Talent Intelligence vs People Analytics

What’s the difference between talent intelligence and people analytics? As I speak with people analytics leaders, HR tech vendors, and research analysts, this question comes up a lot. To help clarify the difference, I've developed a quick two-step trick, complete with real-life examples. Read on and let me know what you think.

Why is there confusion between people analytics and talent intelligence?

Since HR Tech 2022, talent intelligence (TI) has been on fire in the HR tech branding space. There was an incredible market valuation of a few TI companies that woke up the market, and since then there has been a “run on the brand”. This created a lot of noise and complications as companies that weren’t doing TI started calling themselves “TI vendors”.

But we’ve seen that “generative AI” has taken the crown for this latest hype cycle — meaning the pretenders are quickly changing their banners to #GAI and we’re seeing the true talent intelligence companies remaining. The core talent intelligence platforms seem to be Lightcast, TalentNeuron, Claro, SkyHive, Revelio, People Data Labs, Draup, Horsefly, and LinkedIn Talent Insights. They gather and generate labor market data, make sense of resume data, social profile data, government data, and job posting data, and make it available for use by teams who analyze the market.

We’ve also seen a rise in talent intelligence teams this past year that are distinct from people analytics (PA) teams. Toby Culshaw’s Talent Intelligence Collective is a distinct community with their own conference and conversations. I would suggest that it’s common to find folk who are part of both communities, but we’re starting to see the TI community come into its own and separate from the people analytics community. People analytics had similarly separated from IO psychology in the past 20 years. While there’s a lot of overlap and people participating in both communities, they’re becoming more distinct over time.

Lastly, I’ve heard one too many people just give up and say these words mean the same thing. I think that could be true for workforce analytics / HR analytics / people analytics, but talent intelligence is proving distinct.

Naming something is the first step to understanding it, so if we blur names, we blur our understanding. I’ve found two big tricks to defining these terms:

• Differentiating the function from the act
• Focusing on the second word

Trick 1: Differentiating the function from the act

The first step is to clarify whether we are referring to the business unit or department that performs the work (the function) or the act of doing the work itself. 

We often see people analytics as an umbrella term that encompasses workforce planning, people strategy, and sometimes even compensation and HR technology. This doesn't necessarily mean that all of these teams engage in the act of people analytics, but they are all part of the same function or business unit. 

Not knowing the difference between the function and the act of doing something can cause confusing, semantic arguments. The name of the function? Honestly, it doesn't matter. The name of the act? That's important because it helps us understand who is doing what, whether it needs to be centralized or decentralized, and to distinguish the work from the skills required to do it.

So when someone asks you what you think PA or TI are, make sure you clarify the function of the business unit vs the act of doing the work first.

Trick 2: Focusing on the second word

The second trick, and the most important one, is to ignore the first word and focus on the core meanings of “intelligence” and ”analytics” in the human resources space. Simply put:

Intelligence - the ability to gather and combine data about the world to support decisions

Analytics - the ability to make decisions based on insights from data

We now have real starting places for our definitions. Focusing on the second word highlights the true difference between “intelligence” and “analytics”. These are distinct words with unique contexts that they bring into conversations.

By bringing back the first words and ensuring that we stick to the definitions of the second word, we get a clear definition of the two spaces:

• The term talent intelligence refers to the act of gathering and combining data about the labor market and talent to inform decisions.
• The term people analytics refers to the process of making decisions based on insights from data we have about our people.

To illustrate how these definitions of talent intelligence and people analytics play out, let’s consider two examples:

1. Talent intelligence: A global company is planning to expand its operations to a new region. By leveraging talent intelligence, they can gather data about the local labor market, including the availability of skilled professionals, salary expectations, and competitor presence. This information helps the company make informed decisions about where to establish their new office and how to attract top talent.
2. People analytics: An organization is experiencing high employee turnover rates. Using people analytics, they can analyze workforce data to identify patterns and trends, such as which departments have the highest turnover and which employee demographics are most affected. Armed with this information, the organization can make better decisions and develop targeted retention strategies to improve employee satisfaction and reduce turnover.

These examples may still hold confusion, except in the largest organizations where a people analytics or talent intelligence function are both represented and responsible for distinct areas, but ideally this language can help define the space.

Taking this further

Let's take this thought exercise further. Another benefit of the approach of separating the first and second words is that it can be mapped onto a 2x2 matrix, which uncovers further insights.

Playing this out, there are people analytics (function) teams that do all four of these tasks. Some also do 3/4, 2/4, or 1/4 and some of these tasks are centralized by another team or just decentralized within the business unit still. Knowing which is which before digging into a conversation with someone on names and tasks is critical.

Unraveling the Mystery

By embracing these two handy tricks, we've successfully untangled the web of confusion surrounding talent intelligence and people analytics. Remember, the key is to differentiate between the function and the act, and to focus on the core meaning of the second word.

Talent intelligence solutions are all about gathering and combining data on the labor market and talent, while people analytics revolves around making decisions with the data we have about our people. With these distinctions in mind, we can avoid misunderstandings and promote effective communication in the HR and talent management world.

Our handy 2x2 grid further showcases the range of functions and acts that people analytics teams can perform, emphasizing the versatility and breadth of their work. By fostering a comprehensive understanding of talent intelligence and people analytics, organizations can better harness the power of their workforce data to drive informed decision-making and achieve their business objectives. So, go ahead and spread the word — it's time to put this newfound clarity to good use!

What are you doing for talent intelligence and people analytics?
Let's have a conversation.