People Analytics: Augmenting Horizon from Predictive Analytics to Prescriptive Analytics

2022 ◽  
pp. 145-153
Author(s):  
Anurag Singh ◽  
Hardeep Singh ◽  
Ashutosh Singh
Keyword(s):  
Predictive Analytics ◽  
Prescriptive Analytics

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.

Application of Analytics in Supply Chain Management from Industry and Academic Perspective

2021 ◽  
pp. 231971452110280
Author(s):  
Anup Kumar ◽  
Santosh Kumar Shrivastav ◽  
Sarbjit Singh Oberoi
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Data Availability ◽  
Chain Management ◽  
Prescriptive Analytics ◽  
Ict Adoption ◽  
Future Supply ◽  
Information And Communication

Information and communication technology (ICT) has been the backbone of businesses for some time now. ICT adoption has rendered data availability in vertically connected organizations. In recent times, the rise of analytics has paved the way to rethink the structure and working of vertically connected firms within a supply chain. Big data analytics (BDA) for instance, has become a prominent tool to analyse unstructured data. This study explores the application and benefit of various types of analytics such as descriptive analytics, predictive analytics and prescriptive analytics in the process of supply chain management (SCM). Additionally, the study also looks at ways by which analytics could integrally be included within the SCM curriculum in higher education to prepare future supply chain analyst. Notably, the article is positioned for bridging the gap between the use of analytics in SCM, both from academia and the industry perspective.

Coupled Learning Enabled Stochastic Programming with Endogenous Uncertainty

2021 ◽  
Author(s):  
Junyi Liu ◽  
Guangyu Li ◽  
Suvrajeet Sen
Keyword(s):  
Stochastic Programming ◽  
Predictive Analytics ◽  
Trust Region Method ◽  
Trust Region ◽  
Data Driven ◽  
Exploration And Exploitation ◽  
Prescriptive Analytics ◽  
Sequential Prediction ◽  
Data Driven Approach ◽  
Decision Dependent Uncertainty

Predictive analytics, empowered by machine learning, is usually followed by decision-making problems in prescriptive analytics. We extend the previous sequential prediction-optimization paradigm to a coupled scheme such that the prediction model can guide the decision problem to produce coordinated decisions yielding higher levels of performance. Specifically, for stochastic programming (SP) models with latently decision-dependent uncertainty, without any parametric assumption of the latent dependency, we develop a coupled learning enabled optimization (CLEO) algorithm in which the learning step of predicting the local dependency and the optimization step of computing a candidate decision are conducted interactively. The CLEO algorithm automatically balances the exploration and exploitation via the trust region method with active sampling. Under certain assumptions, we show that the sequence of solutions provided by CLEO converges to a directional stationary point of the original nonconvex and nonsmooth SP problem with probability 1. In addition, we present preliminary experimental results which demonstrate the computational potential of this data-driven approach.

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 A Predictive Prescription Using Minimum Volume k-Nearest Neighbor Enclosing Ellipsoid and Robust Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 119
Author(s):  
Shunichi Ohmori
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Robust Optimization ◽  
Nearest Neighbor ◽  
Predictive Analytics ◽  
Uncertain Parameters ◽  
Minimum Volume ◽  
K Nearest Neighbor ◽  
Modeling Framework ◽  
Prescriptive Analytics

This paper studies the integration of predictive and prescriptive analytics framework for deriving decision from data. Traditionally, in predictive analytics, the purpose is to derive prediction of unknown parameters from data using statistics and machine learning, and in prescriptive analytics, the purpose is to derive a decision from known parameters using optimization technology. These have been studied independently, but the effect of the prediction error in predictive analytics on the decision-making in prescriptive analytics has not been clarified. We propose a modeling framework that integrates machine learning and robust optimization. The proposed algorithm utilizes the k-nearest neighbor model to predict the distribution of uncertain parameters based on the observed auxiliary data. The enclosing minimum volume ellipsoid that contains k-nearest neighbors of is used to form the uncertainty set for the robust optimization formulation. We illustrate the data-driven decision-making framework and our novel robustness notion on a two-stage linear stochastic programming under uncertain parameters. The problem can be reduced to a convex programming, and thus can be solved to optimality very efficiently by the off-the-shelf solvers.

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.

Prescriptive Analytics Using Synthetic Information

2014 ◽  
pp. 1-19 ◽  
Author(s):  
Madhav V. Marathe ◽  
Henning S. Mortveit ◽  
Nidhi Parikh ◽  
Samarth Swarup
Keyword(s):  
Predictive Analytics ◽  
Washington Dc ◽  
Disaster Resilience ◽  
Multiple Sources ◽  
Combining Data ◽  
Prescriptive Analytics

In this chapter, the authors describe the use of synthetic information for doing prescriptive and predictive analytics. They discuss in detail how synthetic information is created by combining data from multiple sources and then describe its role in an ongoing disaster resilience study where they simulate the aftermath of a hypothetical nuclear detonation in Washington DC.

scholarly journals Advanced spatial analytics and management: models, methods and applications

2016 ◽  
Vol 14 (2) ◽  
pp. 67-73
Author(s):  
Sven Müller
Keyword(s):  
Spatial Econometrics ◽  
Predictive Analytics ◽  
Future Research ◽  
Discrete Choice Analysis ◽  
Mathematical Programs ◽  
Research Perspectives ◽  
Choice Analysis ◽  
Prescriptive Analytics ◽  
Spatial Analytics ◽  
Spatial Decision Making

In this contribution, a concept of the integration of spatial predictive analytics and mathematical programs for spatial decision making – namely, advanced spatial analytics and management – is outlined. In particular, selected methods for spatial predictive analytics are discussed, including spatial econometrics and discrete choice analysis. Then, the integration of spatial predictive models in mathematical programs (prescriptive analytics) for facility location and districting is demonstrated. The paper includes illustrative applications which stem from health care, retail, marketing, logistics, and transportation. Based on the discussion, future research perspectives are developed

scholarly journals Analytics of Specialized Data by Data Mining

2020 ◽  
pp. 169-173
Author(s):  
Gnanamurthy S ◽  
Vishnu Kumar Kaliappan
Keyword(s):  
Data Mining ◽  
Predictive Analytics ◽  
Analytical Techniques ◽  
Large Data ◽  
New Techniques ◽  
The Troubles ◽  
Clinical Dataset ◽  
Huge Data ◽  
Prescriptive Analytics ◽  
Special Information

In this paper, we are trying to discover Predictive Analytics by means of combining special information mining methods with large data. Predictive lookup consists of a number of mathematical and analytical techniques to increase new techniques for doable prediction possibilities. The paper also portrays the integration of Big Data characteristics as the foundation of Data Mining by Apache Hadoop's usefulness in achieving the above. With the resource of accessible statistics mining techniques, predictive analytics predicts the activities in future and can make tips referred to as prescriptive analytics. This evaluates paper offers clear thought to follow facts mining strategies and predictive analytics on distinctive clinical dataset to predict a variety of ailments with accuracy levels, execs and cons that conclude about the troubles of these algorithms and futuristic processes on huge data.

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