The metaphorical data explosion of the current age has changed how organizations function. Today, with the right amounts of effort and investment in technology and expertise, business heads can take operational and other major decisions with greater success as they're based on cold statistics and empirical evidence instead of--as was the case in the days of yore--inadequately supported gut feelings and instinct. The application of business intelligence is also much more widespread now, with organizations in several sectors using machine learning, computer vision, NLP, IoT and other data-oriented technologies to collect, process and analyze the data collected before putting it to use in a variety of ways. An often-overlooked aspect of business intelligence is the need to incorporate a long-term data-driven culture at the workplace in a way that complex analytical terms and working mechanisms become second nature for all stakeholders in any organization. Only organizations with high data fluency can foster such a culture for the long term.

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.

In this section, we will learn about the course structure and the meaning of some key terms associated with HR Analytics.

Thinking of home as the new workplace has put most HR teams in a driver's seat when it comes to controlling and coordinating various aspects of business operations. From creating space online for smooth employee communications to managing challenges related to talent management (hiring, onboarding and attrition / RIFs), HR teams are using a mix of HR technology tools and platforms to effectively manage the organizational goals at the people's level. Like their peers in the Marketing, Sales, IT and Finance, HR teams too have a solid reason to leverage AI, Machine Learning, Automation and Data Science to upgrade their people analysis and enhance vision of how their various policies affect human capital management. With demands for personalization and DEI at an all-time high, HR teams have a solid reason to sought powerful analytics tools to create a purposeful workforce in the'new-normal' era of business operations. That's why we are focusing on the current definition of HR Analytics and the top HR analytics software makers available today for enterprise consumption.

Companies form HR departments to handle hiring and compensation. Soon, HR leaders find themselves tackling, retention, performance management, culture and a myriad of other responsibilities. And now CEOs are asking them: What's your AI strategy? The short answer is: AI will transform HR creating a lean department that is less intrusive yet more impactful. Software has eaten the world and now AI is eating software.

Data is a guiding light. Staring directly into it can blind us, but with it, we can see everything else. The facts and figures present in any given dataset tell stories, identify trends, and chart courses of action. But none of these come to fruition by the mere presence of information. Empirical evidence usually has this intended effect when those in possession of it commit to data analytics -- a critical foundational need in every company that wants to analyze data and use it to make decisions.

HR executives and organizational leaders are sure that merging HR analytics and HR functions like administration and onboarding with artificial intelligence can and will change the overall experience of the employees as well as the HR executives. In fact, company owners and CEOs of several companies are investing in merging cognitive computing with different functionalities of HR. They have high expectations for their success. The total expenditures of integrating artificial intelligence to HR analytics is expected to rise from $8 billion in the year 2016 to $47 billion in the year 2020. A recent survey by Information of Business Management has found that half of the recruiters, HR representatives and CEOs have already recognized and anticipated the future of HR analytics.

A critical arm of AI, machine learning makes technology truly intelligent, and capable of understanding human needs. We discuss it's role in HR and people-centric transformation. This isn't a new trend, but what's changed are the essentials. Human Resources have been using analytics for years; however, what's changed is the approach - the collection, processing, analysis of data was in the past, entirely manual, thereby constraining intelligence and interpretation. Therefore, in a way, it's surprising that HR teams are finally accepting the importance of machine learning and its transformative potential.

The heart of AI in HR lies in making well-informed choices concerning people, process, and technology. AI in HR analytics is one of the fastest growing areas in recruitment, and rightly so: AI can both automate and streamline the recruitment process. While AI can bring in 100% automation of candidate sourcing, screening, and matching, it can ameliorate the relations between candidates, recruiters, and hiring managers too. If you still want to know what more AI can bring to a recruiter's table, here you go: Planning a recruitment cycle is one of the most grueling responsibilities for any HR personnel. You go through a whole lot of data, statistics, and numbers from past hiring activities to craft a sound hiring plan for the future.

Employee turnover (attrition) is a major cost to an organization, and predicting turnover is at the forefront of needs of Human Resources (HR) in many organizations. Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. First, we'll use the h2o package's new FREE automatic machine learning algorithm, h2o.automl(), to develop a predictive model that is in the same ballpark as commercial products in terms of ML accuracy. Then we'll use the new lime package that enables breakdown of complex, black-box machine learning models into variable importance plots.