The People Space Is Full Of Data. But We Need More Diversity Of Data.
September 22, 2022
Minh Hua, who is a Chief People Officer in the private equity space, and has held senior HR roles at companies such as Stanley Black & Decker, Amazon Web Services, and JPMorgan, shared smart insights with David Reimer and Adam Bryant in this “X-Factor Leadership” interview.
Reimer: How did you get into the HR field in the first place?
Hua: My career was shaped by my family history. I was born in Saigon. We were among the “boat people” in the ’70s. We landed in Malaysia, spent a year in a refugee camp, and then somehow ended up in Augusta, Georgia, which was not a very diverse place. I remember early on aspiring to be a cook at the Waffle House, because they made $10 an hour. I thought that was amazing.
After college, I joined a nonprofit under a grant from Bill Clinton’s Welfare to Work reform of the 1990s. The mission was to go deep into neighborhoods and help welfare recipients, who were mostly single mothers, get job training and then get a job so that they could be self-sustaining. I stayed in HR because I like helping individuals and teams be successful.
Bryant: How do you think about the application of data to the profession of developing talent?
Hua: The people space is full of data. Where it gets complicated is that we have to, first, recognize whether a research question or conversation topic is an emotional one or a political one, or if it’s a problem ready to be solved with data. We have to recognize the human in front of us. Are they ready to consider data and hear how to solve it? Or do they need to talk about it and work through something else that is not data dependent?
The goal is to get a fuller perspective.
There’s also a misunderstanding about data. Everyone is data-driven. We need more diversity of data. Quantitative data is data and opinions are data, too. Observations in human behavior are data. When I’m talking to data scientists, I’m usually trying to get them to diversify their data by talking to people to better and understand what happens in the world. When I’m talking to HR business partners, I’m usually trying to get them to be wary of echo chambers and to look at quantitative data. The goal is to get a fuller perspective and seek to disconfirm our presumptions.
Reimer: It’s clear that you like to pressure-test conventional wisdom. What are some other issues that have caught your attention?
Hua: We can talk about compensation, which is driven more by negotiations and tradition than science. There’s not a lot of science giving us insight into what actually works, and which metrics to use. Do long-term incentives actually motivate anything? Do short-term incentives actually motivate anything? How much of it is kind of the luck of the draw?
Another question is, what type of compensation is more psychologically impactful and therefore motivating? Conventional wisdom would say the answer is cash. But tech companies have debunked that a little bit. For instance, Amazon, until this year, had a $160,000 base salary cap and everything else was pure RSUs (restricted stock units). The company and its employees have done really well.
If you’re a CEO and compensation is one of the largest line items in your P&L on the expense side, you should at least have a data-dependent theory as to what pay mix attracts, motivates, and retains the best talent. What works and what doesn’t work? Those are still open questions in the compensation field.
Bryant: What about interviewing techniques for job candidates?
Hua: What I urge recruiters and CEOs to do is to go beyond behavioral interviewing because the efficacy is now suppressed. You should mix behavioral interviewing with work demonstration, which is the best technique for assessing a candidate’s ability to do the job. Interviews measure interview performance. That partly explains the mis-hires we are seeing. Interviews are not consistently great at predicting job performance.
Reimer: What are the big priorities coming at the profession over the next several years?
Hua: Decades ago, HR was measured on how well they played the role of friendly neighborhood HR person. You were in a plant or a site and everyone knew your name, and employee relations and morale were a big part of your job.
HR needs to embrace data analytics, predictive analytics, and machine learning.
Today, in corporate America, the cost structure doesn’t allow for that approach and the employee population is so much larger. HR can’t be expected to know everyone. You lose touch. The long-term trend is that the scale of organizations is outpacing the natural skill set and tools of HR. The world has become more complex. HR needs to embrace data analytics, predictive analytics, and machine learning. Now what’s the litmus test? For big companies, how many CPOs have a tech person on their leadership team? And how many HR functions have a people analytics function?
Bryant: Going back to the story about your early years, how did those experiences shape who you are as a leader today?
Hua: Growing up in Augusta, Georgia, as an immigrant, I didn’t speak English at first, and I was in an environment where race relations were more about Black and White. I was part of the silent minority. All of that helped me think about things differently. It helped me bring an outside-in perspective.
Reimer: What aspect of leadership do you see people struggling with most?
Hua: People who are very smart about technical matters can be challenged by not always being the one to have the answer. They have an opportunity to empower others and show patience. At the other end of the spectrum, sometimes people who have high EQ need to be coached to push their team more, even though it’s unnatural to them because they like to be liked.
Another fundamental challenge of leadership that they don’t teach you in schools or textbooks is that humans are self-centered, and when humans want feedback, they most really want to either hear good news or get their beliefs confirmed. What humans want out of their boss is support, to be told they’re doing a good job, and to be paid well, et cetera.
A lot of new coaches and new leaders make the mistake of having a static standard in their head and then they coach from that standard. For example, when we’re giving advice, we’re often giving advice based on who we are and our past experiences. The truly effective coaches start to decipher whom they have in front of them and then go from there. That type of coaching is really difficult to do, and it’s not done very often.
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