Ancestry CEO Deb Liu on Building Teams, Closing the Gender Gap in Product and Learning from Failure
We recently had the opportunity to sit down with Deb Liu, President & CEO of Ancestry. In addition to her role as President & CEO, Deb is an author, speaker and former senior executive at Facebook where she created and led the company’s Marketplace service. We were excited to learn about Deb’s vision for Ancestry and her involvement with promoting diversity and women in tech. She’s the founder of Women In Product, a nonprofit to connect and support women in the product management field.
Arize: When we learned about you joining Ancestry as CEO, we were really intrigued by the move. What led you there?
Deb: When I came in, one of the first things we did was we set out a new product vision: to enable everyone to easily discover, craft and connect around their family story.
I say “everyone” because Ancestry is an amazing company, but the product worked best for those of Western European descent where our content archives were strongest. We want to expand our reach to everyone around the world who cares about their family.
We have an incredibly deep content library. We want to make it easier for our customers to craft stories, which is really more than accessing records and looking up the birth date and the death date of relatives. It’s actually about finding their history and crafting their story.
One of the first questions we asked was: how do we go from something which has been a solo activity to something you do with your entire family?
What if we brought the people who lived in your family tree to life? Beyond birth, marriage, and death dates, we can show you where they lived and how they lived. We want to not only digitize collections of historical records, but enable people to tell their own stories through our platform.
We encourage people to share their stories so other people in the community can learn more about traditions or their friends can actually hear more about the celebrations that they care about as well.
As we think about our priorities going forward, we have a lot in the works.
We just launched the Freedmen’s Bureau. It’s a collection that we helped digitize and has records from the post-Civil War, encompassing freed slaves, their lives and their stories.
That’s been an incredible adventure, and it’s been really important to us as we diversify the experience, both for those who already love our platform and for those who have yet to try it.
We are one of the biggest digitizers of archives in the world, and our work is to capture the human stories that are attached to that content.
Arize: Fascinating. We also heard that you’re working on a book, tell us more.
Deb: The book is called “Take Back Your Power: 10 New Rules for Women at Work.” It’s a reflection of the world as an uneven playing field for women. We know that we can’t fix the system immediately, but this book is about what we can do in the meantime. It asks the question: “as women, how do we thrive in an imperfect world, and how do we take back our power where we can?” It’s about showing up and standing up. It’s about building allies. The book is a composite of the things that I wish someone had told me when I was first getting started, as well as the things that I’m still learning today in the workforce.
I’ve had so many people say they wish they had read this book earlier. I talk about unconscious bias, and differences in the expectations we have of men and women. The world isn’t fair, and that makes it harder for you to actually speak up. It makes it harder for you to raise money or found a company. And the question is, do we shy away from that? Or do we kind of push through? I think you can guess my conclusion.
I share stories of women who’ve overcome significant obstacles. Abigail Hing Wen shows up. She became a New York Times bestselling author after spending ten years writing while working in AI. Now she’s a successful content producer.
I tell her story, and the stories of so many amazing women who struggled and took back their power. I pass their advice forward while sharing my own journey in Silicon Valley and the lessons I’ve learned.
Take Back Your Power: 10 New Rules for Women at Work comes out in September of 2022.
Arize: You’ve been outspoken about building teams and the history of women in product and how it’s trending in the wrong direction. Can you tell us about that?
Deb: I used to teach a new hire class for all the new PMs at Facebook. I would start by talking about building a team in order to build a great product. The atomic unit of success is not an individual. It’s a team.
What individual has actually succeeded without a team? In every endeavor, there are people who take the foreground, and there are people who take the background. But you can’t build the Empire State Building without a very large, well-coordinated team, no matter how good an architect you are. Nobody succeeds alone unless they have written everyone else out of their story.
Building and innovation require making lots of mistakes together as a group. If the team is not willing and able to actually course-correct together, to look each other in the eye and say, “Hey, maybe we did the wrong thing here,” then the team is not a self-healing team and is not able to move forward in a constructive way.
When it comes to Women in Product, we always think the job of getting more women into leadership roles is done, right? We make progress, but what we don’t realize is that progress is two steps forward and one step back.
There were a lot of companies in the late 1990s and early 2000s that had gender-balanced product teams. There was a time when you looked at a lot of leadership teams with women at the helm. At one point at PayPal, the VP of Product and all of her Directors were women. That would be unheard of at most companies today.
Arize: Why is that?
Deb: Somewhere in 2004, Google decided to require a computer science (CS) degree for certain roles, and it spread throughout the industry.
Suddenly a lot of women in product could not get the next job because they no longer met the educational qualifications for a job they had already been doing. This happened to me as well.
When I went to Facebook, I actually went into product marketing, not product management, even though I ran the buyer experience product at eBay. The point is, the industry shifted so fast and a lot of women were left on the sidelines and had no idea what had happened.
Arize: How did you get to the answer?
Deb: I had this inkling in my head that something was going on, but I couldn’t figure it out. I talked to tons of women and in the end, we unpacked it together. The problem was that by this point, years of new graduates had entered product with the requirement of the CS degree. This meant that it became incredibly gender imbalanced. Now we have this huge deficit where people want diverse product leadership, but there are fewer women in the pipeline.
On the other side, you know, as we’re coming out of this, there are many companies who partnered with us, Google included, to actually change this direction.
At Facebook, what caused us to really pause was that three of the most successful women who ran engineering teams didn’t have computer science degrees. Nobody said to us, “You are not good enough because you don’t have this qualification.” It’s just the system that has been set up over a long period of time.
We had set up ways to identify and remove this bias. We were able to make huge strides from less than 10% women in product to well over one-third when I left.
Arize: What are some of the recommendations that you would give to an earlier-stage company on how they can avoid some of those same biases?
Deb: It starts even earlier. Of the startups that were funded in 2019, less than three percent of funding went to all-women founding teams, and only twelve percent went to female founders or mixed-gender teams. That means over 88 percent of VC funding goes to all-male founding teams.
Part of it is that the VC community is 90 percent male. I don’t think male VCs are intentionally discriminating against women. I think what they’re saying is, “I have an affinity for this person.”
The affinity bias is: “let’s hire someone who’s most like us because that way we can get things done. We speak the same language.” And affinity bias is actually what causes a lot of systemic bias, not outright discrimination. Early on, maybe you say “I want to move fast. I want to be able to make decisions quickly. I want to be able to pivot. I want to hire people who are most like me.”
This is a subconscious thing. But the thing is, the friction that’s created by having someone different in your corner helps teams make better decisions. It can be harder to make those decisions, but on the other hand, it’s valuable to hire someone who’s willing to challenge you by saying, “Maybe we’re going in the wrong direction because I have this different experience I can bring to the table.” That diverse background, that diverse life experience, that diverse visibility into things that other people don’t see, can add enormous value.
Aparna Dhinakaran, co-founder and Chief Product Officer at Arize: If you look at founding teams with at least one woman, they are more capital-efficient and outperform on early revenues than teams that are all male. That kind of push towards diversity is really having someone who’s different from you asking the tough questions. We underestimate that because we are in the spirit of “let’s go faster” or “let’s finish each other’s sentences” as opposed to challenging each other and making it harder — because once we get to the other side, we’ll have the right answer.
In the machine learning (ML) and AI space, which is a very technical field, the barrier for entry is a CS degree plus a data science or ML degree oftentimes.
Of our first four founding engineers, half were women. And so we had a founding crew of half women and half men very early on. And it changes you because you’re recruiting people to join a team that’s already diverse.
Candidates who may be different aren’t leery to join your team. Having that base already set up has really helped us attract some of the best talent in the industry and the feedback is refreshing.
When we were looking at VC’s, the makeup of the firm and perspectives and viewpoints on diversity were also a big part of our evaluation process.
Arize: Deb, when you talk about allowing teams to fail it resonates because a lot of data science teams are building and deploying models that run into performance issues in the real-world. It can really cripple a business if they aren’t monitoring and observing the problem. That’s part of what we’re trying to address from a technology standpoint with ML model monitoring.
Deb: The work that we all do in building technology is closely aligned with failure. For every successful startup, there are 99 failures. A lot of it has to do with whether you allow failure and things that don’t work to be a learning experience. Do you allow it to be something you can get some good out of, or do you allow it to be the stumbling block that stops you?
Too often, we allow the experience of failure when something doesn’t work to be the stop sign that says, “okay, this is the wrong thing,” when actually it’s part of the journey. Making mistakes is integral to the act of building. There are two ways to succeed. One is to catch lightning in a bottle, to hit it out of the park immediately. The other way is to fail fast, understand what you’re not supposed to do and then to succeed because you iterate and learn faster than anyone else.
Sometimes we forget that failing fast gives us more data than if we try to find the perfect answer. In software, we need to put things into production, understand the impact, figure out the problems, and then learn ways to improve on it. If we can understand our mistakes, we have the ability to fix the issues quickly, iterate, and learn.
It’s a story of refinement. It’s a story of pivoting. It’s a story of rethinking that gets to that golden nugget, that “aha” moment.
Technology is not like building a bridge, where it has to work perfectly the first day. It actually is an iterative process where you’re building on the corpus of knowledge, where you’re constantly pushing the product and experience to get better and better. It’s that kind of constant improvement that makes the industry so dynamic and so powerful. Accepting that some things aren’t going to work and iterating to find what does is the name of the game.