A case for more data-driven talent management
Employees are a company’s number one asset and we generally tend to undervalue them. The sustainability of an organization depends on the retention of its best employees and their motivation and satisfaction. To achieve this, a data-driven approach is to use People Analytics. People analysis collects and applies data to improve critical talent and business results. This enables HR leaders to develop data-driven insights to inform talent decisions, improve workforce processes, and promote positive employee experiences.
A McKinsey & Company The survey found an 80% increase in recruiting effectiveness and a 50% reduction in attrition rates across organizations that used people analytics. Organizations have also seen a 25% increase in business productivity.
This data-driven decision-making uses analytical, statistical, data science, data visualization, and machine learning techniques. Analytics in HR enables more strategic, data-driven decisions throughout the employee lifecycle. It enables better hiring decisions as well as effective management of employee retention.
A clear transition from prescriptive to predictive analytics enables organizations to deal effectively with the dynamism of their operating environment.
Different areas of people analysis
External labor market analysis
People analysis can help HR identify the appropriate labor market for different types of positions. This helps organizations determine employee salaries and how to position them within the company. It also helps them identify analytical and data science market trends and how organizations can participate in them. Finally, external labor market analysis helps to understand labor needs.
Workforce planning is about creating a process to identify the team members and talents required by the organization to achieve its business goals and objectives. Workforce planning takes into account current and future needs as well as succession planning. Responsibilities of the workforce planning function include proactive forecasting – headcount, employees with the right skills, when to make changes, when to make certain decisions, and the optimal cost to enable them.
A well-implemented human resource analysis practice can provide a comprehensive view of the company’s employees. This helps business leaders and HR managers better understand their workforce. The human resources analysis function can play a transformational role. It can provide insights on critical issues such as – who is leaving, which function is prone to higher attrition, the gender and salary gap, how can I improve internal mobility.
Compensation analysis focuses on optimizing labor cost to promote the development of results. It’s a great way for companies to grow as an employer brand that effectively communicates an attractive value proposition to employees. This helps in comparing salaries to understand where candidates stand against their peers and the market salary for similar positions. It also leads to equal pay and helps leaders track their business goals and determine if the Total Rewards program is meeting its goals.
Role of AI/ML in People Analytics
The idea of using data to understand and solve employee problems is not new. As described above, external talent market analysis, workforce planning, human resource analysis, and compensation analysis attempt to gather information about people. What has changed now is our ability to look at historical data holistically and generate predictive insights that accurately capture employee behavior.
The first phenomenon that fueled the use of AI/ML in HR is big data. Traditional HR information systems favored the capture of structured information such as – attributes that the candidate entered during the application process, employee compensation and rank, current and previous salary changes, etc. Data analysts then generate descriptive analyzes from this structured data. With the commercialization of Big Data and NoSQL technologies, it has become easier and more accessible for technologists to store unstructured and semi-structured information such as resumes, job descriptions, survey results, etc. This means HR has convenient access to information-rich information. new datasets.
The second driver of the increased adaptation of AI to HR is the precipitous interest of industry leaders. Business leaders have witnessed the transformative power of AI in other business functions such as sales, marketing, and supply chain. These encouraging results from previously implemented AI projects fostered an environment that helped HR teams experiment with AI to solve their employees’ challenges.
To strengthen the capabilities of AI to solve HR problems, we will discuss a few use cases.
A well-designed strategy for retaining the best employees is essential to the continued success of any organization. There are many reasons why companies strive to retain top performers; a Gallup survey pointed out that the cost of replacing an employee can range from half to twice the employee’s annual salary. In addition, a permanent employee embodies the culture, key values and relationships of the company. Apart from increasing the cost of replacing an employee, it also slows down the progress of a business.
A successful strategy must understand the attributes and conditions that contribute to employee attrition. AI and ML techniques can help organizations understand the latent variables at play, contributing to employee departures from the business. AI enables the organization to understand direct and indirect employee feedback signals and manage workplace issues. ML algorithms with good inference capabilities can help companies understand the exact factors or combination of factors driving attrition and can even help leaders organize trends by business unit, job category , employment level, etc.
Organizations conduct several surveys to better understand their workforce. Each survey has a different focus and attempts to understand different perspectives. Satisfaction surveys, engagement surveys, culture surveys, 360 degree feedback and exit surveys are some of the established surveys we come across in organizations. Almost all of these surveys ask employees a few questions and offer predefined options. They also have a text box, usually at the end, to allow comments to be made. Survey options selected by associates can serve as appropriate input variables for border ML experiments. For example, unfavorable responses defined by associates can be incorporated as inputs to ML models for the purpose of modeling churn. Natural language processing (NLP), a subfield of AI, can also understand and summarize associate sentiment from raw text. Techniques such as “topic modeling” and text summarization can help organizations understand the topics of discussion among associates. These identified themes can then be analyzed in context to develop strategic initiatives to assist associates.
Recruitment and talent management:
Identifying top talent is critical to the success of any organization. ML/AI techniques can be used in the recruitment process to scan, read and assess candidates. This process can be used to mimic the recruiter’s first screening pass. The idea behind AI-assisted recruitment is not to replace the human touch in recruitment, but to help the recruiter quickly identify the key characteristics that the candidate possesses. NLP can be used to summarize the skills of an organization, and this knowledge can be used effectively to understand the skills of the future.
The importance of human intervention
AI for HR is not the same as AI for self-driving cars or AI for products. AI, or any other technological innovation for that matter, should not be used to replace “the human” in the HR function. Although AI-based systems are remarkably successful in uncovering the latest trends, it is important to note that the insights generated are only as good as the data. Biased AI-based recruitment systems have in the past resulted in sexist decision-making processes. HR should ensure that appropriate governance measures are in place to proactively understand decisions made by AI systems.
This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives from the data science and analytics industry. To check if you are eligible for membership, please complete the form here.