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Zoe Jervier Hewitt wishes you'd stop thinking about hiring and HR as the soft side of business.
As a talent partner at EQT Ventures, she helps the firm's portfolio companies to recruit and manage their staff. But her focus is on making performance management and executive hiring more analytical to simultaneously help founders move away from using gut feelings to make decisions while making their pipeline for leadership teams and board seats more diverse.
That often means changing the minds of headstrong founders and having difficult conversations about the systems they have put in place. But Jervier Hewitt has found that cold, hard data is often the best way to tackle that, which is why she pushes companies to quantify as much as the hiring process as possible.
And she thinks we're only just at the start of a longer journey to improve the hiring process. "We're going to see such heightened engagement in data and evidence and psychology in everything to do with talent selection and talent development," she said. "I think you can be as data-driven and as informed from an evidence point of view with HR-related decisions as you are with all the other aspects of running a company."
In an interview with Protocol, Jervier Hewitt spoke about the best ways to quantify talent, how she approaches D&I topics with founders and the strategies she uses to coach executives to think differently about HR.
This interview has been edited for length and clarity.
As a talent partner advising on hiring decisions, what does the data-driven piece actually look like throughout the process?
HR and talent have a reputation for being the softer part of business, and there's a lot of gut feel, which results in bias. I think you can be as data-driven and as informed from an evidence point of view with HR-related decisions as you are with all the other aspects of running a company. So my personal agenda and how I support these companies is asking how I can help them buy into the idea that people in leadership is the lever for growth and is the most important thing to focus on.
Secondly, I ask how I can help them become more data-driven and evidence-based in the way that they're running those functions. To give you an example, in talent acquisition, I spend a lot of my time with founders before we even start the search [for employees]. What are you looking for? What is the role here? What are the specific skills and competencies and behaviors and values that you need to show up in in the perfect candidate? And it might sound sort of obvious, but you would be surprised at how many founders really skip that preparation part and just go straight to market to start interviewing all these candidates without actually having a clear framework or a scorecard around what we're assessing.
I try to make the assessment setup really watertight, and I find that there's less room for unhelpful bias to creep into the process that way. They're actually focusing on trying to surface data that is going to be predictive as to whether that person will perform in the role or not.
When that data does get surfaced, what does it look like? What kinds of factors are you evaluating?
It can look like many things, but I think one piece of data that I always spend a lot of time on is the skills and the competencies and the behaviors that need to show up. In sourcing, there are data signals that we might be looking for, but I think when it comes to assessing talent, there are ways to extract data from an interview process. Psychometric testing has definitely fallen out of vogue in recent times, but we have all of the tools we need and all of the science we need to actually make more predictive hiring decisions, using things like psychometric tests and strengths-based profilers and personality profilers.
They just seem to be not widely adopted and not very popular, partly because some of them are very academic and are inaccessible. Some of them have horrible UX, and some of them just add friction to the process. Most people like to believe they are a really good judge of talent and of character, so I think what I'm trying to challenge is that there is data and there is information that would help hiring decisions. You just have to make the conscious effort to go outside of yourself to go and seek it.
To somebody on the outside, pulling up a company page and seeing that the executive team is eight white men would seem to show pretty clearly that there's a need to bring someone like you in, but what other signals in the data are you paying attention to as you make assessments or identify issues?
This is one of the most common pitfalls I see in our portfolio and in early-stage, venture-backed companies generally. A lot of people tend to have a very narrow view on what leadership looks like and what executives look like, and I don't think that's helped by investors. There is a tendency to focus on signals that could be misleading, such as credentials — where they went to school, companies they worked for — and just taking that at face value and reading it as desirable. But that's done with no reason other than the fact that it's hard to get into Oxford.
What I try to get founders to focus on more is the type of personality that person has and trying to move beyond the confidence that's coming across in the interview to the actual competence. I always get founders to talk about what the objective is: If this person was doing their job really well, what would happen? And then let's reverse engineer that and say, what track record do they need to have, and what skills would they need to have to be able to do that?
By going through that exercise, you come up with this assessment scorecard, which is really specific and granular, but it means that when you have a candidate in the pipeline, you're not getting thrown by these other illustrious things on their CV. I've seen this have a positive effect on the diversity of the pipeline and the diversity of the hires.
The second thing is kind of simple, but I make sure that structured interviews are in place, as unstructured interviews are just the worst when trying to make predictive decisions. Even if you really love the person sitting in front of you, don't divert and go off and talk about what you do on the weekends, let's stick to these questions so we're doing a fair assessment.
As the interviews and conversations have moved largely online due to the pandemic, have there been any adjustments you've had to make to the scorecards? Are there new or changed factors that play into the evaluations?
To a large extent, it has stayed the same. In fact, I think these assessment guides have become even more important in this virtual world of hiring because there are things that you do miss and you can't pick up. Some of the more tacit things like body language and how they walked into the office, and how they greeted the receptionist, and all these sorts of things.
But I think it largely hasn't changed because you're always looking for the same set of things. You still want to make sure that the process is screening for what a person's done in the past and the skills they have and the competencies.
I would imagine some of the conversations you have around executive talent can be tough topics to broach with the founders, especially when you've identified an issue in their current processes. How do you approach them?
When I'm introduced to a founder, and they guess what my role might look like, I probably disappoint a lot because I think they expect me to serve candidates on the plate or jump straight to the sourcing and the interviewing. So I often say that if we're doing our jobs well as operating partners, we have to deliver messages that are tough, and they might not want to hear them in the short term. But as you start working with a founder, and especially after I've explained why this is so important and how it can go horribly wrong, they really come around and there's a high willingness to stick to a process that's more data driven.
Those issues of diversity, equity and inclusion and racial injustice have been under the microscope more so in the U.S. since the killing of George Floyd. You work with both American and internationally based companies; has there been a difference in the response you've seen to the approaches taken to corporate D&I conversations?
I've seen much higher engagement with the topic. I think there was a good level of interest before, but now it's really made people think more about things like implicit racism and actually look at their structures internally to evaluate what exists in our companies that is not promoting a diverse and inclusive and equitable culture and team.
I've had a lot of inbound requests for support from our portfolio on helping them revisit their performance evaluation structures with a diversity and inclusion lens. And part of that is seeing that even these companies that felt like they had meritocratic setups have a lot of subjectivity there.
Your goal is to inject data into the parts of business that are sometimes considered softer, so what's the dream data set that you don't have yet that would help that goal?
I would love to have a data set on people's personalities, their tendencies. There's so much to gain if you know what someone is strong at and what their performance risks might look like. In the recent months, I've become really interested in strengths profiles, which are tests that have psychometric properties but focus on telling you what your top seven strengths are and then what the downside of those strengths might be.
One of the things I did last year was work with an organizational psychologist to review the portfolio founders that we'd invested in the past, match that to their outcomes so far, and then look at the latest theory on leadership derailment and success. We came up with a framework around the six most-common founder derailing characteristics. We can now build a set of questions and a scorecard around that to use as a reminder as we go through and meet new teams.
We have an internal software that we're building called Motherbrain. It's currently used as a tool for our investment team to find companies that are not coming through their network. We're not there yet, but I would love it if there were some sort of data set in Motherbrain that would tell us about a person to watch who might be starting a company in the future.