scholarly journals Managing the Training Process in Elite Sports: From Descriptive to Prescriptive Data Analytics

2021 ◽  
pp. 1-5
Author(s):  
Kobe C. Houtmeyers ◽  
Arne Jaspers ◽  
Pedro Figueiredo
Keyword(s):  
Data Analytics ◽  
Predictive Analytics ◽  
Business Analytics ◽  
Business Organizations ◽  
Elite Sport ◽  
Training Process ◽  
Use Of Data ◽  
Prescriptive Analytics ◽  
And Performance ◽  
Conceptual Data

Elite sport practitioners increasingly use data to support training process decisions related to athletes’ health and performance. A careful application of data analytics is essential to gain valuable insights and recommendations that can guide decision making. In business organizations, data analytics are developed based on conceptual data analytics frameworks. The translation of such a framework to elite sport may benefit the use of data to support training process decisions. Purpose: The authors aim to present and discuss a conceptual data analytics framework, based on a taxonomy used in business analytics literature to help develop data analytics within elite sport organizations. Conclusions: The presented framework consists of 4 analytical steps structured by value and difficulty/complexity. While descriptive (step 1) and diagnostic analytics (step 2) focus on understanding the past training process, predictive (step 3) and prescriptive analytics (step 4) provide more guidance in planning the future. Although descriptive, diagnostic, and predictive analytics generate insights to inform decisions, prescriptive analytics can be used to drive decisions. However, the application of this type of advanced analytics is still challenging in elite sport. Thus, the current use of data in elite sport is more focused on informing decisions rather than driving them. The presented conceptual framework may help practitioners develop their analytical reasoning by providing new insights and guidance and may stimulate future collaborations between practitioners, researchers, and analytics experts.

scholarly journals Mathematical Issues in Data Science and Applications for Health care

2019 ◽  
Author(s):  
Sitti Zuhaerah Thalhah ◽  
Mohammad Tohir ◽  
Phong Thanh Nguyen ◽  
K. Shankar ◽  
Robbi Rahim
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Computational Techniques ◽  
Healthcare Applications ◽  
Use Of Data ◽  
Source Data ◽  
Military Applications

For development in military applications, industrial and government the predictive analytics and decision models have long been cornerstones. In modern healthcare system technologies and big data analytics and modeling of multi-source data system play an increasingly important role. Into mathematical models in these domains various problems arising that can be formulated, by using computational techniques, sophisticated optimization and decision analysis it can be analyzed. This paper studies the use of data science in healthcare applications and the mathematical issues in data science.

Robust Data-Driven Machine-Learning Models for Subsurface Applications: Are We There Yet?

2021 ◽  
Vol 73 (03) ◽  
pp. 25-30
Author(s):  
Srikanta Mishra ◽  
Jared Schuetter ◽  
Akhil Datta-Gupta ◽  
Grant Bromhal
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Oil And Gas ◽  
Model Building ◽  
Model Updating ◽  
Predictive Analytics ◽  
Energy Resources ◽  
Data Driven ◽  
Classical Statistics ◽  
Prescriptive Analytics

Algorithms are taking over the world, or so we are led to believe, given their growing pervasiveness in multiple fields of human endeavor such as consumer marketing, finance, design and manufacturing, health care, politics, sports, etc. The focus of this article is to examine where things stand in regard to the application of these techniques for managing subsurface energy resources in domains such as conventional and unconventional oil and gas, geologic carbon sequestration, and geothermal energy. It is useful to start with some definitions to establish a common vocabulary. Data analytics (DA)—Sophisticated data collection and analysis to understand and model hidden patterns and relationships in complex, multivariate data sets Machine learning (ML)—Building a model between predictors and response, where an algorithm (often a black box) is used to infer the underlying input/output relationship from the data Artificial intelligence (AI)—Applying a predictive model with new data to make decisions without human intervention (and with the possibility of feedback for model updating) Thus, DA can be thought of as a broad framework that helps determine what happened (descriptive analytics), why it happened (diagnostic analytics), what will happen (predictive analytics), or how can we make something happen (prescriptive analytics) (Sankaran et al. 2019). Although DA is built upon a foundation of classical statistics and optimization, it has increasingly come to rely upon ML, especially for predictive and prescriptive analytics (Donoho 2017). While the terms DA, ML, and AI are often used interchangeably, it is important to recognize that ML is basically a subset of DA and a core enabling element of the broader application for the decision-making construct that is AI. In recent years, there has been a proliferation in studies using ML for predictive analytics in the context of subsurface energy resources. Consider how the number of papers on ML in the OnePetro database has been increasing exponentially since 1990 (Fig. 1). These trends are also reflected in the number of technical sessions devoted to ML/AI topics in conferences organized by SPE, AAPG, and SEG among others; as wells as books targeted to practitioners in these professions (Holdaway 2014; Mishra and Datta-Gupta 2017; Mohaghegh 2017; Misra et al. 2019). Given these high levels of activity, our goal is to provide some observations and recommendations on the practice of data-driven model building using ML techniques. The observations are motivated by our belief that some geoscientists and petroleum engineers may be jumping the gun by applying these techniques in an ad hoc manner without any foundational understanding, whereas others may be holding off on using these methods because they do not have any formal ML training and could benefit from some concrete advice on the subject. The recommendations are conditioned by our experience in applying both conventional statistical modeling and data analytics approaches to practical problems.

Business Analytics and Big Data

2015 ◽  
pp. 1-11 ◽  
Author(s):  
Dennis T. Kennedy ◽  
Dennis M. Crossen ◽  
Kathryn A. Szabat
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Quantitative Methods ◽  
Business Processes ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Database Systems ◽  
Three Dimensions ◽  
Business Analytics ◽  
The People

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.

An Introduction to Data Analytics

2017 ◽  
pp. 1-14 ◽  
Author(s):  
A. Sheik Abdullah ◽  
S. Selvakumar ◽  
A. M. Abirami
Keyword(s):  
Social Media ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Analytical Techniques ◽  
Educational Institutions ◽  
Social Media Analytics ◽  
Text Analytics ◽  
Raw Data ◽  
Industrial Organizations ◽  
Prescriptive Analytics

Data analytics mainly deals with the science of examining and investigating raw data to derive useful patterns and inference. Data analytics has been deployed in many of the industries to make decisions at proper levels. It focuses upon the assumption and evaluation of the method with the intention of deriving a conclusion at various levels. Various types of data analytical techniques such as predictive analytics, prescriptive analytics, descriptive analytics, text analytics, and social media analytics are used by industrial organizations, educational institutions and by government associations. This context mainly focuses towards the illustration of contextual examples for various types of analytical techniques and its applications.

scholarly journals Big Data, Tacit Knowledge and Organizational Competitiveness

2013 ◽  
Vol 3 (3) ◽  
Author(s):  
Nowshade Kabir ◽  
Elias Carayannis
Keyword(s):  
Big Data ◽  
Tacit Knowledge ◽  
Business Processes ◽  
Business Analytics ◽  
Management Support ◽  
Business Organizations ◽  
Knowledge Based ◽  
Innovative Methods ◽  
And Performance ◽  
The Right

In the process of conducting everyday business, organizations generate and gather a large number of information about their customers, suppliers, competitors, processes, operations, routines and procedures. They also capture communication data from mobile devices, instruments, tools, machines and transmissions. Much of this data possesses an enormous amount of valuable knowledge, exploitation of which could yield economic benefit. Many organizations are taking advantage of business analytics and intelligence solutions to help them find new insights in their business processes and performance. For companies, however, it is still a nascent area, and many of them understand that there are more knowledge and insights that can be extracted from available big data using creativity, recombination and innovative methods, apply it to new knowledge creation and produce substantial value. This has created a need for finding a suitable approach in the firm’s big data related strategy. In this paper, the authors concur that big data is indeed a source of firm’s competitive advantage and consider that it is essential to have the right combination of people, tool and data along with management support and data‐oriented culture to gain competitiveness from big data. However, the authors also argue that organizations should consider the knowledge hidden in the big data as tacit knowledge and they should take advantage of the cumulative experience garnered by the companies and studies done so far by the scholars in this sphere from knowledge management perspective. Based on this idea, a big data oriented framework of organizational knowledge‐based strategy is proposed here.

Emerging IT Technologies for Accounting and Auditing Practice

2020 ◽  
Vol 18 (160) ◽  
pp. 731-751
Author(s):  
Lavinia Mihaela CRISTEA ◽  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Analytics ◽  
Research Method ◽  
New Technologies ◽  
Predictive Analytics ◽  
Interesting Case ◽  
Prescriptive Analytics ◽  
Financial Audit ◽  
Big Four

The IT impact can be noticed in all activity fields of this world, and the audit is no exception from the evolution of this technological trend. Motivation: Given that professionals are progressively pursuing experimentation in working with new technologies, the development of Artificial Intelligence (AI), Blockchain, RPA, Machine Learning through the Deep Learning subset is a particularly interesting case, on which the researcher argues for debate. The objective of the article is to present the latest episode of the new technologies impact that outline the auditor profession, the methods and tools used. The quantitative, applied and technical research method allows the analysis of the emerging technologies impact, completing a previous specialized paper of the same author. The results of this paper propose the integration of AI, Blockchain, RPA, Deep Learning and predictive analytics in financial audit missions. The projections resulted from discussions with auditing and IT specialists from Big Four companies show how the technologies presented in this paper could be applied on concrete cases, facilitating current tasks. Machine Learning and Deep Learning would allow a development for prescriptive analytics, revolutionizing the data analytics process. Both the analysis of the literature and the conducted interviews admit AI as a business solution that contributes to the data analytics in an intelligent way, providing a foundation for the development of RPA.

scholarly journals Mathematical Issues in Data Science and Applications for Health care

2019 ◽  
Vol 8 (2S11) ◽  
pp. 4153-4156
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Computational Techniques ◽  
Healthcare Applications ◽  
Use Of Data ◽  
Source Data ◽  
Military Applications

For development in military applications, industrial and government the predictive analytics and decision models have long been cornerstones. In modern healthcare system technologies and big data analytics and modeling of multi-source data system play an increasingly important role. Into mathematical models in these domains various problems arising that can be formulated, by using computational techniques, sophisticated optimization and decision analysis it can be analyzed. This paper studies the use of data science in healthcare applications and the mathematical issues in data science

Business Analytics and Big Data

Big Data ◽  
2016 ◽  
pp. 1773-1783
Author(s):  
Dennis T. Kennedy ◽  
Dennis M. Crossen ◽  
Kathryn A. Szabat
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Quantitative Methods ◽  
Business Processes ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Database Systems ◽  
Three Dimensions ◽  
Business Analytics ◽  
The People

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.

Data Analytics on Restaurants

2018 ◽  
Vol 7 (2.18) ◽  
Author(s):  
Amratansh Sharma ◽  
Poovammal E. ◽  
Jugal Joshi
Keyword(s):  
Data Mining ◽  
Health Informatics ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Data Driven ◽  
Business Analytics ◽  
Digital Revolution ◽  
Data Mining Techniques ◽  
Data Driven Decisions

In the phase of today’s world when data is boosting in myriad amount and the information is on its peak due to digital revolution, it becomes very crucial to handle and manage the data and convert it into useful information so that it can be analyzed well and be productive enough. Whether, its health informatics, business analytics or any domain, data driven decisions are need to be taken which should be accurate and efficient enough to have fruitful impact for humanity and the society. Specifically, in this project the main aim is specifically narrowed down to perform analytics in the restaurant.  By observing various trends of data in different categorizations, we will be applying the predictive analytics, data mining techniques and algorithms to analyze the data and come out with innovative suggestions and predictions, which would help to ameliorate the profits of hotels and restaurants business and will serve the demands of customers.

scholarly journals Demystifying Prescriptive Analytics Frameworks and Techniques

2020 ◽  
Vol 9 (4) ◽  
pp. 1422-1427
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Mathematical Formulation ◽  
Big Data Analytics ◽  
Optimization Techniques ◽  
Research Management ◽  
Data Sets ◽  
Simulation Techniques ◽  
Prescriptive Analytics

Big data analytics refers often a very complex process to examine the large and varied data sets to provide the organization to take smarter decisions and better results. Big data analytics is a form of advanced analytics including predictive, prescriptive models and statistical algorithms. The prescriptive analytics is a later stage of the Big data analytics which is not just anticipating what the event will happen as in the predictive analytics but also suggests the decision options and consequences of the decision. The paper addresses the survey of prescriptive analytics and the importance of prescriptive analytics. The prescriptive analytics techniques and methods include machine learning, operation research/management science, optimization techniques, mathematical formulation, and simulation techniques and methods. The paper discusses the techniques and methods, frameworks, and domain applications of prescriptive analytics.

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