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Author: Jac Fitz-enz
Publisher: John Wiley & Sons
ISBN: 1118893670
Category : Business & Economics
Languages : en
Pages : 180
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Book Description
Create and run a human resource analytics project with confidence For any human resource professional that wants to harness the power of analytics, this essential resource answers the questions: "Where do I start?" and "What tools are available?" Predictive Analytics for Human Resources is designed to answer these and other vital questions. The book explains the basics of every business—the vision, the brand, and the culture, and shows how predictive analytics supports them. The authors put the focus on the fundamentals of predictability and include a framework of logical questions to help set up an analytic program or project, then follow up by offering a clear explanation of statistical applications. Predictive Analytics for Human Resources is a how-to guide filled with practical and targeted advice. The book starts with the basic idea of engaging in predictive analytics and walks through case simulations showing statistical examples. In addition, this important resource addresses the topics of internal coaching, mentoring, and sponsoring and includes information on how to recruit a sponsor. In the book, you'll find: A comprehensive guide to developing and implementing a human resource analytics project Illustrative examples that show how to go to market, develop a leadership model, and link it to financial targets through causal modeling Explanations of the ten steps required in building an analytics function How to add value through analysis of systems such as staffing, training, and retention For anyone who wants to launch an analytics project or program for HR, this complete guide provides the information and instruction to get started the right way.
Author: Jac Fitz-enz
Publisher: John Wiley & Sons
ISBN: 1118893670
Category : Business & Economics
Languages : en
Pages : 180
Get Book Here
Book Description
Create and run a human resource analytics project with confidence For any human resource professional that wants to harness the power of analytics, this essential resource answers the questions: "Where do I start?" and "What tools are available?" Predictive Analytics for Human Resources is designed to answer these and other vital questions. The book explains the basics of every business—the vision, the brand, and the culture, and shows how predictive analytics supports them. The authors put the focus on the fundamentals of predictability and include a framework of logical questions to help set up an analytic program or project, then follow up by offering a clear explanation of statistical applications. Predictive Analytics for Human Resources is a how-to guide filled with practical and targeted advice. The book starts with the basic idea of engaging in predictive analytics and walks through case simulations showing statistical examples. In addition, this important resource addresses the topics of internal coaching, mentoring, and sponsoring and includes information on how to recruit a sponsor. In the book, you'll find: A comprehensive guide to developing and implementing a human resource analytics project Illustrative examples that show how to go to market, develop a leadership model, and link it to financial targets through causal modeling Explanations of the ten steps required in building an analytics function How to add value through analysis of systems such as staffing, training, and retention For anyone who wants to launch an analytics project or program for HR, this complete guide provides the information and instruction to get started the right way.
Author: Shivinder Nijjer
Publisher: Routledge India
ISBN: 9781003026822
Category : Business & Economics
Languages : en
Pages : 216
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Book Description
"This volume is a step by step guide to implementing predictive data analytics in human resource management (HRM). It demonstrates how to apply and predict various HR outcomes which have an organizational impact, to aid in strategizing and better decision making. The book: Presents key concepts and expands on the need and role of HR analytics in business management; Utilises popular analytical tools like Artificial Neural Networks (ANN) and K-Nearest Neighbour (KNN) to provide practical demonstrations through R scripts for predicting turnover and applicant screening; Discusses real world corporate examples and employee data collected first-hand by the authors; Includes chapter-wise problem exercises and case studies for students and teachers. Comprehensive and accessible, this guide will be useful for students, teachers and researchers of data analytics, big data, human resource management, statistics, and economics. It will also be of interest to general readers unfamiliar with statistics or programming"--
Author: Dr Martin R. Edwards
Publisher: Kogan Page Publishers
ISBN: 0749484454
Category : Business & Economics
Languages : en
Pages : 537
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Book Description
HR metrics and organizational people-related data are an invaluable source of information from which to identify trends and patterns in order to make effective business decisions. But HR practitioners often lack the statistical and analytical know-how to fully harness the potential of this data. Predictive HR Analytics provides a clear, accessible framework for understanding and working with people analytics and advanced statistical techniques. Using the statistical package SPSS (with R syntax included), it takes readers step by step through worked examples, showing them how to carry out and interpret analyses of HR data in areas such as employee engagement, performance and turnover. Readers are shown how to use the results to enable them to develop effective evidence-based HR strategies. This second edition has been updated to include the latest material on machine learning, biased algorithms, data protection and GDPR considerations, a new example using survival analyses, and up-to-the-minute screenshots and examples with SPSS version 25. It is supported by a new appendix showing main R coding, and online resources consisting of SPSS and Excel data sets and R syntax with worked case study examples.
Author: Gene Pease
Publisher: John Wiley & Sons
ISBN: 1118466764
Category : Business & Economics
Languages : en
Pages : 261
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Book Description
An insightful look at the implementation of advanced analytics on human capital Human capital analytics, also known as human resources analytics or talent analytics, is the application of sophisticated data mining and business analytics techniques to human resources data. Human Capital Analytics provides an in-depth look at the science of human capital analytics, giving practical examples from case studies of companies applying analytics to their people decisions and providing a framework for using predictive analytics to optimize human capital investments. Written by Gene Pease, Boyce Byerly, and Jac Fitz-enz, widely regarded as the father of human capital Offers practical examples from case studies of companies applying analytics to their people decisions An in-depth discussion of tools needed to do the work, particularly focusing on multivariate analysis The challenge of human resources analytics is to identify what data should be captured and how to use the data to model and predict capabilities so the organization gets an optimal return on investment on its human capital. The goal of human capital analytics is to provide an organization with insights for effectively managing employees so that business goals can be reached quickly and efficiently. Written by human capital analytics specialists Gene Pease, Boyce Byerly, and Jac Fitz-enz, Human Capital Analytics provides essential action steps for implementation of advanced analytics on human capital.
Author: Srikanta Patnaik
Publisher: Springer Nature
ISBN: 9811393303
Category : Technology & Engineering
Languages : en
Pages : 398
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Book Description
This book discusses an emerging area in computer science, IT and management, i.e., decision sciences and management. It includes studies that employ various computing techniques like machine learning to generate insights from huge amounts of available data; and which explore decision-making for cross-platforms that contain heterogeneous data associated with complex assets; leadership; and team coordination. It also reveals the advantages of using decision sciences with management-oriented problems. The book includes a selection of the best papers presented at the International Conference on Decision Science and Management 2018 (ICDSM 2018), held at the Interscience Institute of Management and Technology (IIMT), Bhubaneswar, India.
Author: Jac FITZ-ENZ
Publisher: AMACOM
ISBN: 0814416446
Category : Business & Economics
Languages : en
Pages : 369
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Book Description
Using Fitz-enz’s proprietary analytic model, you will be equipped to measure and evaluate past and current returns and apply the information to make predictions about the future value of human capital investments. In his landmark book, The ROI of Human Capital, Jac Fitz-enz presented a system of powerful metrics for quantifying the contributions of individual employees to a company’s bottom line. Now, in The New HR Analytics, he reveals how human resources professionals can apply this expense-based knowledge to make the most strategic staffing decisions for their companies. You’ll learn how to: evaluate and prioritize the skills needed to sustain performance; build an agile workforce through flexible Capability Planning; determine how the organization can stimulate and reward behaviors that matter; apply a proven succession planning strategy that leverages employee engagement and drives top-line revenue growth; and recognize risks and formulate responses that avoid surprises. Brimming with real-world examples and input from thirty top HR practitioners and thought leaders as well as exclusive analytical tools, The New HR Analytics ushers in a new era in human resources and human capital management.
Author: Eric Siegel
Publisher: John Wiley & Sons
ISBN: 1119145686
Category : Business & Economics
Languages : en
Pages : 368
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Book Description
"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer "The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.
Author: Shonna D. Waters
Publisher:
ISBN: 9781586445324
Category : Business & Economics
Languages : en
Pages : 0
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Book Description
The need for HR professionals to understand and apply data analytics is greater than ever. Today's successful HR professionals must ask insightful questions, understand key terms, and intelligently apply data, but may lack a clear understanding of the many forms, types, applications, interpretations, and capabilities of HR analytics. HR Analytics provides a practical approach to using data to solve real HR challenges in organizations and demystifies analytics with clear guidelines and recommendations for making the business case, starting an HR analytics function, avoiding common pitfalls, presenting data through visualization and storytelling, and much more.
Author: Mong Shen Ng
Publisher: Independently Published
ISBN: 9781790406371
Category :
Languages : en
Pages : 417
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Book Description
You don't need to spend months learning the Python, R or SQL programming language, and you don't need to buy expensive statistical software like SPSS or SAS. This is the only book that teaches you Predictive Analytics using Microsoft Excel (which you already have & know how to use)! This book not only share with you the analytics findings of other companies, but also teaches you how to derive it by yourself! It covers the ARHAT Predictive HR Analytics framework, teaches you data-storytelling & data-visualization techniques, and teaches you how to use Microsoft Excel's statistical tools (Decision trees, Correlation, Multiple Regression, Logistic Regression, Chi-Square) with step-by-step print-screen instructions. It is also the only book that covers the full HR Analytics scope (Benefits, Compensation, Culture, Diversity & Inclusion, Engagement, Leadership, Learning & Development, Payroll, Personality Traits, Performance Management, Recruitment, Sales Incentives) with numerous real-world Predictive HR Analytics examples, & shows how Predictive HR Analytics answers questions such as: (1) Predict who are the people at risk of leaving using Decision tree, Correlation, Excel Logistic Regression, etc. (e.g. employee aged 30, who stays more than xx km from the company, who is rated "average for performance", has a 90% probability of resigning in her 3rd year.). (2) Identify where the best people come from and how successful a candidate will be if hired using simple correlation (E.g. Customer Service staff and Sales staff with x & y personality traits are likely to be good performers if hired). (3) Predict impact of Employee Engagement on customer satisfaction, revenue and Shareholder Returns, etc. using Excel Multiple Regression. (e.g. 1% increase in employee engagement leads to $100k increase in company revenue, 2% increase in customer satisfaction, 1% increase in Shareholders return, 1 day reduction in average sick leave, etc.). (4) Predict financial impact of training using Excel Multiple Regression (e.g. training satisfaction rating of xx leads to $y increase in company revenue). (5) Predict Diversity & Inclusion's impact on revenue and EBIT (e.g. convert your company's ethnic diversity mix to an index number, then use Excel Multiple Regression to predict if your company's diversity Index is x --> your company's Sales will be $y and EBIT will be z%). (6) Predict employee absenteeism and accident, using Chi-Square.
Author: Christopher M. Rosett
Publisher: Springer Nature
ISBN: 3030676269
Category : Psychology
Languages : en
Pages : 266
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Book Description
This book directly addresses the explosion of literature about leveraging analytics with employee data and how organizational psychologists and practitioners can harness new information to help guide positive change in the workplace. In order for today’s organizational psychologists to successfully work with their partners they must go beyond behavioral science into the realms of computing and business acumen. Similarly, today’s data scientists must appreciate the unique aspects of behavioral data and the special circumstances which surround HR data and HR systems. Finally, traditional HR professionals must become familiar with research methods, statistics, and data systems in order to collaborate with these new specialized partners and teams. Despite the increasing importance of this diversity of skill, many organizations are still unprepared to build teams with the comprehensive skills necessary to have high performing HR Analytics functions. And importantly, all these considerations are magnified by the introduction and acceleration of machine learning in HR. This book will serve as an introduction to these areas and provide guidance on building the connectivity across domains required to establish well-rounded skills for individuals and best practices for organizations when beginning to apply advanced analytics to workforce data. It will also introduce machine learning and where it fits within the larger HR Analytics framework by explaining many of its basic tenets and methodologies. By the end of the book, readers will understand the skills required to do advanced HR analytics well, as well as how to begin designing and applying machine learning within a larger human capital strategy.
