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Big Data Analytics as Service Provider
Published in Diego Galar Pascual, Pasquale Daponte, Uday Kumar, Handbook of Industry 4.0 and SMART Systems, 2019
Diego Galar Pascual, Pasquale Daponte, Uday Kumar
Advancements in the speed of computing and the development of complex mathematical algorithms applied to data sets have made prescriptive analysis possible. Specific techniques used in prescriptive analytics include optimization, simulation, game theory and decision-analysis methods (Dan Vesset et al., 2018c). This state-of-the-art type of data analytics requires not only historical data, but also external information due to the nature of statistical algorithms. Prescriptive analytics uses sophisticated tools and technologies, such as machine learning, business rules and algorithms, which make it sophisticated to implement and manage. That is why, before deciding to adopt prescriptive analytics, a company should compare required efforts vs. an expected added value (Bekker et al., 2018).
Basics of Analytics and Big Data
Published in Ramakrishnan Ramanathan, Muthu Mathirajan, A. Ravi Ravindran, Big Data Analytics Using Multiple Criteria Decision-Making Models, 2017
U. Dinesh Kumar, Manaranjan Pradhan, Ramakrishnan Ramanathan
Analytics can be grouped into three categories: descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive analytics deals with describing past data using descriptive statistics and data visualization; useful insights may be derived using descriptive analytics. Predictive analytics aims to predict future events such as demand for a product/service, customer churn, and loan default. Prescriptive analytics on the other hand provides an optimal solution to a given problem or offers the best alternative among several alternatives. In other words, descriptive analytics captures what happened, predictive analytics predicts what is likely to happen, and prescriptive analytics provides the best alternative to solve a problem. Although all three components of analytics are important, the value-add and the usage of different analytics components are shown in Figure 3.1. For all the hype around analytics, vast majority of organizations use descriptive analytics in the form of business intelligence (BI). Significantly, a smaller group of organizations use predictive analytics, mainly for forecasting; the number of organizations using prescriptive analytics is minimal at this point in time in comparison with descriptive and predictive analytics. However, it is interesting to note that the value-add to a company increases many fold if organizations were to use predictive and prescriptive analytics conjointly as compared to descriptive analytics alone.
Natural Language Processing in Data Analytics
Published in Jay Liebowitz, Data Analytics and AI, 2020
Prescriptive analytics makes use of the results obtained from descriptive and predictive analysis to make prescriptions (or recommendations) around the optimal actions to achieve business objective such as customer service, profits, and operation efficiency. It goes beyond predicting future outcomes by suggesting actions to benefit from the predictions and showing the implications of each decision option.*Optimization and decision modeling technologies are used to solve complex decisions with millions of decision variables, constraints, and tradeoffs.†
Smart agriculture: a literature review
Published in Journal of Management Analytics, 2023
Predictive analytics involves predicting possibilities for the future. Prescriptive analytics is used to optimize decision making through various algorithms, i.e. machine learning, data mining, deep learning, and others (El Morr & Ali-Hassan, 2019; Lepenioti et al., 2020). As predictive analytics, prescriptive analytics has gathered interest in all fields (Larson & Chang, 2016), (https://www.gartner.com/binaries/content/assets/events/keywords/catalyst/catus8/2017_planning_guide_for_data_analytics.pdf). However, by applying prescriptive analytics in agriculture, farmers can optimize the use of inputs in real-time and reduce the production cost, and also crop losses can be avoided. However, the role of the farmer can only be fully simplified if the corresponding actions are explicitly prescribed.
Big data for cyber physical systems in industry 4.0: a survey
Published in Enterprise Information Systems, 2019
Predictive analytics can predict what will happen in the future based on the assumption that what happened in the past will happen in the same or a similar way in the future. For example, it can be used to forecast the demand for an existing product. With the predicted demand, companies can have different plans to produce the predicted number of products by considering when it is needed, what is the current available production capacity, the requirements of raw materials, labor cost, and others. Different plans will have different costs for different factors. For example, manufacturing products with its full load can reduce the labor cost but increase the inventory storage cost. Prescriptive analytics are very important because they can search for the optimal plan with the lowest overall cost. The existing research of prescriptive analytics in industry 4.0 includes different self-optimizing autonomic strategies to accomplish a given goal in a dynamically changing environmental conditions and demands (Maggio et al. 2012), and the goal-oriented self-organization algorithms to optimize design cost functions in a distributed fashion and induce an overall degree of autonomy in the CPS (Bogdan 2015). There are two types of prescriptive analytics methods: mathematical programing and heuristic search. While mathematical programming is designed to find the global optimal solution, heuristic search is designed to find local optimal solutions.
Business Analytics and Organizational Value Chains: A Relational Mapping
Published in Journal of Computer Information Systems, 2018
Rudolph T. Bedeley, Torupallab Ghoshal, Lakshmi S. Iyer, Joyendu Bhadury
BA capabilities can generally be classified into the following three categories [15]: Descriptive analytics: Descriptive analytics is the type of statistics that provide descriptive analysis of what is evident from the data. With this type of analytics, it is possible to find out current trends and basic statistics from available data. This type of analytics tries to answer the question of what has happened.Predictive analytics: Predictive analytics is the type of analytics where future of a process, product, or activity can be predicted based on the result of the descriptive analytics. This type of analytics tries to answer the question of what could happen.Prescriptive analytics: Prescriptive analytics is the most active type of analytics where the optimum output can be prescribed based on results of descriptive and predictive analytics. This type of analytics attempts to answer the question of what should happen.