What Is People Analytics, And Why Does It Matter?

By Mike Howard - July 25, 2019

What is people analytics? In a previous article we noted the subtle differences between HR analytics and people analytics. Essentially, HR analytics is about solving HR problems.

In contrast, the goal in people analytics is to go beyond just the observation of data trends to gain an understanding of what is actually driving employee behaviors, then using that information as the basis for data-driven decision making. Foundationally, it is about not only knowing your people, but also using that knowledge to find better ways to respond to business challenges. People analytics is therefore broader in scope than HR analytics, encompassing data trends from many sources, including those external to the business. While HR analytics focuses on solving HR problems, people analytics by definition focuses on solving business problems that may affect all dimensions of the organization.

In this article, we’ll examine why people analytics matters, and what a mature people analytics practice looks like. (If you’d rather watch than read, click on the video above.)

Why People Analytics Matters

Rather than relying on gut feelings or blindly trying different things to see what works, people analytics allows leaders to make more informed decisions with greater speed and confidence. Because those decisions are based on data, they have a much greater chance of success. At its essence, people analytics is about solving business problems.

While data about people in the workforce has been gathered and analyzed for a long time, it was mostly the province of a small group of HR analysts and industrial/organizational psychologists. The practice was limited by slow tools, offline analytics, small data storage capabilities, and disconnected databases.

But things have rapidly changed in the past few years. Technology advances have lowered the IT barriers to entry, tools are faster and better, and data storage capacity is nearly limitless. Most importantly, people analytics has become a priority for business. As a result, statisticians, engineers, data scientists, and computer scientists are getting involved, and capabilities in people analytics practice are rapidly advancing. HR has also become more data-savvy, thanks to the plethora of educational resources available online and the increased priority of having this skillset as an HR professional. As more and more companies up their people analytics game, failure to develop a people analytics course of action is becoming a competitive disadvantage.

People Analytics: The Role Of The Survey

Although people analytics can draw from a variety of data streams, including human resource information systems (HRIS), social media, and organizational network analysis (ONA), the survey remains the most critical source of information. Simply put, the best way to find out what people think is to ask them.

Surveys can give insight into what drives employees better than any passive data collection method, as well as highlight risks and opportunities. Survey content can also help leaders gain an understanding of all aspects of the employee experience: management, teamwork, inclusion, development, communication, and more. The survey allows the right questions to be asked of the right people at the right time—that data can then be examined in conjunction with other data sources to gain deeper insights.


Are you asking the right survey questions to support your people analytics practice? Find out in our free guide, Using Employee Survey Questions To Support A People Analytics Practice.

Building A People Analytics Practice

Building a people analytics practice is a journey; even the companies with the most sophisticated programs built them over time. To build a practice, it’s important to start with an inventory of the current state of your people data, and identify where your people analytics function—and your organization—are on the maturity models.

There are two maturity models to consider:

Talent Analytics Maturity

The talent analytics maturity model has four levels (Bersin and Associates, 2012):

  • Level 1: Operational reporting—This is a reactive model, where data is gathered when it must be, for compliance or other purposes. The main focus is on getting the data right and making sure it gets reported out on time.
  • Level 2: Advanced reporting—On this level, data collection is more proactive. There may be scheduled reports, but the data is still descriptive rather than prescriptive.
  • Level 3: Advanced analytics—This level starts to get into more sophisticated modelling, looking at the why rather than just the what.
  • Level 4: Predictive analytics—At this level, organizations can do predictive modelling, scenario planning, and attrition prediction.

Surveys have indicated that 86% of companies are at Level 1 or 2 on the talent analytics maturity scale; only 4% of companies have achieved Level 4 maturity.

Organizational Maturity

The organizational maturity model refers to the organization’s maturity in regard to people analytics practice. In other words, does the analytics function have the support of leadership? Is there acceptance of data analytics in the company’s decision making process?

In terms of organizational maturity, there are also four levels, which roughly correspond to the levels on the talent analytics maturity scale (Bersin, Deloitte Consulting LLP, 2017):

  • Level 1: Fragmented and unsupported—Data collection is sporadic and reactive. There is no plan or strategy for collecting and analyzing data.
  • Level 2: Consolidated and building—Data collection is more frequent and timely, and the focus is shifting to data as a single source of truth and enhancing data security and accuracy. Organizations at this level have dedicated people analytics leadership, who have some partnerships with executives.
  • Level 3: Accessible and utilized—Multiple data listening channels and advanced tools are used to collect data. The people analytics focus shifts from HR to the larger organization, to serve its strategies and goals. Reporting is done via self-service dashboards rather than waiting for reports to be generated and distributed. There is significant experimentation with new analysis and tools, and a larger centralized survey team. Everyone in HR is moderately data fluent.
  • Level 4: Institutionalized and business integrated—Advanced, real-time AI- aided tools are used to gather and analyze data. People analytics is integral to business and talent decisions and everyday work. There are multiple robust delivery mechanisms for reporting and analytics, and increased experimentation. Very few organizations—only around 2%—are at this level.

Characteristics Of Mature Organizations

People analytics maturity is not possible without data accuracy, security, consistency, and scalability.

  • The data doesn’t have to be perfect but it does have to be good enough to be able to trust the results.
  • Mature organizations generally have cross-functional data governance councils—these consist of HR, IT, and sometimes leaders or management, meeting and getting on the same page with regard to what the numbers mean and what the benchmarks and goals should be. Controls also must be put in place to ensure access is limited only to those who should have access.
  • Employees need to be able to trust that their data will be used for good purposes. There is so much data available today on employees that is identifiable at the individual level (survey data, network analysis data, etc.) that the organization needs to be careful in its use and how it communicates with employees about its use so as to foster a positive reputation of trust and of being beneficial to employees.
  • The systems used to deliver the analytics should be scalable so that managers, leaders, HR, and others who need access to data can get it via mechanisms such as dashboards rather than waiting for results to be delivered individually. This allows many people across the organization snapshot views of the current state of their department, location, team, or the organization as a whole, for example, on demand rather than waiting for an analyst to deliver it.
  • Mature organizations use multiple listening channels. These can include internal HR systems, internal and external social media, ONA, or other sources. They go beyond conducting annual surveys to include onboarding, exit, and pulse surveys to get a full picture of the employee experience.
  • Data literacy in HR is critical, as HR staff need to be able to understand and discuss the data intelligently. Mature people analytics teams are typically no larger than those at other levels, but they tend to be more diverse in expertise and structured to enable strong connections to the larger organization. In the mature organization, the people analytics team usually reports directly to the CHRO.


Ultimately, to reach maturity in people analytics, there needs to be broad support for using data to make decisions, starting at the highest levels of the organization and extending through all aspects of the business, including people decisions. This form of data driven decision making must be embedded in the company culture, which evolves over time, and leaders must demonstrate proficiency themselves and lead by example in their own decisions. In the mature people analytics practice, leaders, HR, and others in the company regard people data as a core asset, one that can provide a sustained advantage over their competitors. (Tweet this!)

Need help building a people analytics practice for your organization?

At Perceptyx, we believe that people analytics is the key to unlocking innovation and employee engagement. Our custom surveys, paired with our powerful people analytics platform, can help your company gain the insights you need to address your biggest challenges. Contact us to see how a purposeful people analytics strategy can give your company a competitive edge.

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Bersin and Associates (2012). Big data in human resources—Making it happen.
Retrieved from https://www.slideshare.net/jbersin/bigdata-in-human-resources-making-it-happen/36-Talent_Analytics_Maturity_Model_Level

Bersin, Deloitte Consulting LLP (2017). High-Impact leadership [PDF file].
Retrieved from https://www2.deloitte.com/content/dam/Deloitte/ca/Documents/audit/ca-audit-abm-scotia-high-impact-leadership.pdf

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