People Analytics

What it is, why you need it, and best practices. This guide provides definitions and practical advice to help you understand the role of analytics in HR and establish world class people analytics.

What Is People Analytics?

People analytics refers to the tools and processes used to analyze candidate and employee data to gain insights into the hiring, productivity, engagement, and retention of talent. Also known as HR analytics or talent analytics, people analytics helps HR leaders make better, data-driven decisions on talent, improve personnel processes, and evaluate the effectiveness of their HR initiatives. This results in improved business results and a more positive employee experience.

People Analytics Benefits

Your employees are most likely your organization’s most valuable asset. They also may be your biggest expense. So, insights which lead to better performance from your workforce can result in a significant positive impact on your business. Here are the key benefits of adopting modern talent analytics in your organization:

How People Analytics Works

Modern people analytics is powered by a cloud-based, end-to-end data integration and analytics platform. This platform helps you manage big data across its lifecycle as well as perform the necessary types of analytics.

A key aspect of people analytics is the application of statistics and modeling to candidate and employee data. This helps you to identify patterns and predict outcomes across the human-capital lifecycle, from hiring and managing performance to better retention.

Data Sources. Data is sourced from Human Resources Information System (HRIS), other HR surveys and systems, business systems, and productivity platforms.

  • HRIS such as Workday, Oracle, or SAP will provide data on recruiting, demographics, compensation, benefits, performance, learning, job architecture, development, succession planning, and exit interviews.

  • Other HR data such as surveys and siloed systems will provide data on employee travel, learning, mentoring, wellness, and absenteeism.

  • Business systems such as Salesforce or QuickBooks will provide data on sales, CRM, production, and financials.

  • Productivity platforms such as Outlook, Slack, and Microsoft Teams will provide behavioral data from employee’s daily digital activity such as e-mails, messaging, and posts.

Data Repository. All of these different types of data are extracted, transformed, and combined into a repository–typically a cloud data warehouse–to give you a comprehensive view of your workforce.

People Analytics Platform. Your platform makes it easy for you to use this data to perform different types of analysis. For example, you could use predictive analytics to project turnover rate in a sales division.

  • Your tool should allow you to create interactive visualizations and dashboards which help you identify patterns and develop insights.

  • Today’s top tools provide augmented analytics capabilities such as automated machine learning (AutoML), predictive analytics, and prescriptive analytics and to embed your analytics into other applications.

Outputs. These processes and tools result in insights which you can action on and/or they can trigger alerts and actions in other systems.

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See how to explore information and quickly gain insights.

  • Combine data from all your sources

  • Dig into KPI visualizations and dashboards

  • Get AI-generated insights

Types, Examples, and KPIs

4 Types of People Analytics

  1. Descriptive analytics summarizes historical trends in visualizations, interactive dashboards, and reports. It answers the question, What happened? or What is happening? For example, “What is the ratio of female employees?”

  2. Diagnostic analytics requires your intuition to develop hypotheses on what might be causing an issue. Then it makes it easier for you to explore and analyze the data to find patterns. It answers, Why did something happen? For example, “What is causing high attrition rates in Texas?”

  3. Predictive analytics uses statistical models to identify patterns in your data to project the probability of outcomes or forecast trends based on current and/or historical data. It answers, What will happen? For example, “What will the absenteeism rate be next month?”

  4. Prescriptive analytics uses advanced machine learning to analyze data and recommend the optimal course of action or strategy moving forward. It answers, What should we do? For example, “What’s the optimal compensation package to retain top performers?”

Examples of People Analytics

Using the types of analytics described above, here are examples of specific analysis you can conduct.

Example 1: Ethnic Diversity. This dashboard measures workforce diversity, breaking out ethnicity data by country and by salary. Modern, interactive dashboards allow you to dig deep into demographic data and analyze each variable.

Click the dashboard below to explore.

A workforce diversity KPI report shows demographic data to optimize for inclusion.

Example 2: Executive Dashboard
This example allows an HR executive to quickly review and analyze critical KPIs on one screen. Here you see key ratios such as training completion and employee satisfaction plus high-level KPIs like new hires and employees by role.Click the dashboard below to explore.

An executive HR dashboard provides all critical KPIs in one place and the ability to drill into the data

See more HR Analytics Dashboards.

People Analytics KPIs

Here are the top-10 workforce management KPIs:

  1. Absenteeism rate

  2. ROI of outsourcing

  3. Open/closed grievances

  4. Promotion rate

  5. Time to productivity

  6. Worker composition by gender, experience, and tenure

  7. Internal mobility

  8. Manager quality index

  9. Employee satisfaction rates

  10. Training ROI

See all 35 critical KPIs for recruitment, workforce management, and compensation.

Turn Knowledge Into People Power

Download the ebook with 6 use cases of modern people analytics.

Real-Time People Analytics

The approach begins with data integration to bring all your disparate sources together. The raw data is then transformed as it moves through the pipeline to deliver up-to-date information. Real-time alerting then brings actionable insights or triggers automatic, immediate actions in other applications.

Automation, artificial intelligence and machine learning eliminate painstaking human analysis, provide the ability to highlight potential issues before they happen, and raise pertinent questions (and answers) that haven’t yet been considered. The data is executed moment to moment and embedded directly into people and machine-driven processes. Not only do your teams know what is happening right now, they can understand what is likely to happen and they are alerted to take action when specific conditions are met.


Here are three key challenges to be aware of as you implement modern people analytics in your organization.

1. Data Management. You should make sure that all the relevant data you need is correct and up to date. And you need to bring all of this data together so that you can achieve full visibility of your workforce. The key challenge is that this data is usually in different formats and sits in a variety of siloed systems: HRIS, surveys, other HR systems, business systems, and productivity platforms. “Siloed'' means these systems don’t talk to one another. Find an analytics tool which includes the data integration capabilities you’ll need.

2. Legal and Compliance. You should be keenly aware of the risk of abuse. People analytics deals with sensitive information such as demographic, health and behavioral data which could be used to discriminate against employees. Work with your legal team to define clear boundaries of data access and use cases and continually ensure compliance with these guidelines.

3. Being Solely Data Driven. While data insights usually result in better decisions, when dealing with employees, you should always balance what the data is telling you with what the broader context and what your human intuition is telling you. Here are some pitfalls that can result from being solely data driven:

  • Employees may be concerned about data collection and analysis. They may feel like they’re being treated like a number and wonder whether talent analytics is being used against them.

  • Patterns of biased behaviors, like hiring or promoting based on race or gender, may be picked up and continued by AI.

  • Evaluating employees based on a limited set of metrics won’t accurately predict success. There are aspects of performance which can’t easily be captured quantitatively, and employees may learn to “game the system” by finding alternative ways to achieve goals.

How to Get Started

Below are the four key steps for implementing talent analytics in your organization.

  1. Define your questions. Determine which question(s) you seek to answer. This also involves identifying key stakeholders and understanding their requirements.

  2. Build your business case. Demonstrate how an investment in data analytics will drive value for the organization. Find other leaders within your organization who have developed analytics capabilities and have them help you make your case.

  3. Identify and bring together your data sources. Integrate data from HRIS, other HR surveys and systems, business systems, and productivity platforms into a data repository. This involves extracting, transforming, and combining all of these different types of data into a repository. Be sure to explore data profiles and lineage to help reconcile employee data between systems.

  4. Select and set up your people analytics platform. Ideally, the tool you select will be an end-to-end data integration and analytics cloud platform. It should also make it easy for you to create interactive visualizations and dashboards and it should provide augmented analytics capabilities such as automated machine learning, predictive analytics, and prescriptive analytics. The best tools support real-time data analytics as described above.

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