The vast majority of businesses plan to increase their investments in data, analytics, and AI, yet many will fail to see a strong return on their investment.

Why will many fail? The reasons are countless, including a lack of clear objectives, poor data quality, inadequate technology infrastructure, insufficient talent and experience, integration challenges, resistance to change, and inadequate training.

By leveraging the power of data analytics and machine learning, HR professionals can gain deeper insights into employee behavior, predict future trends, and make evidence-based decisions. This approach enables a more strategic and personalized management style, from optimizing recruitment processes to tailoring employee development programs.

In this article, we explore the multifaceted ways in which data science is revolutionizing the HR domain, offering innovative solutions to traditional challenges and paving the way for a more data-driven, efficient, and employee-centric workplace.

The two main categories of machine learning are supervised and unsupervised learning. Supervised learning is about predicting outcomes. We train the model on past data—performance reviews, skills assessments, survey responses—and it learns patterns and trends.

The models are then used to predict future outcomes, like whether an employee is likely to succeed in a new role or which candidates possess the hidden potential to take your company to the next level.

Unsupervised learning isn’t about prediction, but rather segmentation or grouping. For example, by analyzing patterns in employee data such as performance metrics, engagement levels, skill sets, and personal interests, unsupervised learning algorithms can group employees into different clusters. These clusters can help HR to tailor specific training programs, team assignments, or career development plans that are more aligned with each group’s characteristics.

Both supervised and unsupervised learning empower HR professionals to move beyond gut instincts and delve into the depths of data. They become tools for smarter decision-making, optimized processes, and a future-proofed workforce.

Below we identify some of the powerful applications of Supervised Machine Learning that can shape the future of your organization, one algorithm at a time.

1. Candidate Identification: Resumes, job descriptions, social media profiles and past application data can be used to develop models to support candidate identification. This can include doing automated screening where models scan resumes and profiles for keywords, skills, and qualifications specific to the open position. This saves time and resources by focusing on relevant candidates. Models can also be used to diversify the talent pool by identifying qualified candidates who might not have applied through traditional channels, thus expanding the pool beyond those actively seeking new positions.
2. Fraud detection in recruiting: A complementary model for the candidate identification model is one in which fraudulent applications are identified. This type of model leverages the same information as applications such as resumes, job descriptions, social media profiles and past application data but also past fraud cases. The model scans for inconsistencies, keywords linked to known fraud, and discrepancies between stated qualifications and online information. This protects the organization from hiring unqualified candidates and potential legal risks.
3. Talent marketplace matching: This is an extension of the candidate identification modeling that matches internal talent to open positions. Models such as collaborative filtering and recommendation systems can use input data such as employee skills, experience, career aspirations, open positions, and past internal moves. These models develop recommendations of positions that best fit an employee’s skills, interests, and career goals. This increases employee engagement and internal mobility, and reduces external recruitment costs.
4. Predicting attrition: Past employee data such as performance, demographics, and engagement surveys can be inputs to models that predict the probability of attrition. There are a broad set of possible model structures for predicting attrition but, regardless of the model details, they seek to identify high-risk employees. Patterns linked to past resignations, like low performance, low engagement, specific demographics, or recent changes in job responsibilities, are likely to be strong predictors of attrition. These models are useful in that they can allow for proactive retention strategies. For example, HR can target these employees with personalized interventions like mentorship, increased career development opportunities, or addressing specific concerns identified by the model.
5. Predicting employment success/future job promotion: From a modeling point of view, the methodology for this modeling is very similar to predicting attrition, but the target variable is quite different. Using data like performance data, skills assessments, personality tests, promotion history, this model can predict the probability of promotion or achieving success. A well-crafted model could remove some of the bias and subjectivity associated with promotion decisions, ensuring those with the greatest potential are recognized and offered opportunities for advancement.
6. Compensation analysis: Leveraging salary data, job descriptions, employee demographics, location adjustments and benefits utilization data are inputs for models that are used to detect and address pay discrepancies: The model identifies potential discriminatory pay gaps based on gender, race, or other protected characteristics. This helps ensure fair compensation practices and avoids legal issues.

As with all modeling decisions, it is important to assess whether these models should be built internally or if externally developed software should be leveraged. That “build or buy” decision is a cost-benefit analysis, one that should be systematically explored.

Remember, these are just examples of supervised machine learning applications. The specific models and data used will vary depending on the organization and its needs. It’s crucial to ensure ethical considerations and transparency throughout the supervised machine learning implementation process in HR.

Howard Steven Friedman is a data scientist, health economist, and writer with decades of experience leading data modeling teams in the private sector, public sector, and academia.

Akshay Swaminathan leads the data science team at Cerebral and is a Knight-Hennessy scholar at Stanford University School of Medicine. Together they are authors of Winning with Data Science: A Handbook for Business Leaders (Columbia Business School Press).