People analytics, or leveraging information to understand talent and maximize potential, is a key feature of future-ready organizations and it starts with the underlying data. Colin Anderson, Managing Director at Accenture observes, “People analytics is understanding all of the attributes of people, the ones that you can see and measure, and increasingly also the ones that you can't see as much, but you still have to get to. How are people feeling? How are people behaving? And I think the magic of people analytics is how you bring those things together.
COLLECTING THE INFORMATION: THE DATA BEHIND PEOPLE ANALYTICS
The data at the heart of people analytics comes from HR core data sets, measurable behaviors, and employees themselves.
Core HR Systems include tracking recruitment, roles, rewards, and promotions aligned with people’s skills, their pay, and their demographics. Measures of performance, development, and retention provide key data.
Digital Tools can detect important behaviors in an organization. For example, Anderson describes Microsoft Viva. “It can tell you that these people are collaborating with those people. These people are working this many hours a day. These people are having discussions in big groups or smaller groups. These people are taking breaks in their day for lunch or for focus time.” Anderson adds, “It’s not about tracking an individual, it's about detecting correlations, for example whether people who have smaller meetings are more agile in how they deliver their work.”
Active listening pulls essential information from people directly. “Everybody in the world is over surveyed,” Anderson says. “But imagine if every week you were asked just two questions, and different people were being asked different questions? What if it was more routine, more tailored, and more personalized? And if you've answered one thing one way, maybe you get a different set of questions the next week to really understand what is happening?”
PULLING IT TOGETHER: HOW MACHINE LEARNING AND HUMAN INSIGHTS MAKE UP PEOPLE ANALYTICS
Gaining meaningful insights from a data set requires both human and machine. Anderson explains, “People analytics can't be scientists in a lab, you need the human element. Neither side can work without the other.”
Machine learning looks at data and predicts future outcomes. “The problem with machine learning—because it relies on data in the past, it perpetuates the past,” Anderson says. “For example, machine learning will help you put more white men in executive roles looking forward, because in the past there's been more white men in executive roles.”
To mitigate that problem, Anderson says, “I'm not going to feed the machine all people, I'm going to feed it an equal mix of people that are different genders that have been successful, an equal mix of people that are different races that have been successful, an equal mix of people that come from different economic backgrounds that have been successful. If you feed the machine an equal mix of things, then all of a sudden you can get something really awesome.”
Human insights make data relevant and actionable. It takes a human to talk to people and understand how they are feeling, whether they feel engaged and why, and it takes a human to interpret the data in the real-life context of an organization. Anderson says, “While you're not going to set the machine up right now and unleash new insights, data and the analytics around data is critical and super powerful regardless. And it should complement instinct and experience.”
“That's it,” he says. “Human plus machine. Data plus knowledge. You have to bring it together.”
USING DATA TO THINK DIFFERENT: WHAT TO GAIN FROM PEOPLE ANALYTICS
What are some of the outcomes to be gained by using people analytics in decision-making?
Proof positive: People analytics will prove for an organization that diversity does create a more healthy business from a top and a bottom line standpoint. You can read it out there, and everybody says it. But it’s different when you see it in your own company.”
THE HIDDEN WORKER
Hidden workers, people who want to be fully employed but are not, are an overlooked pool of talent. They can be people who took time off, for example to raise a family. They can be people who have dropped out of the workforce but now seek work, or people who are working part time jobs but would prefer to work full-time.
Anderson says, “They almost always come from non-traditional backgrounds.” The number one variable driving a hidden worker is a break in service, which Anderson explains is made worse by technology in the form of automated candidate filtering and ranking. “Most recruiting systems spot that break in service and mark you down. But was that person raising their family? They were building tremendous skills, but you have to think different to acknowledge that. Formerly incarcerated people who have great experiences, great hustle, great grit, who messed up but want to get back in there, these people could be hugely valuable. Veterans? Older people? Again, there are lots of different pockets of people who show themselves in completely different ways.”
Help is wanted—desperately. “We're not going to solve it when 27 million people in the U.S. alone are in this category. These are people who want to work,” Anderson explains. “These are super-skilled, super-capable, super-ambitious people with a desire to work, and companies should challenge themselves to think different, act different, be different. Companies searching for talent can find great hidden talent—if they challenge themselves.
Innovative recruiting. At Accenture, “We've now taken 48% of our roles in the U.S. and removed four-year college degree requirements, because we basically found for those roles, there was no correlation between our high performers and their education,” Anderson explains.
More effective promotions: A lot of companies promote the most tenured person in an area, not necessarily the best people leaders. Anderson asks, “To lead an engineering team do you want the best engineer, or would you take an okay engineer that's a really inspiring leader? The data will show you every time the second is the better pick. Every time. Which, by the way, doesn't mean that the senior ‘best engineer’ can't become a super-duper senior engineer. It just means they shouldn't be the people leader.”
Providing confidence. “People don't make a change unless they have some confidence it won't create a problem, until somebody puts data in front of them. And this isn't big data, this isn't machine learning. This is just data-driven decision-making complementing instinct and experience.”
Early warning. “We have predictive attrition models and we can tell you with 80% confidence who's going to leave and why,” Anderson says. “Our ability to actually act on those things is much harder.” That comes down to human insight and experience. For example, Anderson says, “The way you inspire somebody in Japan, a very traditional workforce that has lifelong tenure in most places, the way you inspire that type of worker is totally different than somebody in India that has a much younger, tech-heavy workforce that doesn't value employment tenure the same way.” He adds, “Personalized experiences are really the name of the game. Because what do people want? Everybody's inspired by something different.”
People analytics is connecting the dots and looking forward. It's letting you know what's happening today and it's starting to predict what could happen tomorrow. Anderson says, “The data is there and the organization that is serious about getting it, those companies will get bolder in their decision-making. They will get more confident in their decision-making. They'll make better decisions. They will outmaneuver their competition every time.
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