Discussion 7 | Predictive vs Prescriptive Analysis
Initial discussion post is to be 220-250 word mimum and 2 cite scholarly resources
Peer reply response to two peers
150-160 word mimum for each post reply to peer stated below. One response should have 1 citation scholarly resource
REVIEW
Review all assigned reading and resources in preparation for the discussion.
Early Predictions of Movie Success: The Who, What, and When of Profitability (opens in a new window) (please see attached article below by the name of download3)
Data can be processed and analyzed in various ways including predictive and prescriptive analysis.
RESPOND
Share a real-world example for each type of business analytic: descriptive, predictive and prescriptive, by discussing how businesses used the analytic to make decisions.
Share some of the conclusions you have made thus far for the Final Assignment – Data Mining For Profitability.
Based on the predictive and prescriptive analyses, were there any unexpected data outcomes?
What were some challenges/problems in creating the test data model? How did you resolve the problem?
Were there any data issues that might have affected the outcome? How did you resolve the issue?
Requirements
Initial posts: 150-200 words
DISCUSS
Post at least two substantive responses to your colleagues. Responses could include suggestions for further resources, questions of clarification, or providing context and insight. Avoid simple posts of agreement; if you agree, explain why, and then thoughtfully further the conversation. It is important to include at least a scholarly resource and provide valid examples as needed.
Requirements
Response to Peers:
150 minimum words
Respond to a minimum of two posts
APA formatted sources where specified
Please respond to peers below
Peer 1
Yurell Kellem:
The example of predictive analytics in business is in the current COVID 19 pandemic. It was highly predictable that firms may underperform based on the facts laid down related to the government restrictions (Chaturvedi & Chakravarty, 2021). For instance, it was predicted that COVID 19 would help in reducing the virus spread in India. The descriptive analytics entails summarizing historical data to get the trend or what will happen next and a good example is the firm’s financial reports, which are summarized, analyzed and used during budgeting and forecasting (Evans, 2017). Lastly, the prescriptive analytics example is the googles waymo (a self-driving car). The cars make multiple calculations for each trip hence supporting its AI technology to decide on places to make turns, load, and even offload passengers.
Data mining for profitability was a good and enjoyable topic, which has improved by research and analytical skills for making informed decisions (Korovin et al., 2016). Depending on the predictive and prescriptive analyses, there were unexpected outcomes due to uncertainties that cannot be told. Some of the challenges in creating the test data model include issues related to data privacy and accuracy of information. The problems were solved through cyber security strategies and counter checking of data entries (Duan et al., 2020). Data issues that may have affected the outcomes are problems such as inaccuracies and data theft, which were solved through strategizing for data security and training staffs for data entry.
References
Chaturvedi, D., & Chakravarty, U. (2021). Predictive analysis of COVID-19 eradication with vaccination in India, Brazil, and U.S.A. Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases, 92, 104834. https://doi.org/10.1016/j.meegid.2021.104834
Duan, Y., Cao, G., & Edwards, J. S. (2020). Understanding the impact of business analytics on innovation. European Journal of Operational Research, 281(3), 673-686.
Evans, J. R. (2017). Business analytics. Pearson.
Korovin, I., Khisamutdinov, M., Schaefer, G., & Kalyaev, A. (2016, May). Application of hybrid data mining methods to increase profitability of heavy oil production. In 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV) (pp. 1149-1152). IEEE
Peer 2
Kyler Keef,
For me, a real world example of these three analytics would be applied in the music industry. It can be very descriptive because you have to identify your target audience. If you are singing hip hop or rap, you don’t want to pitch to an audience that is alternative rock or a jazz group because you will do no good. In being prescriptive, you have to be understanding and enforcing on the type of shows, merch, or other items you embellish in. Hand in hand, you have to also be predictive by foreshadowing what you may need and how much you will sell. I think the most unpredictive measure taken was some of the inquiries. I was surprised to see that not many inquiries were affected. The hardest problem for the model was the layout. I’m always unsure how I need to make them, so I try my best and hope it is okay. I do not feel as though I had any major data issues. Most of the process seemed to go smoothly.
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