What is People Analytics?
People analytics is the use of employee and candidate's data to understand their impact on business goals and assess the effectiveness of hiring and HR initiatives.
There's a quiet revolution happening—one that involves understanding and leveraging data to enhance the way organizations manage their most valuable asset: their people. People Analytics is an emerging field, where the marriage of data science and human resources is reshaping the way businesses approach workforce management.
This guide isn't about complex theories or high-level strategies; it's a practical resource for those looking to navigate the world of People Analytics. Whether you're an HR professional seeking to enhance your contributions or a business leader aiming to optimize your workforce, we'll explore the basics of People Analytics and its applications across various HR functions.
What is People Analytics?
People analytics is the process of collecting and analyzing human resources data to gain insights that improve business outcomes. It is a data-driven approach to talent management that uses data to make better decisions about hiring, retention, performance management, and other people-related processes.
People analytics can be used to answer a wide range of questions about the workforce, such as:
- What are the key drivers of employee engagement?
- What are the most common reasons for employee turnover?
- What are the most effective ways to recruit and hire top talent?
- How can we improve the performance of our employees?
- How can we create a more diverse and inclusive workplace?
Difference Between People Analytics and HR Analytics
People analytics and HR analytics have been used synonymously in the past - but this fact holds no truth at all. People analytics and HR analytics differ across major characteristics and some of them are listed below.
|Primarily focused on HR functions and processes.
|Broader scope, encompassing the entire employee experience and organizational dynamics.
|Concentrates on HR-specific metrics and processes.
|Extends beyond traditional HR, including cultural aspects, collaboration, and organizational networks.
|Strategic vs. Operational
|Often associated with operational HR activities.
|Tends to have a more strategic orientation, aligning people-related insights with broader organizational goals.
|May have a limited focus on organizational culture.
|Emphasizes understanding and shaping organizational culture, employee experience, and social dynamics.
|Applies data analytics to streamline HR processes.
|Utilizes data-driven insights to inform strategic decision-making related to the entire workforce.
|Primarily addresses HR efficiency and compliance.
|Aims for a broader impact on organizational performance, effectiveness, and employee well-being.
Types of People Analytics
There are four main types of people analytics:
- Descriptive analytics: Descriptive analytics is the most basic type of people analytics. It involves collecting and analyzing data to understand what has happened in the past. For example, a company might use descriptive analytics to track employee turnover rates or to identify the most common reasons for employee complaints.
- Diagnostic analytics: Diagnostic analytics goes a step further than descriptive analytics by trying to understand why things have happened. For example, a company might use diagnostic analytics to identify the factors that contribute to employee turnover or to understand the causes of employee dissatisfaction.
- Predictive analytics: Predictive analytics uses historical data to predict what is likely to happen in the future. For example, a company might use predictive analytics to predict which employees are at risk of leaving the company or to identify which employees are likely to be successful in a particular role.
- Prescriptive analytics: Prescriptive analytics uses predictive analytics to recommend actions that organizations can take to improve their people outcomes. For example, a company might use prescriptive analytics to identify the best training and development programs to reduce employee turnover or to identify the most effective way to recruit and hire top talent.
Benefits of People Analytics
People Analytics offers a range of benefits for organizations by leveraging data to gain insights into their workforce. Here are some key advantages:
- Informed Decision-Making: Provides HR professionals and leaders with data-driven insights, enabling more informed and strategic decision-making across various aspects of workforce management.
- Talent Acquisition Optimization: Helps optimize the recruitment process by identifying effective sourcing channels, assessing candidate quality, and improving overall talent acquisition strategies.
- Employee Engagement Enhancement: Enables organizations to measure and improve employee engagement by identifying factors that contribute to job satisfaction, motivation, and overall well-being.
- Retention and Turnover Prediction: Predicts and identifies factors contributing to employee turnover, allowing organizations to implement targeted retention strategies and reduce talent attrition.
- Performance Management Improvement: Enhances performance management by providing insights into individual and team performance, facilitating data-driven evaluations, and identifying areas for improvement.
- Workforce Planning Efficiency: Facilitates efficient workforce planning by helping organizations understand current and future talent needs, ensuring alignment with business objectives.
- Strategic Succession Planning: Aids in identifying and developing talent within the organization for key roles, ensuring a smooth transition and continuity in leadership.
- Learning and Development Effectiveness: Optimizes training and development programs by assessing their impact on employee performance, identifying skill gaps, and tailoring learning initiatives to individual and organizational needs.
- Diversity and Inclusion Promotion: Supports diversity and inclusion efforts by providing insights into workforce demographics, tracking progress toward diversity goals, and identifying potential biases in HR processes.
- Cost Reduction and Efficiency: Helps organizations identify areas for cost reduction and operational efficiency by analyzing workforce data and optimizing resource allocation.
- Predictive Workforce Trends: Predicts future workforce trends, allowing organizations to proactively address challenges, anticipate skill gaps, and align resources with strategic goals.
- Cultural Alignment: Assists in understanding and shaping organizational culture, fostering an environment that aligns with the company's values and goals.
- Organizational Network Analysis (ONA): Provides insights into communication patterns, influence networks, and collaboration dynamics, facilitating better team structures and improved collaboration.
- Adaptability to Change: Enables organizations to adapt to changing market conditions and business environments by quickly adjusting their workforce strategies based on data-driven insights.
- Enhanced Employee Experience: Contributes to a positive employee experience by addressing pain points, improving work conditions, and tailoring HR practices to meet the needs and preferences of employees.
The Process of People Analytics
1. Define Objectives and Key Metrics
- Objective: Clearly define the goals of the People Analytics initiative. Identify specific HR or organizational challenges you aim to address, such as improving employee engagement, reducing turnover, or optimizing talent acquisition.
- Key Metrics: Determine the key performance indicators (KPIs) and metrics that align with your objectives, ensuring they are measurable and relevant to the organization's goals.
2. Data Collection
- Source Data: Collect relevant data from various sources, including HR systems, performance evaluations, employee surveys, and other relevant repositories.
- Clean and Prepare Data: Ensure the data is accurate, consistent, and free of errors. Clean and prepare the data for analysis by addressing missing values, outliers, and other data quality issues.
3. Data Analysis
- Descriptive Analysis: Begin with descriptive analytics to understand historical trends, patterns, and basic insights into the workforce.
- Diagnostic Analysis: Dig deeper to identify the root causes of specific trends or challenges, aiming to answer questions about why certain workforce patterns exist.
4. Predictive Modeling
- Build Models: Use statistical methods or machine learning algorithms to build predictive models. This step involves forecasting future trends, such as predicting employee turnover or identifying high-potential talent.
- Validate Models: Validate the accuracy and reliability of predictive models using historical data or testing against real-time data.
5. Prescriptive Analytics
- Recommend Actions: If applicable, move into prescriptive analytics to recommend specific actions or interventions based on the insights gained from predictive modeling.
- Scenario Planning: Explore different scenarios and strategies to understand the potential impact of different actions on workforce outcomes.
6. Interpretation and Insights
- Translate Findings: Interpret the results of the analysis in the context of organizational objectives. Clearly communicate the insights to key stakeholders, including HR professionals, department heads, and executives.
- Identify Opportunities: Identify opportunities for improvement, innovation, or intervention based on the data-driven insights.
7. Implementation of Insights
- Develop Action Plans: Collaborate with relevant teams to develop action plans based on the insights gained from the analysis.
- Implement Changes: Execute the action plans, which may involve changes to HR policies, talent development programs, or other aspects of workforce management.
8. Monitoring and Evaluation
- Track Progress: Implement mechanisms to monitor the impact of changes over time. Continuously track key metrics and KPIs to assess the effectiveness of interventions.
- Iterate and Improve: Based on ongoing monitoring, iterate and refine strategies to continuously improve workforce outcomes.
9. Ethical Considerations
- Privacy and Compliance: Ensure that People Analytics processes adhere to privacy regulations and ethical standards. Protect employee confidentiality and comply with relevant data protection laws.
10. Feedback Loop
- Continuous Improvement: Establish a feedback loop for continuous improvement. Regularly review the People Analytics process, update models, and refine approaches based on lessons learned and evolving organizational needs.
Here are some tips for implementing a successful people analytics process:
- Start by identifying the business questions that you want to answer with people analytics. This will help you to focus your data collection and analysis efforts.
- Collect data from a variety of sources to get a more complete picture of the workforce.
- Clean and prepare the data carefully to ensure that your analysis is accurate.
- Use a variety of statistical and analytical techniques to analyze the data.
- Interpret the results carefully and explain what they mean for the business.
- Communicate the results to the relevant stakeholders in a clear and concise way.
Examples of People Analytics
- Google: Google uses people analytics to improve its hiring process, performance management process, and employee engagement. For example, Google uses people analytics to identify the factors that contribute to employee turnover and to develop programs and interventions to reduce turnover.
- Walmart: Walmart uses people analytics to optimize its scheduling process, improve its training programs, and reduce employee turnover. For example, Walmart uses people analytics to identify the best times to schedule employees to work and to develop training programs that are tailored to the specific needs of its employees.
- Netflix: Netflix uses people analytics to improve its hiring process, performance management process, and employee engagement. For example, Netflix uses people analytics to identify the factors that contribute to employee turnover and to develop programs and interventions to reduce turnover.
People Analytics Trends
People analytics is a rapidly growing field, and there are a number of emerging trends that are worth paying attention to. Here are a few of the most important people analytics trends:
- Increased use of AI and machine learning: AI and machine learning are being used to automate many aspects of the people analytics process, from data collection and cleaning to analysis and visualization. This is making it easier for organizations of all sizes to use people analytics to improve their decision-making.
- Focus on predictive and prescriptive analytics: Organizations are increasingly using people analytics to predict future outcomes and to make more informed decisions about their people. For example, organizations are using people analytics to predict which employees are at risk of leaving the company and to identify which employees are most likely to be successful in a particular role.
- Emphasis on diversity, equity, and inclusion (DEI): Organizations are using people analytics to identify and address DEI gaps in their workforce. For example, organizations are using people analytics to identify areas where they are lacking in diversity and to develop programs and interventions to address those areas.
- Greater use of employee feedback: Organizations are increasingly using employee feedback to improve their people analytics practices. For example, organizations are using employee surveys to collect feedback on the employee experience and to identify areas where they can improve.
People Analytics Tools
There are a number of people analytics tools available that can help organizations of all sizes to collect, clean, analyze, and visualize people data. Some of the most popular people analytics tools include:
Visier is a cloud-based people analytics platform that offers a wide range of features, including data visualization, reporting, and predictive analytics.
Tableau is a data visualization platform that can be used to create interactive dashboards and reports. Tableau is often used in conjunction with other people analytics tools to visualize and analyze people data.
- Qlik Sense
Qlik Sense is another data visualization platform that can be used to create interactive dashboards and reports. Qlik Sense is often used in conjunction with other people analytics tools to visualize and analyze people data.
- Power BI
Power BI is a Microsoft business intelligence platform that offers a wide range of features, including data visualization, reporting, and predictive analytics. Power BI is often used to analyze people data in conjunction with other Microsoft products, such as Excel and Dynamics 365.
Lattice is a people analytics platform that focuses on helping organizations to improve their employee engagement. Lattice offers a variety of features, including employee surveys, performance reviews, and goal setting.
Workday is a cloud-based human capital management (HCM) system that includes a variety of people analytics features. Workday's people analytics features can be used to track employee performance, identify trends, and make better decisions about the workforce.
People Analytics provides a compass for HR professionals and organizational leaders, guiding them towards a deeper understanding of their workforce and paving the way for strategic initiatives that align with broader business objectives.
In a world where adaptability is key, People Analytics stands as a beacon for organizations seeking to stay agile, responsive, and people-centric. As we conclude this exploration, the call to action is clear: embrace the power of data, invest in analytics capabilities, and foster a culture where evidence-based decision-making becomes a cornerstone of workforce management.