The extraction of information from semi-structured text, such as resumes, has long been a challenge due to the diverse formatting styles and subjective content organization. Conventional solutions rely on specialized logic tailored for specific use cases. However, we propose a revolutionary approach leveraging structured Graphs, Natural Language Processing (NLP), and Deep Learning. By abstracting intricate logic into Graph structures, we transform raw data into a comprehensive Knowledge Graph. This innovative framework enables precise information extraction and sophisticated querying. We systematically construct dictionaries assigning skill weights, paving the way for nuanced talent analysis. Our system not only benefits job recruiters and curriculum designers but also empowers job seekers with targeted query-based filtering and ranking capabilities.

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.

Only nine per cent of chief human resources officers (CHROs) agree that their organisation is prepared for the future of work, according to Gartner, Inc. To drive future performance – of the organisation, employees and the community at large – senior HR leaders must focus on five areas of work. Brian Kropp, chief of research for the Gartner HR practice said tackling the next phase of work involves planning for and leveraging the changes in the way work gets done over the next decade, influenced by social, generational and technological shifts. "Rather than looking at the various aspects of work, like AI, the gig economy and the multigenerational workforce, in silos, HR leaders should focus on the big picture of what the future of work can and should look like in their organisation," he said. Data is increasingly used to make work-related decisions in talent acquisition and management and even workplace design.

IBM wants to keep its employees from quitting. And it's using artificial intelligence to do it. In a recent CNBC interview, CEO Ginni Rometty said that thanks to AI, the tech and consulting giant can now predict with 95% accuracy which employees are likely to leave in the next six months. The "proactive retention" tool -- which IBM uses internally but is also selling to clients -- analyzes thousands of pieces of data and then nudges managers toward which employees may be on their way out, telling them to "do something now so it never enters their mind," Rometty said. IBM's efforts to use AI to learn which employees might quit is one of the more high-profile recent examples of the way data science, "deep learning" and "predictive analytics" are increasingly infiltrating the traditionally low-tech human-resources department, arming personnel chiefs with more rigorous tools and hard data around the tricky art of managing people.

Diversity and inclusion are intrinsically linked to a company's business strategy. As more companies set audacious goals for the diversification of their workforce, talent analytics can support subjectivity with objective data-driven decisions that will make diversity goals a reality. There is no doubt that more diverse and inclusive workforces have a competitive advantage over those that do not. In fact, diversity and inclusion are intrinsically linked to a company's innovative strategy, enabling a more creative and agile organization. It is no wonder that one of Gartner's top 10 predictions for IT organizations in 2019 is that more than three-quarters of larger enterprises will set their diversity and inclusion goals toward a 2020-2022 timeline by the end of this year. As more companies set audacious goals for the diversification of their workforce, the spotlight will start to shift to their real-world metrics and whether they are actualizing their goals.