Course - Business Intelligence Discussion 1 (Chapter 1): Compare and contrast predictive analytics with prescriptive and descriptive analytics. Use examples. Note: The first post (600-700 words)should
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Post 1:
1 day ago
Madhuri
Discussion Week 1
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Discussion Week 1
Predictive analytics is concerned with foreseeing and predicting future events, while descriptive analytics is concerned with past events. Using historical data and customer insights to analyze past data patterns and trends will help predict what will happen in the future and, as a result, inform some areas of an organization, such as setting realistic goals, for example, effective planning, managing performance expectations and avoiding risks.Predictive analytics helps to forecast possible outcomes as well as the likelihood of those outcomes happening. In order to make predictions, machine learning algorithms, for example, take existing data and try to fill in the gaps with the best possible guesses.
Examples of Predictive analytics are as follows
E-commerce – forecasting consumer tastes and making product recommendations based on previous orders and searches.
Predicting whether consumers will buy another product or leave the shop is called sales.
Human resources – identifying whether or not workers are considering leaving and persuading them to stay.
IT protection – detecting potential security violations that necessitate further investigation
Prescriptive analytics anticipates what will occur, where it will occur, and most importantly, why it will occur. Following consideration of the possible implications of each decision, recommendations as to which decisions will best capitalize on future opportunities or reduce future risks can be made. According to Halo Business Intelligence, predictive analytics ,predicts several futures and, as a result, allows you to weigh the pros and cons of each before making a decision.Prescriptive analytics tells you what can be done if descriptive analytics tells you what has happened and predictive analytics tells you what could happen. This methodology is the third, final, and most advanced stage of the business analysis process, and it is the one that motivates companies to take action by assisting executives, managers, and organizational employees in making the best decisions
Examples of Prescriptive analytics are as follows.
Price fluctuations in the oil and manufacturing industries are being tracked.
Equipment management, maintenance, price modeling, production, and storage are all being improved.
Healthcare – evaluating readmission rates and costs in order to enhance patient care and healthcare administration.
Pharmaceutical insurance – assessing risk in terms of premiums and pricing for clients.
Descriptive analytics is a form of data analysis in which historical data is collected, structured, and then presented in a logical way. Descriptive analytics is primarily concerned with what has already happened in a market, and it does not make inferences or predictions based on its results, unlike other forms of analysis. On the other hand, descriptive analytics is a cornerstone.Descriptive analytics uses two primary approaches to uncover historical data: data aggregation and data mining (also known as data discovery). Data aggregation is the method of collecting and arranging data to establish manageable data sets. These data sets are then used in the data mining process, which uncovers patterns, trends, and meaning before being presented in an understandable manner.
Examples of how descriptive analytics can be used are as follows
Past practices are summarized, such as revenue and operations specifics or marketing campaigns.
Likes on Instagram and Facebook, are examples of social media engagement data.
Broad trends are reported on.
Obtaining survey results
References
Lindert, Bryan (October 2014). "Eckerd Rapid Safety Feedback Bringing Business Intelligence to Child Welfare" (PDF). Policy & Practice. Retrieved March 3, 2016.
Finlay, Steven (2014). Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and Methods (1st ed.). Basingstoke: Palgrave Macmillan. p. 237. ISBN 1137379278.
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Post 2:
Puja Sah
Discussion 1
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Predictive analytics is a method of analysis that generally applies to large data sets. The focus of most predictive modeling is to predict or predict the future state of a population. The term predictive analytics is sometimes used interchangeably with natural language processing, machine translation, and text analytics (Pessach et al., 2020). However, in most cases, it is not specific to such techniques, mainly when applied to information retrieval. Specifically, predictive analytics can classify as the application of algorithms, data mining, and modeling in the process of prediction. An example of Predictive analytics is a classification or assessment process in which predictive analytics is to identify patterns and predict future events. An example of predictive analytics (P) is data mining, and it is where data sets use to derive conclusions based on what humans or computer programs. One example of predictive analytics is the concept of neural networks is the mathematical structures that use to approximate neural states (Pessach et al., 2020).
Prescriptive analytics focuses more on identifying new opportunities and providing insight to maximize the return on investment. It considers the business impact of the data generated and the analytics used to identify opportunities, make decisions, and optimize business performance. Prescriptive analytics enables companies to achieve increased revenue from customer loyalty and other important insights into customer behavior (Pessach et al., 2020). Prescriptive analytics is a powerful tool for organizations because it predicts where specific problems exist before they become problems, which patterns emerge early in the data collection process and can use as triggers for action. The use of example in prescriptive analytics is beneficial for business strategy, as it enables organizations to identify opportunities and identify actions that will drive long-term value creation. An example of prescriptive analytics analysis describes the data needed for any business decision. The prescriptive analysis identifies specific issues and trends to prioritize and prioritize which data is most needed to determine the next business decision. These specific issues and trends usually address through analytic data techniques (Pessach et al., 2020).
Descriptive data analytics is the collection, analysis, and visualization of structured data and presenting information in a structured and visual way that conveys an understanding of the underlying relationships. Descriptive analytics is the method that describes the internal state of an entity. Descriptive analytics refers to the methods used to create and capture predictive models and patterns, such as a sequence of events and correlation of data. The use of example in descriptive analytics is most useful in the business environment, and its primary role is to describe the internal state of an entity (Sharda et al., 2020). Descriptive analytics uses example as a data science technique to build a complete picture of the business than is possible with traditional data mining, which focuses on finding and analyzing meaningful data to infer underlying patterns, relationships, and other information from big data. The use of example in descriptive data analysis is a data mining activity designed to find meaningful patterns and relationships between disparate data sources. The primary goal of descriptive data analysis is to help a business organization learn and analyze more about its customers, the products, and its customers' behaviors (Sharda et al., 2020).
References
Pessach, D., Singer, G., Avrahami, D., Ben-Gal, H. C., Shmueli, E., & Ben-Gal, I. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134, 113290.
Sharda, R., Delen, Dursun, and Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Edition. By PEARSON Education. Inc.
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