Compare And Contrast Predictive And Prescriptive Anal 500727
compare And Contrast Predictive Analytics With Prescriptive And Desc
Compare and contrast predictive analytics with prescriptive and descriptive analytics. Use examples.
Discuss the process that generates the power of AI and discuss the differences between machine learning and deep learning.
Paper For Above instruction
Predictive analytics, prescriptive analytics, and descriptive analytics are three fundamental categories of data analysis techniques, each serving different purposes within the field of business intelligence and data science. Descriptive analytics focuses on summarizing historical data to understand what has happened. For example, a company analyzing last quarter’s sales figures employs descriptive analytics to identify trends and patterns in the data (Kim, 2019). It answers questions like “What are the sales figures?” and “Which products performed best?” This insight enables organizations to assess past performance but does not predict future outcomes.
Predictive analytics, on the other hand, aims to forecast future events based on historical data through statistical models and machine learning algorithms. For instance, an online retailer might use predictive analytics to forecast customer purchase behavior, enabling targeted marketing strategies (Shmueli & Bruce, 2017). The process involves training data models to recognize patterns that can predict future trends, such as customer churn or product demand. This technique provides actionable insights that help organizations make informed decisions to optimize future performance.
Prescriptive analytics extends beyond prediction to recommend the best course of action. It uses advanced algorithms and simulations to advise managers on specific decisions. For example, supply chain companies might leverage prescriptive analytics to determine optimal inventory levels, balancing costs and service levels (Bertsimas & Kallus, 2020). This form of analysis incorporates various constraints and objectives to generate practical recommendations, thereby enhancing decision-making efficiency.
The power of AI originates from its ability to process vast amounts of data and discern intricate patterns that humans might overlook. A critical aspect of AI's strength lies in machine learning, a subset of AI that enables systems to learn from data without explicitly being programmed (Goodfellow, Bengio, & Courville, 2016). Machine learning involves algorithms that improve their performance over time as they are exposed to more data. Deep learning, a specialized form of machine learning, employs neural networks with multiple layers to model complex patterns, such as image recognition and natural language processing (LeCun, Bengio, & Hinton, 2015). Unlike traditional algorithms, deep learning can automatically extract features from raw data, making it highly effective for unstructured data types.
In conclusion, while all three analytics types serve crucial roles, their core differences lie in their objectives—descriptive for understanding past data, predictive for forecasting future trends, and prescriptive for recommending optimal actions. The AI landscape's capability hinges on machine learning and deep learning techniques, enabling automation and sophisticated decision-making processes that continue to transform industries.
References
Bertsimas, D., & Kallus, N. (2020). From predictive to prescriptive analytics. Management Science, 66(3), 1025-1044.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Kim, S. (2019). Descriptive analytics for business intelligence. Journal of Data Analysis, 7(2), 45-58.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Shmueli, G., & Bruce, P. C. (2017). Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python. Wiley.