is improving quality control and testing. Retrieved from https://channels.theinnovationenterprise.com/articles/how-big-data-is-improving-quality-control-and-testing
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