Predictive Analytics
Predictive analytics is a method that gathers big data and presents t in a way that is well understood. The main aim of predictive data is to present current and historical data to plan for the future. It uses methods and tools that are either supervised, semi-supervised, or unsupervised. Predictive analytics is categorized into two; machine learning and regression techniques. To properly implement parallelism in machine learning, the authors utilize predictive knowledge from Neural Networks using Graphics Processing Unit Machine Learning Library (GPUMLib) by applying algorithms like Self-Organizing Maps and Multiple Backpropagation.
Importance of Predictive Analytics
Predictive analytics has been used in many companies and institutions due to its vast advantages. It improves customer service since all the complaints are analyzed, and proper responses are taken to action. The analytics are quick to offer recommendations; hence there is understanding. Predictive analytics systems can detect fraud and help the company to prevent it in the future. Other importance includes reducing risks, improving efficiency, reducing cost, and helping in the identification of opportunities that add value to the company.
Prescriptive Analytics
Prescriptive analytics utilize is a method that utilizes data from descriptive and predictive analytics to make decisions. It makes use of AI to collect big data that overwhelm humans. Prescriptive analytics may not be accurate; hence data scientists need to monitor the systems to ensure missing or incorrect data. Model overfitting can lead to wrong predictions. Data scientists should exercise machine learning algorithms and functionalities to build a predictive analytics system because different algorithms assume different data structures and completeness. For example, when using a linear regression model, it is assumed that the prediction variable can be represented as a weighted sum of the descriptive characteristics. In practice, however, not all data is linearly connected, and as a result, linear regression cannot be used to solve every data science problem in every situation.
Importance of Prescriptive Analytics
Prescriptive analytics make use of artificial intelligence in place of human intervention. Although we cannot downplay human power, IA can collect big data and organize it so that the algorithms can use it to make decisions. Decisions made from prescriptive analytics systems increase the performance of the business since all factors have been checked and predictions made right. If there is an error either in big data collection or organization, the prescriptive analytics algorithms can recommend solutions immediately since it is automated.
Role of Finance and Accounting Departments in using Predictive and Prescriptive analytics
The two departments have the role of standardizing the most crucial information in the organization, that is, master data. They are mandated to prepare an excel sheet for data presentation used in decision making. The decision-making model operationalizes thinking to be used by accounting and finance managers. The two departments have a responsibility to unite other departments while using the two analytics. Other roles of finance and accounting departments include pinpointing outliers across the firm based on a continuous assessment, utilizing open-source components to be used in the supply chains like MySQL, end-to-end processing, and machine learning, and establishing and maintaining a list of approved external data sources to supplement existing internal data, with the list being updated regularly to reflect any new sources that have become available.
Technology and Skills Limitation in Finance Department
Organizations have introduced big data (BD) that is overwhelming employees in collecting data. A company can receive thousands of data each day that needs to be analyzed and related to other historical data. Most finance departments lack automated systems to collect and organize data. The limitation can be overcome by installing an automated system that will give employees an easy time processing data. Another limitation in the finance department is the lack of a strong data system for visual representation. Data is always presented visually in charts or graphs for easy understanding. Manual putting the data in the reporting tool may be tiresome and time-consuming. Traditionally, finance departments manually fit the data. The finance department can address the problem by having strong data systems to build reporting for easy decision-making. Employees from the entire organization face confusion and anxiety about changing from traditional data entry even with the knowledge of the importance of automation and analytics. The finance team should comprehensively educate the employees on the meaningfulness of using analytics.
Many finance departments face the problem of analyzing data due to a lack of skilled labor. A couple of employees may lack the capacity to deal with comprehensive analysis. The finance department can overcome this limitation by enhancing the hiring process and hiring according to competency and making all employees’ analytics easy to use. Finally, finance departments are faced with the challenge of accessing necessary data for use. They should hence employ an effective database to counter such issues.
Real-life Application for Predictive Analytics
Predictive analytics is widely used in the healthcare industry. In 2019, Digital Health Cooperative Research Centre partnered with the Royal Melbourne Institute of Technology and came up with software to elevate clinical decision-making for the aged. The software predicts the health condition of the aged to avoid emergency hospitalizations using predictive analytics algorithms. Predictive analytics help the family, residents and healthcare providers to plan ahead before the worst happens.
Weather forecasting utilizes predictive analytics to predict weather patterns. By using historical data and satellite imagery, weather estimates are drawn even a month in advance. Additionally, the information from the estimates can be used to find the impact of global warming. For example, if data visualization is used together with predictive models, we can be able to see the level of carbon dioxide and the rising sea.
Real-life Application of Prescriptive Analytics
Prescriptive analytics is used in navigation. Prescriptive analytics is widely used in GPS technology to give suggestions on routes that can be used by giving the users a way to reach to their destination according to the during of the journey and road closures. Prescriptive analytic tools are used to calculate the distance between your starting point and the stopping/destination point and predict the shortest and the quickest way.
Another example is an insurance company that uses prescriptive analytics to determine when it is most beneficial for them to pay new claims versus appealing them. The insurance company collects data on incoming claims; it takes time to process the claim and the different outcomes of these claims. By using predictive analysis, the insurer can forecast whether a particular claim can be paid or appealed based on the data surrounding the event.
References
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UNSW. 2020. “Descriptive, Predictive, Prescriptive Analytics | UNSW Online.” Studyonline.unsw.edu.au. Web.
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