Analyzing data about people and using that information to make informed, people-related decisions instead of relying on gut instinct makes a big difference.

This practice, which is increasing in popularity, is called people analytics.

Want to know how to apply people analytics on any budget?

Having practiced people analytics myself for a few years, I went back into the textbooks for this article.

In this guide, I will cover all the people analytics essentials, even if you are just starting out on this journey:

• What is people analytics?
• Why is it important to apply people analytics?
• How does people analytics work?
• How do you use people analytics to make decisions?
• How do you apply people analytics to your business?

What is people analytics?

There has been a lot of talk about 'people analytics' in recent years. 

With more and more people working remotely and traditional processes going digital, we can now gather and analyze huge amounts of data. 

Surveys can be conducted easily thanks to free and easy-to-use employee survey tools and other employee management software.

So, what is people analytics? People analytics is about collecting and analyzing data about your people to improve your employee experience and business outcomes. 

Smart managers take advantage of people analytics by collecting data and using it effectively to make informed decisions. 

This can help improve your ability to be managing remote teams, embrace employee engagement best practices, and retain top talent.

How does people analytics work?

People analytics involves collecting data from digital HR tools and using algorithms to create charts and graphs for informed talent decisions.  

According to Vizier, People Analytics consists of three components: data, analytics, and insights.

• People data includes all information collected about employees. 
• Analytics processes and organizes the data, creating relationships between data points. 
• Insights present the processed data in a consumable way through charts, graphs, questions, answers, and guidebooks, allowing users to analyze data on their own.

Benefits of people analytics

At first glance, "people analytics" may sound like something only HR teams get involved in, but managers can and should get involved. 

At a high level, people analytics, when used correctly and strategically, can help teams and companies:

Find and attract top talent

People Analytics can help find and attract top talent by leveraging data-driven insights to identify the most effective recruitment strategies, assess candidate fit, and optimize the hiring process. 

This includes using data to refine job descriptions, target recruitment channels, and evaluate the success of different sourcing methods. 

Engage your stars

From your regular discussions with team members, like by asking one-on-one questions, how well do you understand the underlying sentiment of your team and know what is happening? 

Let the data tell you the truth! 

Analyzing the satisfaction and engagement levels and proactively untangling the knots can help keep everyone on your team happy.

Retain your people

Similarly, continuous listening and data analysis will also help you retain your best people. Besides knowing how to improve the work experience for your team, you can also avoid getting an unexpected resignation letter.

Identifying early warning signs and promptly addressing issues can help you avoid unwanted exits.  

Boost individual performance and growth.

Supporting employee growth is a continuous journey; as a manager, you play a crucial role in their success. 

People analytics can provide valuable insights to evaluate individual performance, pinpoint improvement areas, and identify career development opportunities.

In short, People Analytics can give you data-driven insights, which can be used to improve decision-making across all aspects of a team, including promotion, performance, productivity, employee engagement, and resource allocation. 

To gather valuable insights, intentional planning for employee surveys is necessary, or resources available within the organization can be utilized. You can also work with employee engagement survey providers to get the needed data.

People analytics case studies 

To better understand the value of people analytics, let's explore some inspiring case studies from companies like Microsoft and Google and learn how they use people analytics. 

Microsoft: Listening to employees’ voices

Microsoft uses its Daily Pulse Survey platform to capture "what their people are thinking" and gather feedback through quantitative, qualitative, and open-ended questions. 

The information gathered from a daily employee pulse survey enables Microsoft to provide the necessary support and know "what is going on" with their employees. 

Microsoft did this, for example, during the shift to remote work, to get real-time insights into how to improve the remote employee experience.

From there, they make confident decisions based on current insights and effectively address employee challenges. They are also launching a manager’s dashboard, where each team leader can get practical insight into their team.

According to Microsoft’s Head of People Analytics, Dawn Klinghoffer, effective people management goes beyond viewing employees as a cost to the business. It leads to innovation, business impact, and growth opportunities.

Takeaway: Microsoft's case highlights the importance of using real-time data to understand and support employees. 

Using employee survey tools that collect continuous feedback, you can stay proactive and address issues promptly. 

You can do this without any need to involve HR or leadership: many of these tools are free and easy to use for managers. 

Google: Reinventing the art of management

The use of people analytics in decision-making was pioneered by Google. 

As Prasad Setty, the former VP of Google People Analytics, points out, analytics can help us better decision makers, especially about important matters like performance and promotions that directly impact people’s lives.

A notable project Google undertook was Project Oxygen, which focused on identifying the key components of effective management.

In this project, Google analyzed the data and figured out the ten behaviors of good managers. 

Based on the insights derived from Oxygen, Google designed personalized training programs to tailor to the specific needs of their managers.

Takeaway: Everyone has their own strengths; you can identify the components of each team member and design personalized training and development programs to enhance their skills and competencies.

How to get started with people analytics:

Ready and excited to become more data-driven?

To effectively leverage people analytics, it's crucial to start by defining clear goals, gathering the right data, and analyzing it for insights that can improve your decision-making. 

Finally, you’ll want to take action and keep measuring to see if it was the right decision. 

1. Set your purpose

Before embarking on any data collection or analysis, take the time to identify specific areas where you want to improve your team's performance. 

Examples might include increasing employee engagement, reducing turnover, or enhancing overall productivity. So, as Simon Sinek says, always “start with why.”

By clearly understanding what you hope to achieve, you can tailor your data approach accordingly and ensure you're collecting the right information to make informed decisions.

2. Gather the data

Once you've defined your goals, the next step is to gather data from surveys you run and other available data sources. 

It's important to focus on highly relevant data for your specific goals. 

By honing in on the information that directly impacts your objectives, you'll be better equipped to make data-driven decisions that have a meaningful impact on your team.

You may have heard the phrase “shit in, shit out.” Well, nowhere is that more true than in People Analytics.

Data sources such as pulse surveys, employee engagement surveys, performance management tools, and perhaps even HR systems provide valuable insights into employee demographics, performance, engagement, skills, and more.

3. Analyze the data 

To make sense of your data, it's important to transform it into visual representations like charts, graphs, and dashboards. 

Great tools are available, ranging from basic analysis tools like Excel or Google Sheets to more advanced data visualization software. 

By creating visualizations, you can more easily identify patterns and trends in your data and communicate your findings with others.

4. Take action

Once you've analyzed your data and developed insights, the next step is to take action. 

Use your data-driven findings to develop action plans to help you achieve your goals. 

Monitor progress closely and adjust your approach as needed to ensure you continue progressing. 

You can continually improve your team's performance and achieve lasting success by consistently refining your approach based on feedback and outcomes.

Download the free Excel template for people analytics: [Link to the template] 

Using People Analytics Models

Establishing a well-defined process can help consistent and effective implementation when conducting people analytics initiatives. 

There are several people analytics models out there. We like Jonathan Ferrar’s Eight-Step Model for Purposeful Analytics, the Focus-Impact-Value mode, and the analytic value chain of Google.

Jonathan Ferrar’s Eight-Step Model for Purposeful Analytics

The Eight-Step Model for Purposeful Analytics - Source: Jonathan Ferrar

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Jonathan Ferrar’s "Eight Step Model for Purposeful Analytics" is designed to guide workforce analytics practitioners in conducting successful analytics projects. The model consists of eight sequential steps:

1. Frame Business Questions: Clearly define why you are undertaking the analytics exercise and the business reason.
2. Build Hypotheses: Formulate hypotheses that guide the data gathering and analysis, helping test beliefs about business issues' causes.
3. Gather Data: Identify relevant data for testing hypotheses and ensure data quality, making decisions about collecting new or existing data.
4. Conduct Analyses: Apply methodology and statistics to the data to test hypotheses and uncover patterns.
5. Reveal Insights: Workforce analysts must uncover insights from the data, as executives may be unable to derive pertinent insights themselves.
6. Determine Recommendations: Articulate clear recommendations based on insights to drive improvement.
7. Get Your Point Across: Communicate project outcomes effectively, using storytelling and visualizations to influence decision-making.
8. Implement and Evaluate: Ensure decisions are made, actions are formulated based on recommendations, and evaluate the project's value to the organization.

Ferrar emphasizes that following these eight steps in sequence is crucial for the success of analytics projects. This model was developed to help organizations achieve meaningful insights and improvements in their workforce analytics efforts.

David Green’s Focus–Impact–Value Model

In the "Focus–Impact–Value Model," David Green outlines a framework for effective HR and workforce analytics. 

This model is designed to guide HR professionals and analysts in their analytics initiatives. It consists of three key components:

1. Focus: This initial phase emphasizes the importance of identifying a clear focus for your analytics efforts. It involves defining specific HR or workforce-related questions or challenges you want to address.
   
   Having a well-defined focus ensures that your analytics work is aligned with the organization's strategic goals and that you're addressing the most pressing issues.
   
   
2. Impact: In this phase, you aim to make a significant impact with your analytics work. This involves conducting analyses and gathering data to address the questions or challenges defined in the "Focus" phase.
   
   The goal is to generate valuable insights and actionable recommendations to drive positive organizational changes. It's about translating data into meaningful actions.
   
   
3. Value: The final phase focuses on demonstrating the value of your analytics efforts.
   
   It involves effectively communicating the insights and recommendations to key stakeholders, such as executives and decision-makers.
   
   By showcasing your work's value and potential benefits, you can gain support for implementing changes and improvements based on the analytics findings.
   

David Green's "Focus–Impact–Value Model" emphasizes the importance of starting with a clear focus, creating a meaningful impact through data analysis, and then demonstrating the value of your analytics work to drive positive organizational changes. 

Google's Analytics Value Chain

Google is taking a different approach, focusing on a ‘value chain’ of people analytics. In Google’s words:

“Think of the analytics process as a value chain. Each step up the chain requires additional work but yields additional value. Moving up the analytics value chain from opinion to informed action requires a thoughtful approach to understanding, measuring, and analyzing the problem.”

According to Google, opinions are not inherently bad, but they should be informed by data to make a more convincing argument, especially when suggesting a course of action. 

For example, a statement like "Employees spend too much time on expense reports" may not be very useful or convincing without supporting data. 

However, data like "Employees spent over 100,000 hours on expense reports last year" is a more persuasive starting point for developing a solution. 

Platforms for People Analytics:

If your available data sources are outdated or insufficient, conducting surveys manually may be necessary. 

The good news is that many platforms handle the entire process and can save time while providing better insight. 

Some platforms worth considering are Culture Amp, Lattice, and OfficeVibe. 

Culture Amp

Culture Amp provides a complete solution for collecting employee feedback so you can use analytics to understand and improve employee engagement and team culture. 

It’s easy to use even for a single team but also serves entire enterprises.

Lattice

Lattice combines various performance management tools, allowing you to easily manage employee performance, provide feedback, and track progress using analytics.

OfficeVibe

Officevibe focuses on gathering employee feedback through pulse surveys and anonymous feedback, helping you understand employee sentiment and take action to improve engagement.

In Conclusion

We love data and believe that the future of management is heavily data-driven.

Through this article, we hope we could explain the importance of utilizing people analytics to boost your team's satisfaction, performance, and competencies. 

By relying on data-driven insights, you can make informed decisions that will unlock the full potential of your people. 

Data doesn't lie, so taking advantage of people analytics is a significant step forward in your quest to maximize your team's potential.

Q&A

Is people analytics part of HR?

While people analytics is a field often associated with human resources, individual managers can start their people analytics journey just as easily. 

People analytics is simply the use of data and analysis to gain insight into various aspects of the people in your team and improve your decision-making on important matters like performance, promotions, or firing. 

What is the difference between people analytics and HR analytics?

People analytics is a broader term that encompasses HR, total workforce data, and customer insights. 

At the same time, HR analytics focuses specifically on HR-related issues such as hiring, attrition, turnover, and employee engagement.

How do I get started with People Analytics? 

To start with people analytics, follow these steps:

1. Define your objectives, 2. Collect and dig into the right data; 3. Visualize your analysis, 4. Prepare a strategic action plan and storytelling.

What software can I use to analyze my People Data?

There are many options on the market, such as R, Python, Excel or Google Sheets, Power BI, Tableau, Visier, ChartHop, Orgnostic, One Model, or Crunchr.