HR Data Mining: Extracting Workforce Insights With a Purpose

You're sitting on a gold mine.

Every single one of your employee records has a story you can't read with traditional HR processes. 

Now, what if those very stories could tell you who will leave your organization next month or who has the right skills to take on a new role? HR data mining is exactly the process you use to turn these seemingly "worthless" data points from hidden gems into stellar strategies.

Getting started with data mining in HR also became easier. Join us as we explore how.

What is HR data mining?

HR data mining is a process that relies on automation to look at patterns, correlations, and trends in the employee-related data you already have. Think turnover rates, employee engagement, and other HR process metrics that will all add up so you can take proactive measures and address the "real" issues.

Now this is what you might be used to: 

Basic reporting that answers a single question, "What happened?". HR data mining goes deeper into your workforce metrics by asking better subsequent questions: "Why is that happening?" and "What's the most likely result from this information? 

Rather than just summarizing employee-related data like basic reporting does, data mining in human resources uses standard data mining concepts (i.e. classification, clustering, and association rule mining). You can then come in with artificial intelligence (AI) and machine learning (ML) methods to dig out further nuances out of this.

Techniques used in HR data mining

There are various techniques to use for predictive human resource analytics using data mining:

Classification

Classification refers to training an algorithm on historical employee records to place individuals into distinct classifications. Specifically, classification models will look at and analyze previous employees to classify who's likely to resign, who should be promoted, or who will likely fit a role best among all candidates.

Common algorithms to use with classification are decision trees, logistic regression, and support vector machines. This gives HR teams access to predictive analytics with action items to intervene whenever people challenges arise in the workforce.

Clustering

Clustering is a method of grouping employees by identifying natural patterns in their characteristics or behaviors without having assigned labels for the groupings. For example, HR may cluster (or place together) employees based on skills, job satisfaction, or work styles. It would then uncover previously hidden subgroups within their employee population. 

This could lead to improved targeted engagement strategies or individual learning interventions. There are plenty of clustering techniques, including k-means and hierarchical clustering to uncover differences in employee profiles and improve talent management. 

Association rules

This data mining method looks for interesting connections between variables of interest in employee data by comparing frequent itemsets and their probability of co-occurrence.

For example, association rule mining may reveal that employees who take certain training modules tend to have higher productivity. Likewise, it could show you that employees with flexible work arrangements tend to exhibit more engagement. These findings help you design policies and programs that take advantage of positive developments.

Using AI and machine learning in pattern recognition

Artificial intelligence and machine learning are musts today for data mining. They allow automated, scalable, and more accurate detection of patterns. 

As machine learning models are fed data over time, they continuously learn, identify, and reveal complex and non-linear relationships about the workforce. 

Deep learning can conduct more nuanced analyses of vast amounts of unstructured data, such as employee feedback, resumes, and performance reviews to find trends based on sentiment or skills gaps. Reinforcement learning categorical models can even simulate the consequences of executing HR strategies before we actually execute them to provide a clearer perspective of what the future could look like. 

Applications in the HR function

But here's what you've been waiting for: How can you use data mining for people operations in HR?

Turnover prediction

One of the best applications of HR data mining is predicting turnover before it happens. By conducting analyses using historic data like tenure, engagement scores, and performance ratings, HR teams can uncover which employees are at risk of leaving to prevent this. It’s a useful predictive tool that minimizes turnover and allows targeted retention strategies that increase workforce stability.

Skills mapping

Data mining allows organizations to build skills inventories for their employees. By mining employee records, training patterns, certifications, and performance data, you can map existing skills to future business needs. This lets you run strategic workforce planning, support internal mobility, and create custom learning and development programs.

Recruitment optimization

HR data mining also gives you access to innovative recruiting methods by analyzing large data sets from all applicant sources (e.g. application data, hiring sources, and predictive sources of success). 

It identifies the attributes of employees who’ve been successful hires, predicts lead time to fill roles, and uses your existing data to improve candidate sourcing and screening processes. The result? Significant recruitment costs and higher-quality hires.

Employee engagement

Data mining in HR provides employee engagement analysis with an evolution beyond static surveys by providing ongoing analysis of multiple dynamic data points. This includes feedback from pulse surveys, team communication methods, participation in recognition programs, and behavioral signals like how often employees log into learning platforms. 

HR practitioners can mine and correlate all of these data points. This is great for isolating causes of engagement and better understanding issues instead of minimizing their impact whenever they escalate. 

Performance insights

Performance management benefits just as much from data mining. Performance review data combined with quantified productivity data, peer feedback, and sentiment analysis on employee messages are perfect additions to the dataset. This broad base of data reveals highly nuanced bottlenecks in performance, skill gaps, and high-potential employees.

Tools and platforms that support data mining

The best combination of the right technology, data literacy, and statistical understanding helps HR practitioners unlock the undiscovered communications from data and make better, evidence-based decisions.

HRIS (Human Resource Information Systems)

HRISs are at the centre of employee data, capturing everything related to an employee, such as personal details, payroll, and performance. They allow HR to collect, collate, and plug data together, and that is usually the most important piece of information to mine HR data.

Workforce analytics platforms

Tableau and Power BI already allow HR teams to visualize complex data and create actionable insights through customizable dashboards and reports. This lets organizations understand the exact meaning behind mined data to make informed and data-driven decisions.

Visier extends this through a complete workforce analytics solution built for HR professionals. With a mix of data mining, predictive modeling, and artificial intelligence, HR teams get accurate insights on risks for turnover, skills gaps, recruitment effectiveness, and much more.

Synopsys used Visier to democratize access to people data, expanding from HR-only access to self-service analytics for leaders across the organization. They also used Visier's modeling capabilities, and in partnership with Finance, made predictions around time-to-hire by country, all of which advanced their workforce planning, budgeting, and hiring ROI. 

Added to this, you need a good mix of…

Skills for implementing data mining in HR

Traditionally, you needed a combination of skills to make data mining work within the HR function, including:

• Data literacy for understanding/interpreting data and effectively communicating the resulting insights

• Statistical knowledge for concepts such as regression, correlation, and probability

• Technical skills for proficiency in using data analytics tools, HRIS tools, and programming languages 

• Predictive analytics for mastering forecasting techniques

• And more!

With tools like Visier though, you can uncover powerful HR and business insights without the need for technical know-how.

Ethical and legal considerations

Often overlooked, the past years have brought up more laws and regulations that concern the ethical implications of collecting and working with sensitive employee personal data.

Guaranteeing transparency

Organizations need to move past just providing notice. Look into staying proactive to identify what employee data you're collecting and then explain:

1. Why you're collecting the data

2. How the analytics that emerge from the data processed are going to impact real-life outcomes

Besides analyzing employee data across your entire organization, transparency should be part of employee data analytics processes. 

That applies especially where AI/ML is transferring outcomes towards decisions that could likely impact employment. In this case, employee should have the right to know that automation or AI (as part of decision making) is impacting their career and how the outcomes will be reviewed to influence their career.

Getting active consent and choices

For starters, you'll need to get employee consent for collecting, processing, and using personal data. This includes setting up ways for team members to clearly opt in and opt out. That's a must when the employee data being collected goes further than the generalized HR functions.

In practice, all communication (e.g. emails, shared documents, contracts) should outline where employee data is used, how you'll work with it, and how your practices for handling data will evolve. Don't forget to add a mention of when and how they can withdraw their consent, without this posing a disadvantage to their employment experience.

Preventing bias and ensuring fairness

When not built or monitored carefully, algorithms and statistical models are prone to perpetuating bias. That's where humans come in to ensure regular bias examinations, maintain human oversight, and explain the decisions behind automated recommendations. This is what promotes fairness and saves your organization from discriminatory action.

Securing your data and ensuring legal compliance

Data protection involves both complying with laws or regulations and maintaining constant vigilance. 

Here, we're talking proper encryption, logging access to data, conducting regular security audits, and being prepared to counter a data breach or loss. Design and implement a privacy framework that can protect sensitive employee data and keep you compliant in rapidly changing regulatory environments.

Workforce intelligence can be different. Book a personalized demo with Visier to see firsthand how the platform can transform your HR strategy.

FAQs

What is the role of data mining in HR?

Data mining supports HR departments in finding patterns and trends across large employee data sets. This allows you to use analytics for more precise decisions about recruiting, retention, or workforce planning. It also helps transform raw HR data into thoughtful data insights, amplifying how effective talent management and your overall organizational strategy are.

Which HR problems can data mining solve?

With data mining, we can solve problems related to turnover, discover top talent acquisition sources, identify key drivers of high employee performance, assess workforce diversity or pay equity, and analyze training programs and strategic workforce planning. 

What tools are best for HR data mining?

One of the best tools for HR data mining is Visier. This extensive HR analytics platform integrates all your data sources and systems into a single interface. This all comes with AI-driven insights, pre-loaded metrics, and customizable dashboards that simplify making data-driven decisions for HR leaders.

Do HR teams need coding skills for data mining?

No. While code skills are always handy, tools like Visier are designed to allow human resource professionals to use sophisticated analytics and AI without having coding experience. The platform's simple interface and AI digital assistant Vee provide instant answers and automate complex analysis so you can focus on strategic decisions.

How can companies ensure ethical data mining?

With strong built-in data security and governance features, platforms like Visier ensure your company can protect sensitive workforce information and comply with data privacy regulations. You can also monitor ethical data mining by using dynamic role-based access controls and transparency in data usage.