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, by Mong Shen Ng
Free Download , by Mong Shen Ng
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Product details
File Size: 33955 KB
Print Length: 424 pages
Page Numbers Source ISBN: 1790406374
Simultaneous Device Usage: Unlimited
Publication Date: November 26, 2018
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B07KWZ86DK
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Predictive analytics uses a variety of statistical techniques that analyze data ( facts) to make predictions about future events. Although data collection and analysis/feedback as a method or tool has been used in Management (especially in large multinational companies) for many years, the term “predictive analytics†itself was never used in the management literature until very recently, perhaps only four or five years ago. Refer to any book on Management Models or Key Management Ideas or Strategic Planning published in the 1990’s and you will not find this term at all. Indeed, it is possible to complete a degree or diploma in Business Management without any knowledge of predictive analytics as this topic was not in the curriculum. This is even more so in the field of Human Resource Management & Development!I remembered my frustration working as a Personnel Manager in the early 1980’s and 1990’s because I lack ( and do not know how to find) knowledge and skills to advice my company’s Top Management ( the decision makers) on how to analyse and predict trends and adapt to changes that would impact on the workforce. Even in those early days, I felt very strongly that the personnel manager (now called Human Resource Manager) should be some kind of “applied†social scientist, able to analyse social & psychological problems which accompany technical and economic changes. The HR profession has evolved since those early days. Since the publication of books by HR gurus like Dave Ulrich, the merger of Institute of Personnel Management & the Training & Development Institute to form the Chartered Institute of Personnel Development in the UK, ( developments forced in large part by the rapid social, technological & economic changes in society in which business operates) personnel has gone “centre stage†so to speak. HR professionals must become more strategic players in the organisation and they must evaluate their value as created in the eyes of ALL stakeholders (i.e. employees, managers, customers, investors etc.) focussing on what they can deliver. In short, the modern HR Manager must add value to the organisation or he will not survive!Here is where, I think, techniques like predictive analytics are indispensible addition to the HR professional’s†tool box.†This book, Predictive HR Analytics, written by a very experienced HR Practitioner in Singapore, is, as far as I am aware, the only book that covers the full spectrum of HR Analytics written in a jargon free and easy to understand way. Just glance at the Table of Contents and you will be astonished at how much material the author has covered in about 400 pages. The author does not assume expertise in statistical methods on the part of the reader, but takes him/her step-by-step on how to DO predictive analytics using various statistical techniques with the help of software tools. The author has amassed a huge amount of reference as well as case studies, almost all of which are from contemporary sources. Two chapters in particular, the chapter on Compensation Analytics and the chapter on Learning & Development Analytics , are worth the price of the book!The author admits, from his own survey, that in Singapore, less than 10% of HR Departments in organisations today adopts the techniques of predictive HR analytics on a regular basis in running their business. (I suspect the reluctance to do so is based on the understandable fear that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of the assumptions made.) But I am convinced that as more and more organizations adopt predictive analytics into decision-making processes business corporations, including their HR Departments, will be the main users of this model in the future. And one of the major “push†in this direction will be the “education†of our business and HR leaders on the usefulness of predictive analytics by pioneering thought leaders like the author of this book. Excellent book! I would give it a five star rating and recommend the book as essential reading for all HR professionals and Chief Executive Officers in all industries.-- Kenny Loh
In-depth and talks about the most useful statistical topics in HR analytics. I would recommend keeping this book on you at all times. Every detail is clearly explained and easy to understand. This book should be your go-to guide if you're hoping to get into HR analytics.
This is a excellent book which give wide cases on how to go about predictive HR Analytics and building on excel easily.
I found this book to be one of those resources that you will always keep on your desktop - both phycially and digitally. I addition to the positive comments made in other reviews, I was particularly impressed with the breadth of coverage of HR analytic topics, Engagement to Diversity & Inclusion to Compensation. Furthermore, the Data Visualization and Storytelling section offers a terrific checklist to complete before making a presentation or communicating with HR partners and clients.
The strength of this book is twofold. First, it provides an explanation of several mathematical methods used in analytics (correlation, linear regression, logistic regression and Chi-square) while showing you how to execute these using Excel. Second, the book is packed full of case studies that will leave you with many ideas of what you can study in your own company to generate improved business results.
While the basis for and explanation of the analytical tools are both well presented, the grammatical errors are distracting and the results provided to the analyses are not always accurate. I am uncertain if these issues stem from English not being the author's first/primary language or a lack of editorial knowledge and thoroughness. If you can get past the grammatical errors and accept the presentation of the results provided in the analytical examples as probable typographical matters (misplaced decimal points and the like) this is a thoroughly enjoyable and well constructed overview of the components and uses of predictive analytics.
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