Image Title

Search Results for Aubrey Blanch:

Aubrey Blanche, Atlassian | Grace Hopper 2017


 

>> Narrator: Live from Orlando, Florida, it's The Cube covering Grace Hopper's celebration of women in computing. Brought to you by SiliconANGLE Media. >> Welcome back to the Cube's coverage of the Grace Hopper conference here in Orlanda, Florida. I'm your host, Rebecca Knight. We're joined by Aubrey Blanch. She is the Head of Diversity at Atlassian. >> Yeah, thank you so much for having me. >> Well, thank you for coming on the program. >> Absolutely, it's great to be here. >> So, tell me a little bit more about what you do as the Head of Diversity at Atlassian. >> Yeah, so I was always tell people that my job is to make people really happy and to give them an equal opportunity to succeed? But what that actually means day-to-day is that I spend a lot of time looking at the data that tells me are we hiring the right people, are we hiring people equitably, do they love coming to work and are they having an impact? So, I, that means sometimes designing programs, sometimes doing focus groups, but always trying to think about how do we make sure that everyone has the thing that they need to be really successful at Atlassian and sort of fulfill our company mission which is to help unleash the potential of every teams and for us, you know, we, we unleash the potential in every team and we know that every team is diverse and so we know that it's just an imperative for us to look like the customers that we're serving because it means that we understand them and it means that we can help them do better work. And I know that you are really dedicated to the idea of including empirical science-- >> Yes. >> In, in what you do. >> Aubrey: Yes. >> Talk to me about some the, the most powerful studies, the most powerful research that you try to bring to your thought process in terms of hiring. >> Yeah, absolutely, so I'm a recovering social scientist by training so I get really excited about the idea that you can use research to make little tweaks to the way that you do things that changes outcomes in really big ways. So, one example. We know that women, on average, when they have the same contributions as their male colleagues, actually tend to rate themselves lower. Right? Same work and then they say, "No, that's not quite as good." And so, last year we made a change to our performance review process that helps get rid of problems that might be introduced by that. So, if you're a manager and you're reading two people's work and one person has given themselves a three and one's given them a four that might affect your rating. So, we actually changed it so that now managers right the review without seeing their direct reports review. Turns out it removes bias, it shortens the process, and it helps identify whether people have an agreement about what people's work is. And we found that that meant that everyone was getting a more equitable set of ratings and we could say, "Eh, we removed bias "and it made it easier for the business." And it meant that people were getting rewarded for the value that they were creating. >> And you're also, you're also big on data. >> Aubrey: Yes. >> And so you, you first of all have to collect the data. >> So what's kinds of-- >> Yeah. >> How are you collecting data and polling employees about whether or not they are happy? Absolutely, so first, you have to collect data about who people are and how they identify. So, things like gender, race, disability status. We collect that data. And then we survey people, right? Asking them not, are you happy, but have you grown in the last six months? You know, does your manager support you in doing those things? And you can sort of triangulate what a person's experience looks like that way. But you also look at bigger things. You look at things like promotion velocity. Or what is your attrition and retention rates? And those tell you a lot of things. You dig into exit surveys and you say, "What's the number one reason that people are leaving?" Let's fix it. >> Right. >> And the other piece of data that I get really excited about and something that's sort of Atlassian's thing, I guess, is that we actually report on the diversity of our work force at the team level. So, you can check it out. It's atlassian.com/diversity. But in addition to those corporate level statistics, we really think that the diversity on your teams matters because your teams are who you're engaging with day-to-day. And you get the value out of diversity because two different people come together. And so it doesn't actually matter if you have 30% women in your company if all the women are in HR and marketing and all the men are in engineering. What matters is each of those teams is diverse because it helps them build better. And so we think it's important to measure it that way. >> That is such a great point because I think that a lot of companies can bolster their diversity numbers. >> Aubrey: Yeah. >> And with women in the more traditionally female-oriented parts of the company. >> Absolutely. But that cut of data also helps drive bigger impact. So, I'll give you an example. When we cut our data at the team level, what we saw, and this was about a year ago, that about 13.5% of our technical employees were women but when we looked at all of our teams that were developing software, two thirds of them had a woman team member. And so from that insight we were able to say, well those women are probably isolated on their teams. And so they're likely lacking a sense of community and belonging and so instead of just investing in recruiting, we created a variety of programs that helped women collaborate across their teams. So, things as simple as a coffee dates program where women opt in and are assigned to another woman in their office to have coffee with every other week. Or something more structured like a peer-mentoring ring that's cross-functional. And what we found is that that actually helped drive retention for women in those rolls. So, while we're investing in recruiting, we're also making sure that we're keeping and growing the women that are already on our teams. >> So this is, this is incredible. These small tweaks as you started off saying-- >> Yeah. >> That are really changing the way you do business. >> Absolutely. >> What is you're, you're best advice to the rest of the tech industry where Atlassian, feels like you've figured out something here? >> Yeah, I think it's trust the data and know that there are no best practices or silver bullets. So, we've made incredible progress over the last few years so-- >> And you do, and you publish your numbers. >> Yes we do. >> As you said. >> Yeah, every year. We've improved our hiring of women in technical roles by 80% over the last two years and it's, we've honestly just adopted the same approach that our software teams use. Which is we test something, we see whether it works and then we iterate and improve it. >> Agile, right. >> Right. And so it's not about one training or one program, it's about re-thinking about how you engage with your people and how you respond to their experiences. Because they'll tell you what they want and need and it's about providing that. And I always tell people best practices are a starting point but they may or may not work for you. So, you need to be open minded to the idea that the first thing you try just might not work because your culture might be different or something like that. For us, we also like to think about diversity in a really broad way. So, my other piece of advice is think intersectionally, right? So when we say-- >> What does that mean? >> Yeah. >> How do you define that? >> So, it's a big, complicated word but it just means that we all have layers. So, I, for example, identify as a woman but I also identify as American and Hispanic and five feet tall and an HR person and all of us carry all of those identities around and what you, so you need to understand that women is a diverse group. But, when you do that, when you start talking about axes of diversity that are past gender, it turns out it turns what could be an us-versus-them conversation into something that's about we. Because maybe someone says, "Well, I don't identify as female "but this is the unique thing that I bring in." And suddenly you've created it where everyone has an incentive and has skin in the game to create inclusion and you will get greater gender equity out of that. So, it's a little bit counter-intuitive to start backwards in a way, or start complex and work towards simple but that's something that we've found has been incredibly helpful in galvanizing people to get involved and really changing the culture in a way that it's not a top down initiative or a bottom-up initiative, it's everyone moving in the same direction. >> Well, Aubrey, it sounds so common-sensical, of course, yes, yes. >> Yeah. >> But it's only obvious after you say it. >> Right, yeah, yes. >> And after you've tried it and tested and iterated on it. (laughs) >> So that would be my thing is, is whatever diversity matters to you because at Atlassian, for example, we're an Australian company and so international diversity is incredibly important, right? Where you come from. You know we, I always joke, you're more likely to hear three languages walking across the office than anything else and that's a really cool place to be but it means we've already gotten used to working in a diverse environment and now it's how do we just add additional aspects of diversity to our culture and to our teams? >> Right, and let's not fight that. >> Absolutely. >> 'Cause it's working. >> Right, and the other thing that I've found which is really exciting is as I've seen teams start to change their composition, you don't just hear really great things from those folks who come from under-represented groups. People from those majority groups say, "Wow, it's actually improving my experience at work," because they have access to more perspectives and people who have different experiences than them. >> So, it's firing different parts of their brains to-- >> Yeah. >> To-- >> It's just more interesting to do your job that way. >> Have better ideas, yeah. >> So, that's the other thing that's real important is this is a win-win-win solution, it's not a zero-sum game. >> Right. Well Aubrey, thanks so much for joining us. It's been a lot of fun talking to you. >> Absolutely. Thank you so much for having me. >> We will have more from the Cube's coverage of the Grace Hopper Conference just after this.

Published Date : Oct 12 2017

SUMMARY :

Brought to you by SiliconANGLE Media. She is the Head of Diversity at Atlassian. So, tell me a little bit more about what you do And I know that you are really dedicated the most powerful research that you try to the idea that you can use research to make And those tell you a lot of things. And so it doesn't actually matter if you have That is such a great point because I think that And with women in the more traditionally And so from that insight we were able to say, These small tweaks as you started off saying-- and know that there are no best practices or silver bullets. and then we iterate and improve it. that the first thing you try just might not work but it just means that we all have layers. Well, Aubrey, it sounds so common-sensical, And after you've tried it and that's a really cool place to be Right, and the other thing that I've found So, that's the other thing that's real important is It's been a lot of fun talking to you. Thank you so much for having me. the Grace Hopper Conference just after this.

SENTIMENT ANALYSIS :

ENTITIES

EntityCategoryConfidence
Rebecca KnightPERSON

0.99+

AubreyPERSON

0.99+

Aubrey BlanchPERSON

0.99+

30%QUANTITY

0.99+

one personQUANTITY

0.99+

five feetQUANTITY

0.99+

two peopleQUANTITY

0.99+

AtlassianORGANIZATION

0.99+

last yearDATE

0.99+

Orlando, FloridaLOCATION

0.99+

Orlanda, FloridaLOCATION

0.99+

80%QUANTITY

0.99+

fourQUANTITY

0.99+

one programQUANTITY

0.99+

eachQUANTITY

0.99+

atlassian.com/diversityOTHER

0.99+

firstQUANTITY

0.99+

SiliconANGLE MediaORGANIZATION

0.99+

Aubrey BlanchePERSON

0.99+

threeQUANTITY

0.98+

oneQUANTITY

0.98+

about 13.5%QUANTITY

0.98+

one exampleQUANTITY

0.98+

one trainingQUANTITY

0.98+

HispanicOTHER

0.97+

2017DATE

0.96+

Grace HopperPERSON

0.96+

Grace Hopper ConferenceEVENT

0.94+

two different peopleQUANTITY

0.94+

CubeORGANIZATION

0.93+

two thirdsQUANTITY

0.93+

three languagesQUANTITY

0.92+

first thingQUANTITY

0.91+

last six monthsDATE

0.9+

AgileTITLE

0.88+

about a year agoDATE

0.87+

AmericanOTHER

0.84+

yearsDATE

0.82+

last two yearsDATE

0.8+

lastDATE

0.78+

GracePERSON

0.69+

HopperEVENT

0.69+

AustralianLOCATION

0.67+

Grace HopperEVENT

0.66+

NarratorTITLE

0.6+

teamQUANTITY

0.59+

CubeTITLE

0.49+