Latest / The Retail Podcast / Unlocking AI & Data Power, Championing Diversity, and Harnessing Your Superpowers: A Candid and Inspiring Conversation with Tech Maven Robin Sutara

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Unlocking AI & Data Power, Championing Diversity, and Harnessing Your Superpowers: A Candid and Inspiring Conversation with Tech Maven Robin Sutara · The Retail Podcast

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Retail podcast a global conversation hosted by Alex
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rezze Van Veen. Hello and welcome to the retail
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podcast. Today we're joined by Robin
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satara. She filled CTO at data bricks
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and for full disclosure, I've had the pleasure of working with
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Robin and yes, I think she's thoughts.
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Why do I think she's awesome because in my humble opinion to
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create better working environment better, technology
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outcomes, better data sets that are not subject to conscious and
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unconscious bias. You need diversity, and Robin
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has been a champion of diversity, not only for the
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customers that she served, but for the companies that she works
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on and hence her immensely successful career in becoming
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top 20, women in data and Tech in 2023, 23 data IQ top women
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100. And I'm sure a whole raft of
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other accolades that Robin has. So, without any further Ado, let
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me introduce you to Robin. So Robin.
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Good morning. How are you doing?
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Good morning, Alex, how are you? Yeah, very well.
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Thank you. So, where in the world are you
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today? I happen to be in Oslo.
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So despite the American accent, I feel like I have been all over
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the world in the last few months.
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But yeah, my lovely hotel room here in Norway.
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Well, thank you for taking the time out before you start your
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busy schedule. One of the things we're going to
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do a little bit differently, normally I dive in and start
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talking about, you know, data brick.
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What are they to fix about? What's your day job habla?
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What I love to do because this is all about the part of the
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series of Brilliant Minds, in terms of diverse thinkers,
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people who are glazing, or leaving a trail of of goodness
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behind them and empowering people as they go along.
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And so I'd like to do is just take a couple of minutes and
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take a deep dive into you as a person before we get to the
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corporate stuff is that. Okay, Dad.
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So Sounds great. Thank you.
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So one of the most amazing things when I was doing my
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little research, is the fact that you are an Apache
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helicopter, electric one at Armament repairer.
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So early in your career, you're obviously come from a mechanical
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engineering background. I'm just curious to anyone who's
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listening, who is at the start of their career?
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What advice would you offer them?
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Or what advice would you offer the Robin at that point in her
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life when she was sort of just At the beginning of the, of the
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ladder of starting yoga Korea. Yeah, I always have to admit, I
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tell people like, I started in the mud right in the Korean, DMZ
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repairing, Apache helicopters and hellfire missiles and 50 mm
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machine guns. And if I look if I look back you
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know, that that I won't tell you how many decades ago that was.
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But you know, if I look back and sort of say, do I think I would
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have been where I am today. There's no way like you Just
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don't sit and sort of those positions and sort of say, oh,
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I'm gonna end up being, you know, a chief data officer or
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chief technology officer at one of the, you know, some of the
3:07
largest technology companies in the world.
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And so I think if I if I had to look back and sort of say, what
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were the things that I wish I had known earlier in my career?
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I think they're sort of three lessons from my perspective.
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The first one is be confident in yourself.
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I've always sort of been in these male-dominated.
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Trees are. And so typically own being the
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only female in the room are the only female on the flight line
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or the only female at the executive board table.
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I often, I even struggle with this, maybe sometimes today is,
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I often observe and listen, and I'm not as vocal, or is verbal
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about sharing my opinion or bringing sort of a different
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perspective to board. And so, I wish I had been a
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little more, you know. Act and assertive earlier in my
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career too short of the share that that perspective because it
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is very different than everybody else in the room.
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And so when you get those varying sort of points of view
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and those varying experiences that's when organizations and
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teams and companies do their most Innovative and amazing
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work. And so I wish I had had more
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confidence in myself at the beginning.
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Think the second part I wish I are the second piece of advice
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that I I wish somebody had given me was start early with your
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network and your building out sort of your ecosystem.
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And I've talked about this before, but, you know, building
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up that that Network that it consists of, you know, people
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that are your cheerleaders, that are people that give great, sort
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of feedback, like you just did in the introduction, which is
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always so humbling people that are always going to sort of
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encourage you to continue the work that you do because it is
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lonely. Sometimes I think trying to
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drive some of those messaging but make sure you Also have the
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people that are going to challenge you, that are going to
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sort of push, you outside of your comfort zone.
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And then make sure you have, you know, sponsorship because I
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think as I look at many of the people that have been very, very
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successful in their career, you can look back and see where they
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created somebody that was advocating on their behalf when
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they weren't in the room. And I didn't create those
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relationships until probably the last decade or so.
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So if you look at my career, I stayed relatively stagnant.
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First per personal reasons, but the other part of that was
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really I didn't focus enough on building that ecosystem around
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me that was going to help me be successful.
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And then I think the third piece of advice would be grand your
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self in your superpower, right? Like I remember interviewing for
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my purse sea level job and sort of telling the, the hiring
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executive I do my superpower is creating structure out of chaos.
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And once I could really articulated, I think in a simple
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way As opposed to just articulating, a bunch of things
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that I had accomplished or business outcomes or kpis or
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metric. But once I could really distill
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the unique value proposition that I could bring to a role.
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I've really found opportunities on lock from that perspective.
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It just to Echo to two parts, the other guests are the CEO
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from list. She she mentioned as well.
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The voice in the room is such an important thing for her.
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As a man, you should always have no one's perfect.
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So, everyone's got their internal dialogues, right?
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But the fact that you both have mentioned, that makes me feel
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that there is a some aspect there in terms of, I don't know,
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having seeking permission to talk or just questioning what
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you're going to say, especially in a male-dominated environment
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and her the thing that resonated me with me there is just you
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know, forget that don't don't limit Yourself by that type of
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Oh, I shouldn't say that all. And I said this to a women,
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don't say that type of thing or women don't speak whatever that
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stereotype is, is to challenge it.
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So, there's two things that I see in.
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In terms of obviously, again, having worked with you.
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I know that you're a magnet in towards people, wanting to do
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good work with you. So obviously you must that
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structure out of chaos. There is some secret Source
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there that people drift towards that.
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But the two points I just like to sort of get your A thoughts
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on how do you manage priorities because obviously, you sort of
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hinted towards you know, family, priorities.
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For example and how you prioritize that.
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And then if you do have any regrets or experiences that
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again, when looking back, you would wish someone would have
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said something to you about. You know, this prioritizing this
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way or, you know, I'm just curious on your thoughts on
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that. Yeah, I think I've shared this
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before I was actually in a domestic abuse sort of
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relationship for the first 10 years of my career and so much
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of what I pivoted on those first 10 years were my family, my
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children, my safety, there's lots of reasons.
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Why, because the question I get my ass the most often was a sure
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career seems to really have done well over the last decade where
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the first, right? We didn't even see See you or
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hear you, this was within the workplace, right?
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Because obviously we were at Microsoft at the time.
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So I always sort of tell people just be, you know, be cognizant
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of your teams and the people you're working with and people
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that you're trying to collaborate with you, just have
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no idea what's going on necessarily behind closed doors,
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being the patent Dynamic, sort of opened that up a little more
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that it was acceptable to have these other influences in these
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other things vying. For your time.
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For me it was about being really Shirred, which sometimes I think
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drives my family crazy, but I am.
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I'm a planner. I plan everything and sort of
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dress. I you know, dropped it out.
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And so for me, it's being very, very hyper critical about where
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I invest time. How do I make sure that I
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balance it and giving myself permission to say know that I,
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you know, you just don't have the bandwidth to deliver
9:17
something the way that, you know, that it either should be
9:20
delivered or that you want to, you know, be able to sort of
9:22
deliver against it. And so it is okay to say, you
9:27
know, no or no not now and and sort of giving yourself
9:30
permission to balance and and shift those priorities depending
9:35
on the internal or external influences.
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That may be guiding those things, you haven't mentioned
9:41
this. But again, having worked with
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your, I see, one of the, the areas and getting the what
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you've just said, empathy and Leaders with empathy, that
9:49
ability to understand, you know, it's obviously again, something
9:53
that comes across. When you're talking to not only
9:55
clients but t or teams and it's great.
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I am so torn because I can spend another half an hour, just
10:04
unpacking all of that. Because I just think there's so
10:06
much so many lessons that we don't talk about in the
10:09
corporate world, that will help the future Robbins.
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But I am conscious. You're in a hotel about to start
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your day. So why don't we shift focus and
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look at data bricks and your day-to-day job and what you're
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doing. So tell us what is data bricks
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all about? Yeah, so data rix's.
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I think it just an amazing sort of innovative company they've
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been around for about 10 years created by the founders of spark
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out of UC Berkeley. So an amazingly, brilliant group
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of Founders. I think as we really thought
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about how do we unlock the power of Big Data as it comes across
10:45
organizations? And so, data breaks were
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actually the creators of the lake house Paradigm which is now
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recognized by Gardner and essentially what that means.
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In says it's a way for us to tie together all those data silos
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that organizations struggle with.
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So if you think about traditional data warehouses that
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were typically your business insights and analytics platforms
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versus data Lakes, which were used for your more data science
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and machine learning as sort of workloads and capabilities.
11:16
They are they implications of the lake house or that they tie
11:20
those things together so that you can have a single platform
11:23
in a single set of Of tools to be able to drive insights and
11:28
data science out of the same data assets.
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Whether it's structured data or unstructured data, or
11:34
semi-structured data, you want to be able to move your
11:38
organization from backward-looking and sort of
11:40
reporting and dashboards that we're all familiar with.
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When we think about data to how do we now unlock the power of
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things like generative AI, which we're all talking about over the
11:49
last several months. And how do you do that against
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all of the data assets that Exist within your organization.
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So it's an amazing platform that helps organizations tie all of
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that together with one tool the so that your data Engineers,
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your data, scientists, your data analyst, you know, you can sort
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of spread their capabilities with the limited data talents
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that exist in the ecosystem today.
12:12
So, super exciting times, particularly with this quick
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evolution of large language models that we all see between
12:20
chat gbt you know Dolly has come out to show an example of how
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you can can do it without large datasets and relatively cheaply.
12:27
So yeah, some great conversations that I'm having in
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my role which as one of the four feet field ctOS globally, I have
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a great opportunity to talk to hundreds and hundreds of
12:39
customers on sort of, what are they doing with their data?
12:43
How are they thinking about the evolution and the revolution
12:46
that generative AI presents as they think about unlocking more
12:50
of those citizen? You know, citizen experiences
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across the Going to station where you and I both know having
12:57
had experience. That's where the real Innovation
12:59
comes from is those that sit within the business.
13:02
And so yeah, super exciting times.
13:04
I think, as we think about that, how do we leverage this amazing
13:09
technology to unlock the power of our of our talent within our
13:13
companies? One of the things that that I
13:17
that I saw that you talked about, and I mentioned earlier,
13:20
it's about when companies are trying to get data sets to be
13:24
sort of of unbiased and, you know, in their Engineers that
13:27
are they don't end up in this sort of echo chamber of people
13:31
thinking the same just out of interest from all the customers
13:34
that you've seen apart from recruiting diverse engineers
13:39
and, you know, trying to build it from the ground up, how do
13:42
you is there any way to avoid that?
13:45
Because that, you know, I've seen horrible examples of when
13:47
it goes wrong, but I've not seen it in the Enterprise well,
13:50
because I guess, no one wants that.
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Are that in that way? But we've seen, you know, we've
13:56
seen many examples of when the bias is built in and you end up
13:59
getting really horrible outcomes, just curious, How do
14:04
you avoid that? Is there a way to avoid that?
14:07
Yeah, interestingly I saw I was at Garden or the gardener London
14:10
event earlier this week and we were having this exact
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conversation about you know, an inability to correct historical
14:17
data sets necessarily. So I don't know if anybody's
14:20
ever read that book. Invisible women by Caroline
14:23
criado Perez but it's a great example of how we just aren't
14:27
even aware. So if you think about things
14:29
like medical testing or typically done on the, you know,
14:32
a standard nerd 30 year old male that weighs 160 pounds.
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Yeah. And and so now how do you
14:39
translate that into women? So it's not like you can go back
14:41
and hurt. You know fabricate?
14:44
50 years of medical data that are going to accommodate for
14:47
that or like the seat belt and a car, right?
14:50
So for a woman like me, that's only five for the seat belt,
14:53
cuts, into my neck. And why?
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Because the seat belt and an automobile was designed for the
14:58
average male. And so, I think for me, it
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really Not necessarily about going back and correcting, all
15:06
of these data sets. But how do we make sure that the
15:08
creation of net new data sides? Because if you think about the
15:11
rate of data creation, right? The veracity and velocity, that
15:15
data is being created. Today, we have an opportunity to
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influence. What is the data that were
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collecting? And are we, are we trying to
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minimize and mitigate the biases, you know, in the
15:26
construct of how we're collecting data going forward,
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but the other thing absolutely does come down to.
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Do you have If a diverse set of perspectives across the table
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and if it isn't within your data team because you can't find the
15:39
talent, which we could have a whole, I think our conversation
15:43
on, how do you mitigate that? I think, you know, making sure
15:46
that you're opening feet for feedback from diverse
15:50
perspectives and creating an inclusive environment for
15:56
everybody to have a voice at the table to express their, you
16:00
know, their lived experience, their cultural perspective.
16:03
Their sexual preference. The, you know, sort of
16:08
perspectives, that gender perspective, there's all these
16:11
diverse perspectives. And so making sure that as
16:15
you're creating these models and ecosystems and platforms, are
16:20
you taking into account all of those different voices?
16:23
And if it's not within your direct team this is where that
16:26
collaboration comes in super helpful.
16:27
Are you soliciting out of the business?
16:29
Are you soliciting from your customers?
16:31
Are you soliciting from across? Ross, your supply chain, all of
16:35
these different ways that you could bring in all these
16:40
opposing, sort of perspectives to really make sure that you're
16:43
aware of in the historical data. What?
16:46
Biases exist. How do you prevent that from
16:48
going forward? And is there a way that you can
16:50
mitigate the biases in your in your system today to make sure
16:54
that you don't continue to propagate that historical biases
16:59
that exist in the system's understood just moving on.
17:03
Little bit in terms of obviously you must meet with so many
17:07
Executives, you know around the world from from and different
17:10
Industries. I know obviously from retail to
17:12
finance to manufacturing what's on people's mind right now.
17:17
And then I don't know if this is part of it but one of the things
17:21
the that I read or heard this morning was that, how much code?
17:27
Low code, no code, you know, 60 or 70% of future applications
17:32
over the next couple of years. Years will be written by citizen
17:35
developers, right. Which I used to think was, you
17:38
know, a little bit of maybe marketing from from people who
17:42
would sell that stuff. But now with the the tools
17:46
rapidly enabling people to be able to do that.
17:50
And it so I don't know if that's one of the things but I'm just
17:52
curious. What have you heard us as the
17:54
themes from the execs that your meeting?
17:57
Yeah. So right now, the big buzz is
17:59
around generative AI, right? And sort of how are we going to
18:02
integrate that? Into our into our ecosystems.
18:06
So, for example, at Gardner we gave a keynote that was really
18:09
talking about, where do we see the revolution of AI, impacting
18:12
Industries and organizations the most?
18:15
And there's a there's a few sort of low-hanging fruit because
18:18
everybody everybody sort of worried we don't want to repeat
18:20
a Samsung we don't want to put our IP out on the internet,
18:24
right? We want to make sure that we're
18:26
protecting our proprietary information particularly because
18:29
I have that you know that masters of Law and intellectual
18:32
property. I'm getting a lot of questions
18:34
about how do we leverage generative AI in a way that
18:37
still protecting our assets? But in a way that we don't miss
18:42
the opportunity to really revolutionize the way that we do
18:45
business. So organizations today are
18:48
really talking about what are the things that they can start
18:51
to do with generative AI, which I think will ultimately replace
18:54
low code and no code. If you start thinking about
18:56
prompt natural language, sort of currying and prompt engineering,
19:00
those things are going to make low code, no code almost
19:03
obsolete. Elite because now you really are
19:05
unlocking the power of your citizen capabilities.
19:09
And so there's a few thing 11 is around that techniques that
19:12
enable moment, right? Of organizations.
19:16
So some of the conversations I've been having about, you
19:19
know, how are you thinking about setting up sand boxes within
19:22
your environment to test on? But the other thing is encourage
19:25
your talent across the entire business to start doing things
19:30
like prompt engineering. I think, for me, I've done it.
19:33
Build out my weekly menu or give me a recommendation for a
19:36
workout before. The next marathon that I'm
19:38
training for, right? There's lots of things that you
19:40
could do in your personal life. I leveraging this technology
19:44
just to learn how to get better at prompt engineering and
19:47
natural language query. And so I think that's going to
19:50
unlock a whole next generation of talent that doesn't
19:55
necessarily have to come from the python or the sequel, sort
19:58
of backgrounds. And so encourage that in your
20:01
organization, encourage your team, If you don't have an
20:04
internal ecosystem setup yet to support it, think about doing
20:08
the sandbox environments that are protecting your data and
20:11
setting up the boundaries and the ethical sort of
20:14
responsibility things that you want the organization to think
20:16
about, but also encourage people to do it in their, in their real
20:20
lives. Because those things, you know,
20:23
have a lot of impact in just preparing people for this next
20:26
wave of not having to have a hard coding background.
20:31
The other thing is, sorry. We Carry On, Anna.
20:34
Yeah. The other thing is really also
20:36
focusing on what are those internal things?
20:38
So, I think one of the big things organizations struggle
20:40
with his Knowledge Management. We've all seen it where we've
20:42
searched a billion share points or write a million confluent
20:46
Pages trying to find things like HR, you know, the employee
20:51
policy or how do I, you know, how do I take time off?
20:54
How do I find my payroll? Those types of things.
20:57
You can easily create within your organization minimal
21:00
impact, other than I think it's Maximum Impact on the impact.
21:05
It has on all of your employees to start seeing.
21:07
Wow I can now query and are just a regular question, you know and
21:12
it searches the entire internal ecosystem.
21:15
So things all like your back office.
21:17
So your HR your Finance, there's lots and lots of opportunity for
21:21
low-hanging fruit for you to tackle.
21:23
Those use cases to use generative AI in a controlled
21:28
sort of way. And I do think we're also seeing
21:31
lots of Mission in the engineering team.
21:34
So how do we how do we really sort of tackle you know using
21:39
the copilot types of scenarios? I think you and I both probably
21:42
attended build this week or at least listen to the recordings
21:45
later and all of the integration of those types of Technologies.
21:49
I think we'll continue to see a real sort of propagation and
21:53
increase of those capabilities and lots of Technology
21:57
platforms, which then helped you, you know, really focus on
22:00
the people and process site of of this Mission that we're see.
22:04
So, one of the things I that I one of the questions that
22:08
someone asked me once is a lie. Why you focus so much on
22:11
diversity, right? You're a guy.
22:13
Why are you trying to, you know, what, what's your angle, and
22:16
what am I humble beliefs that, I genuinely believe as, when we
22:21
get to parity in the workplace, it actually.
22:24
And you mentioned this earlier on, it actually brings the
22:27
goodness across all diversity, diverse elements, whether that's
22:31
religion race. Gender whatever that is but you
22:35
need to anchor on one and within the Enterprise world I find that
22:40
sometimes it's very scattered gun in its approach to
22:43
diversity. So you're trying to please
22:45
everyone and end up pleasing. No one.
22:47
And again, I'd be curious on your thoughts Robin in terms of
22:50
when you look at all the experiences and leaders that you
22:54
spoken to about diversity and creating a better environment.
23:00
How do you do that? What are those areas?
23:03
That organizations Enterprises need to think about.
23:07
Yeah, I think the big one is, it's not just enough to have the
23:10
representation around the table, you have to create an
23:12
environment that's being inclusive and giving people a
23:15
voice, right? So as we talked about really
23:18
early in our conversation, how do you make sure that that we as
23:23
sort of current leaders at the table are creating the right
23:27
environment that people have the opportunity to speak up and give
23:30
feedback a true collaboration? Doesn't necessarily mean that
23:36
you just have the representation but you have to think about the
23:39
inclusivity of creating the at that environment, where if a
23:44
person doesn't feel comfortable speaking at the table, are you
23:47
proactively going out and seeking their opinion in a
23:50
different environment or in a different sort of way?
23:53
What I found throughout the course of my career,
23:56
particularly over the last decade or so?
23:59
Is that not everybody receives or gives information the same
24:03
way? And so the truly create equity
24:06
and equality across the table. You really have to think about
24:11
meeting people where they are creating the environments that
24:14
is collaborative that gives them the way to give and receive an
24:17
impact and influence and commentary in a way that's
24:21
comfortable for them. And so whether you solicit
24:24
outside of the big open Forum that, not everybody is
24:29
comfortable in speaking. And how do you aggregate that
24:31
information to feed it back into the Team to make sure that you
24:34
have this sort of agile feedback loop that's creating that next
24:39
evolution of collaboration across the organization.
24:42
I think it everybody can do that.
24:44
If you see somebody at the table who's not speaking, don't call
24:47
them out on the spot. They may not feel comfortable
24:49
but absolutely walk up to them after the meeting and have a
24:52
one-on-one conversation there. Would they might be more
24:55
comfortable and opening up and sharing their opinion.
24:58
And I think that's something that all of us can do is really,
25:02
you know, make sure that Creating a trusted sort of safe
25:07
environment that everybody feels that they can share their
25:10
feedback. Their experiences, their
25:12
concerns, their ideas in a way that that truly unlocks sort of
25:17
innovation across an organization.
25:19
Yeah. Well, you need good leaders and
25:22
I think that's, that's the, that's the, the challenge that
25:24
because I do think sometimes, when you go a layer up in some
25:28
companies, there is group think everyone does come from the same
25:32
background, everyone. Is from the same gender or race
25:35
class and so, therefore, it just, you know.
25:38
Yeah, it's sometimes the across the masses, interestingly
25:42
enough, I was listening to and from most people do listen to
25:45
The Diary of a CEO and they were discussing burn out what burnout
25:49
is and this concept that actually whether or not burnout
25:53
is a real thing or not, but it's when employees or workers feel
25:57
that the task that they've been given they have no control or
26:01
power over is when Then filburn out, right as opposed to
26:06
empowering them to do that and I think understanding them you
26:10
know, them in their whole sense is really important.
26:15
Okay. Looking in terms of closing and
26:18
looking to the Future, where do you see the future?
26:22
Obviously, I'm not going to ask you about data bricks in the
26:24
IPO. Don't worry when we're going to
26:26
break any rules, their SEC filings or anything, not there.
26:29
There is an IPO on Hearts. But where do You see the future?
26:34
What's what's on the horizon you mentioned generative, AI, you
26:38
know, looking two or three years down the line?
26:41
What are your thoughts? Oh, my goodness, two or three
26:43
years? I think I would love to stick
26:45
with that all the next three to six months.
26:47
I think as far as fast this technology as a movie, I think
26:53
generative AI is definitely going to be the one.
26:55
So if you are in any role, I think you can definitely take
26:59
advantage of. How do you learn that new skill,
27:01
set, people always ask me. Many jobs, is this going to
27:04
replace what roles are going to go away?
27:07
How do I think is my role at risk?
27:09
Right? I think I even saw a study from
27:11
ey like what's the next evolution of consultative types
27:15
of businesses, right? And where is that going to go?
27:18
Now that chat GPT and similar models, can create a, you know,
27:24
a data strategy with just a few well-worded prompts.
27:28
How do we think about the evolution of functions and so
27:31
everyone should be Talking about how do you learn the capability
27:36
to do this natural language? Sort of queries?
27:39
How do you think about prompt engineering?
27:41
How do you integrate it you know into your into your current
27:45
roles? That's not sharing IP, I'm not
27:47
recommending that. Anybody put IP our proprietary
27:50
information into those systems, but if you don't have the
27:54
capability to do it for work, do it, do it in your personal life,
27:57
learning a skill, learn that capability because that
28:01
absolutely is the way. Have the world that we see over
28:04
the next three to six months, it's going to impact.
28:07
I think everybody's roles in some capacity and so making sure
28:12
that you're practicing those things as much as possible.
28:16
I know that technology isn't available to everyone and so, if
28:21
you can't get access to the technology really think about
28:24
other ways that you can create ecosystems.
28:27
So, if you're a teacher, can you create, you know, engagements in
28:31
the classroom that allow us? For that type of natural
28:34
language sort of interactions with other students.
28:38
Can you create sort of the pseudo environments because
28:41
while it's not strict, we know that not everybody has access to
28:45
the technology or capability. So let's really think about the
28:48
Next Generation coming up. What are the things that we can
28:51
do today? That's going to help them be
28:53
prepared for the jobs of the future.
28:55
And I think this is part of that part of that ecosystem, that we
28:59
see going forward, don't want a wonderful way to end the
29:02
podcast. Astro Robin, thank you so much.
29:05
It's been an absolute pleasure. Thank you for your honesty, your
29:07
your humility, and thank you for all the great work that you're
29:10
doing in promoting, you know, a true inclusive and diverse
29:14
environment within the fields of data and AI.
29:17
Thank you so much, Alex, I appreciate the opportunity.