Examine How Predictive Analytics is Commonly Used.
Predictive analytics is one of the integral aspects of the contemporary business era. It has become one of the valuable facets in several industries. Businesses typically gain valuable insights and implications to set the foundation for better future decisions. For instance, corporations are using predictive analytics in the sales and marketing domain. Companies recognize customer churn as assessing or predicting the probability of leaving, which is essential to making practical retention approaches. Companies use predictive analytics for lead scoring as far as better resource allocation is concerned. Customer segments, mainly based on characteristics for better personalization, are also of good use (O'Connor, 2022). Predictive analytics has become valuable in the finance and risk management domain. Predictive tools have been used for fraud detection, credit risk assessment, and market volatility prediction. Predictive analytics is paramount in the operations and customer service disciplines. For example, predictive maintenance has become a reality, as predicting the potential failure of the system is necessary to make some critical interventions (Smolic, 2023). In customer services, the company can have valuable recommendations regarding personalization based on the practical evaluation of customer sentiments.
Similarly, in the healthcare industry, predictive analytics has been used for assessing potential patient readmission, disease diagnosis, and health surveillance. These examples indicate the significance and workability of predictive analytics in various industries. Predictive tools are still evolving, and applications are becoming vital in the coming years (Kennedy, 2023).
Research and Provide Examples of Companies that Use Predictive Analytics and Why.
Companies are using predictive analytics in multiple ways. For example, in the retail sector, Wal-Mart uses predictive analytics to determine or assess the demand patterns at the customer levels. Evidently, the shift in the demand patterns based on weather, holidays, and even local events is evident, and analytics unveils valuable insights to make relevant decisions. It is all about optimizing the inventory levels and evading the probability of stockout. Amazon, on the other hand, uses predictive analytics, as its suggestion engine utilizes it to make suggestions. It uses browsing history, buying patterns, and demographic information to streamline results (Vivek, 2023). JP Morgan, one of the prominent entities in the finance sector, uses predictive analytics for fraud detection, especially in real-time transactions. The most important thing is to prevent possible financial losses. Mayo Clinic, one of the largest healthcare organizations, is using predictive analytics to recognize patients with excessive vulnerabilities (complications), especially after surgery. This utilization of predictive analytics has helped to assess and reduce readmission rates (Mitchell, 2021). Netflix is one of the best examples in this context. It also uses a recommendation engine, which is backed by powerful predictive analytics. Customers typically have suggestions about series, movies, and shows. Indeed, it depends on the user’s preference and watch history. These organizations have strived to improve the usage of predictive analytics to be relevant in the competitive business landscape (Mixson, 2021).
Determine If Predictive Analytics Drives More Informed Decisions. Why or Why Not?
Predictive analytics has become a vital source for making informed decisions in the dynamic business landscape. The probability of informed decisions is high because identifying patterns and trends that are to be missed by humans is a significant strength of predictive analytics. Stakeholders in the corporations can have a holistic view of the situation, which leads to informed decisions. Several models have become sources for quantitative predictions as far as the future outcomes are concerned. Companies can compare scenarios and risk assessments, and it results in better strategic decisions and resource utilization. Analytics can enable proactive problem-solving recognition even before the occurrence (Lee, Cheang, & Moslehpour, 2022). It sets the foundation for better preventive measures and reduces negative influences. Yet, the quality of the informed decisions may be reduced due to the low-quality data and possible bias. The likelihood of inaccuracies is apparent, which can further lead to misleading strategic choices. Organizations intend to trust these models unquestioningly by ignoring the contextual aspects.
Nonetheless, human judgments and input are valuable in making informed decisions. There may be unforeseen situations or circumstances. There may be uncertainties that predictive analytics cannot consider. In this context, the probability of an informed decision is low. Hence, informed decisions through predictive analytics depend on these factors. It depends on the organization, as there must be a combination of flexibility and adaptability to make some adjustments in the decision-making process (Aljohani, 2023).
Determine if Big Data is Driving the Popularity of Predictive Analytics. Why or Why not?
Determining the interrelation between big data and predictive analytics is complicated. However, there are several ways big data is driving the popularity of predictive analytics. For instance, algorithms (predictive) rely on larger datasets to recognize complicated patterns and interconnections. Indeed, it is almost impossible in smaller datasets. It shows the significance of big data for predictive analytics. Big data is an excellent source to streamline invisible insights compared to conventional methods. Predictive tools can extract this information and simplify unidentified trends. It is one of the critical connections because it leads to innovative applications and discoveries. Big data applications or tools are more than capable of using (processing) massive data sets in real time (Rawat & Yadav, 2021). Making robust and dynamic predictions is possible, especially in a vigorously changing and evolving situation. Data-driven responses have become a reality, notably in the changing business conditions. In this modern and dynamic business landscape, big data is the best way to recognize patterns from diverse data sources, such as social media, transactions, sensors, and many others. Predictive analytics needs this input to present a more holistic and thorough understanding of the influential aspects. Big data is a powerful source of complicated and high-dimensional information. Predictive analytics can leverage this detailed information to recognize sophisticated relationships. Indeed, it is challenging when it comes to the smaller data sets. Scalability and sophistication are two critical facets of big data, as these drive the popularity of predictive analytics (Aljumah, Nuseir, & Alam, 2021).
References
Aljohani, A. (2023). Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility. Sustainability, 15(20), 15088. doi:https://doi.org/10.3390/su152015088
Aljumah, A. I., Nuseir, M. T., & Alam, M. M. (2021). Organizational performance and capabilities to analyze big data: do the ambidexterity and business value of big data analytics matter? Business Process Management Journal, 27(4), 1088-1107. doi:https://doi.org/10.1108/BPMJ-07-2020-0335
Kennedy, S. (2023, February 3). Predictive Analytics Tool Identifies Readmission Risk, Reduces Costs. https://healthitanalytics.com/news/predictive-analytics-tool-identifies-readmission-risk-reduces-costs
Lee, C. S., Cheang, P. Y., & Moslehpour, M. (2022). Predictive Analytics in Business Analytics: Decision Tree. Advances in Decision Sciences, 26(1), 1-29. https://www.proquest.com/scholarly-journals/predictive-analytics-business-decision-tree/docview/2674049708/se-2?accountid=30552
Mitchell, H. (2021, October 4). How Mayo Clinic uses data to improve surgical outcomes: 4 insights. https://www.beckershospitalreview.com/innovation/how-mayo-clinic-uses-data-to-improve-surgical-outcomes-4-insights.html
Mixson, E. (2021, March 30). Data Science at Netflix: How Advanced Data & Analytics Helps Netflix Generate Billions. https://www.aidataanalytics.network/data-science-ai/articles/data-science-at-netflix-how-advanced-data-analytics-helped-netflix-generate-billions
O'Connor, C. (2022, March 16). The Role of Predictive Analytics in Sales. https://www.yesware.com/blog/predictive-analytics-in-sales/
Rawat, R., & Yadav, R. (2021). Big Data: Big Data Analysis, Issues and Challenges and Technologies. IOP Conference Series. Materials Science and Engineering, 1022(1), 1-9. doi:https://doi.org/10.1088/1757-899X/1022/1/012014
Smolic, H. (2023, March 21). How Predictive Analytics Can Be Used in Fraud Detection and Prevention. https://graphite-note.com/predictive-analytics-fraud-detection-and-prevention
Vivek. (2023, September 25). Predictive Models For Forecasting Used By The Big Box Players. https://ruralhandmade.com/blog/predictive-models-for-forecasting-used-by-the-big-box#:~:text=Walmart%20also%20uses%20machine%20learning,and%20improve%20supply%20chain%20efficiency.
Assignment Requirements
Write a research paper based on using predictive analytics. You should complete the following:
Examine how predictive analytics is commonly used.
Research and provide examples of companies that use predictive analytics and why.
Determine if predictive analytics drives more informed decisions. Why or why not?
Determine if big data are driving the popularity of predictive analytics. Why or why not?
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