Bridging the Data Divide with Rachel Workman

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Hi, Sandy Estrada here. Welcome to this week's episode of How I Met Your Data.

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This week, we have a long one. It's a bit of a double feature.

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Anjali and I are going to talk about a couple of conferences that we've attended

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over the last few weeks. And we also have a special guest, Rachel Workman.

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She is the head of data at Sound Commerce.

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And I am so excited to share Rachel with you. I've known Rachel for seven years.

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She is a bottle of lightning.

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She just has a very unique perspective. She has worked both as as an engineer.

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She's worked on the business side. She's worked as a management consultant,

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and now she's backed on the engineering

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side as head of data for SoundCommerce. So let's get into to it.

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Music.

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That was your week. I was on vacation last week. Prior to that,

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I went to a couple of conferences, one of which you attended as well.

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A little bit of travel and meetings, etc.

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It's It's been a whirlwind couple of years. Yeah, I mean, it feels like a bit of a whirlwind as well.

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You were out last week, as you mentioned. I'm out next week.

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So just kind of that run up to being out for the week has been kind of mind-boggling

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a little bit, a little bit of mental gymnastics.

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But I'd rather say that as opposed to telling you I had nothing to try to close out before I left.

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Well, you can time go by before I go on vacation. Yeah.

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But my seat's super warm.

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Spoiler alert, when you get back, 10 times worse than the prep to leave.

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I'll tell you that much. It's been hoey for me. Yeah, it's the luxury tax for going away.

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You prep so much before you leave the office.

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And then when you get back, it's like you pay the penalty for all the stuff

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that didn't get done, plus all the stuff that went awry, plus all the stuff

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that needs to get done in the future. But it's okay.

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It's worth connection and time with my family, going somewhere new,

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all of that. I'll take it. Yeah, same here. Same here. Absolutely.

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Let's talk about the conferences we both went to. So I went to FEMA Boston.

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It's the Financial Services Data Management Conference. It's been running around for a while now.

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I really enjoyed it. I actually met the conference producers leading up to the

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conference because I was going to emcee one of the tracks and run a couple of panels.

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So I moderated a couple of panels during the event. And what really got my attention

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was the agenda and the way they set it up.

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There's a lot of round discussions, a lot of opportunity for folks to engage

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and interact and learn from one another and network.

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It was probably one of the first conferences I've been to in a very long time

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where I really was able to meet folks from financial services that were really

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in the trenches trying to solve problems with data.

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One interesting takeaway from that event was one of the sessions I went to was

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this boardroom session, which is more of a workshop type session on Gen AI.

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I was astonished. The speaker, the person moderating the session,

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asked the room, who's working actively on Gen AI? No one raised their hands. Interesting.

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Yeah. Yeah, exactly. I was in shock. I was like, wait a minute,

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wait a minute, wait a minute.

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The moderator did a great job of outlining all the different areas we've seen

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in terms of use cases for Gen AI.

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Content summarization, being able to search content easily, being able to aggregate

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trends and data, being able to code migrations, those kinds of things,

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code facilitation use cases.

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There were some others, but no one in the room.

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Across all those use cases, there wasn't a single hand in that room.

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And there must have been 50 people in there. That's really shocking.

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You know, we talked a little bit a couple of weeks back about these shadow Gen AI.

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Organizations that are spinning up kind of under the covers at clients that we work with.

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So it's surprising that to hear that. Was there any key themes or drivers around why?

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What I walked away with was that data teams are not involved because the folks

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in the room were part of governance, data management, right?

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They're talking about it. They're trying to prepare for it, but it's potentially

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happening in other pockets within the organization.

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And it's funny because I saw a LinkedIn post this morning about who should own

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AI and your buddy Malcolm Walker responded saying that's no one should own AI.

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AI is going to be in the work stream.

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It's going to be in, you know, in the applications you purchase.

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It's going to be built into your work stream. One person shouldn't own it,

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but people should own data, right?

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Like I thought that was interesting and I agree with that perspective.

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So I walked away, all that kind of have informed my decision today to say,

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I have a feeling the folks in the room are just not involved in the solutioning for Gen AI.

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It's a different group. Totally makes sense to me as I think about it.

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But yeah, it was a great event. Lots of talk around governance.

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Yeah, I was really busy between the panels. The data democratization, that was the topic.

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So the panelists on that were incredible. And we ended up recording with FEMA,

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two separate podcasts on two separate topics with that panel.

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So that was a lot of fun as well, but it was a busy two days.

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Sounds like it. Sounds like it. And then you immediately hopped a plane,

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met me in New York with the next conference.

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Right. So we met at Data Universe, two-day conference there.

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It was their first time running the event in the United States.

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They have a sister event, same production company has a sister event in London.

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Big Data London, I believe. Correct. So they run Big Data London and Data Universe

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is their sister event here in the United States.

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This was the first time. It was an interesting space, wasn't it? Yeah, yeah.

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I mean, I've been to the Jacob Javits convention before for the big auto show.

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So to go there for something that that's a little bit more professionally driven was interesting.

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I think what was unique compared to some of the other conferences that I've

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been to over the last year, two years, was the fact that there were so many,

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like all the stages were in one place.

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So it wasn't separated by different rooms or different floors for presentations,

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but you were given headphones at each of the stages.

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I really enjoyed that. I would love to hear your thoughts on that multi-stage approach.

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Approach but I thought it allowed for a

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little bit more fluidity between different different

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talks as well as more networking

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I ran into a number of people that I've seen

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before spoken to before and just felt that

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that open concept allowed for a

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lot more connection with with people that

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we knew as well as new friends yeah I agree

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with the pros there I absolutely agree there was

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way more more networking that I think other layouts allow

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for because you spend so much time just walking from a

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like floor to floor area to area having it

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laid out the way they did in that open forum was really nice because

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you could always everyone was going back to the center where all the couches

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were and tables were to have a conversation if not that we're talking to the

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booths we I've seen more people at booths there than I did at other places because

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it was right there in your face you couldn't get away from it I really enjoyed

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that I think the downside having been on one of of the panels,

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as a panelist, the downside that I felt was the headphones.

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It did not really allow for interactivity in terms of the conversation you're trying to have.

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I think the only feedback I would give them is provide audience microphones

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for the Q&A at the end versus having the iPad question seem so anonymous.

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And I think the format in terms of the short hit time frame for the presentations

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could be a little challenging as well, because I think that the presentations

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ran about 20 minutes, which was really quick.

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Felt like we were just getting to the surface, barely scratching,

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whereas a little bit more time probably would have allowed for more depth and

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some of those conversations.

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Yeah, no, I totally agree. But there were a lot of great speakers there.

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I met the head of AI at Airbnb and heard her story. And that was like super impressive.

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It was funny because I met her on the couch before the event.

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I got there super early that day. It was the second day of the event.

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I got there very early, grabbed some coffee.

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There was maybe me and one other person there. And then all of a sudden,

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I'm looking around, it was all women. All the women showed up early. It was fascinating.

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So we sat on the couch and chit-chatted. I met a number of very interesting people there.

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So really, really cool layout for networking for sure. For sure.

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Looking forward to see how that evolves next year. Yeah, for sure.

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I mean, they had a very kind of wide-ranging set of agenda topics as well. Thank you.

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But kind of as a counterpoint to your FEMA experience, there were a lot of sessions

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on AI and Gen AI at this particular conference. I think I know why.

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I hate to say this because I appreciate it. I get a lot out of it.

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But I think some of these conference coordinators need to keep an eye out.

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There are a lot of data influencers out there these days. I mean,

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when I see data influencers creating companies to literally become data influencers,

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I get concerned because now you're just talking to talk.

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That's concerning to me in terms of a marketplace where technology is moving

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really fast to see that happening.

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Because the reality is the messages that are being put out there,

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like Gen AI is going to solve all world problems, is tough.

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It's tough to hear that. because I know on the ground that is not happening

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and there's still very large problems to solve.

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The Gen AI is not going to be the magic bullet for us. So yeah,

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I think that was kind of the disappointing aspects there.

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But I'm hoping that conference companies, organizations do what FEMA did.

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Like literally, if you're not part of an organization, a company and have a

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case study, you don't get to be in the room.

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I want organizations doing that more and more so that more of these real life

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use cases actually get on the table.

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Yeah, exactly. Make it real. Make it something that we can then look at and

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say, how do we embed this in our organization?

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How do we replicate that level of success? Similar fashion. Right.

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And then they wonder why clients aren't there. Well, clients aren't learning from other clients.

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You want the local, you know, CPD company or the manufacturing company,

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they're not going to show up to an event where they don't see themselves on stage.

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You want to see other organizations solving the problems that you're trying

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to solve and you want to learn from them.

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And if you're just hearing from, you know, talking heads or product vendors

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or even I'll even say consulting firms, that's a challenge. Yeah,

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I'm gonna eat my words on that later.

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But for now, that's where that's where I'm staying.

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You're cringing over there. I love it. You're like, Oh, no, no,

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because my mind's now racing going, Okay, so who are we bringing with us to

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really talk about a meaty success story?

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So we aren't that consultancy that's just, yeah, talking about what we could

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do, but really anchoring on what we have done, and what benefit it's brought

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to brought to an organization. Yeah.

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And, you know, I say that kind of tongue in cheek because obviously I was on

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a panel. That entire panel was consultants.

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We kind of turned the conversation on its side where the moderator acted like

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a client and we're trying to convince this client what this new terminology

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was of data intelligence.

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So it was a great conversation. I definitely got a lot of feedback from it.

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It seemed like it's a, you know, with newer topics where there aren't too many

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case studies, where there aren't too many folks who actually have done it or

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can you want a different perspective? Yeah, that makes sense.

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But to do that all time for the entire conference, that's a challenge. Yeah, I agree.

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That's why I called out the Airbnb one because it was fascinating.

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It was a real use case. It was about blockchain and Gen AI, a topic I had not even thought of.

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So that was super Super fascinating to me to hear that case study and to understand the application.

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Awesome. So we're going to, we're a couple of minutes away from Rachel Workman.

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Hey, Rachel. Hey, that was fun.

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That works. I was like, oh my goodness, I'm going to fail on the very first step, the technology.

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No, no. That was my sending you the wrong link trick. It works every time.

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You're like, we'll start with panic and then it'll be all uphill from there.

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Exactly. A little bit of panic helps.

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You get your heart pumping. You got to be ready to go and excited to be.

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Hopefully that did it for you.

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Sure. How are you today, Anjali? Pretty good. Pretty good. We're leaving for vacation tomorrow.

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So I'm starting to get into vacation mindset.

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So you get to that edge where you're like, I'm going to be free soon.

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You're never really free.

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See, I'm not gonna have to worry about my existentialness at all.

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You guys share that trait.

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Well, Sandy and I were actually talking about the luxury tax that you pay when

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you get back from vacation, right?

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It's almost like you're already tired from this amazing vacation that you had,

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but now all of a sudden there's this pile that you just have to start working through.

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And it's just almost like you're paying a penalty for having gone away for a little bit. You are.

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Like, I agree with you completely. It's not almost, it's you are because the

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work like doesn't go away and no people will cover for you on like really,

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really critical stuff, but they don't have time.

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Nobody has time to like pick up your work so

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all you've done is condense your work into smaller time period right

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i'm still a fan of vacation and right but the work didn't go anywhere that's

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hilarious i'm definitely a fan of the case i take way too much so rachel i actually

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did a little digging to figure out when did we meet i was wondering that Yeah,

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it was February 2017, in case you were wondering.

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So it's been a while. Seven years? Yeah, seven years now. And we engaged for about two months.

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I think I would have put it in time, but I can't believe we only engaged for like two months.

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Two months. It felt so much longer.

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I don't know if that's because of me or because of the product or because of

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the situation at the organization, which shall we name?

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Nameless. Yeah, it's really all like you were the bright spot in that project.

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I felt like you were one of the lone voices of sanity.

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You know what it is when you're in a project and the words come out of your

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mouth and you look around and you're like, no, nobody has an idea what I'm talking about.

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Everybody says things and it all sounds like it's like Peanuts characters.

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It's like you're like, nobody's making any sense. And then somebody makes sense.

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And you're like, this person makes sense. So it does. It like it leaves.

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This imprint in your brain. So I felt very much like that experience.

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Well, I appreciate that. I appreciate you. And quite frankly,

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I was surprised too. I was like, oh, it's two months. You left an impression on me, obviously.

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I think you and I have been reaching out to one another over the years.

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So it's mutual was my point.

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But at the time, you were the head of operations, the head of customer success.

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It seems like you had multiple roles there. Yeah, 2017.

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I think I was still over North American customer success and services.

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So I've got to know you for the past seven years. Maybe we can spend a little

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time educating Anjali and our listeners in terms of your past and who you are. Sure.

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So I'd love to. Rachel Workman. I currently am the head VP of data at a series

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A data platform startup company, SoundCommerce.

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I'm immersed in this field every single day. I'm doing cool things.

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My company built a tool that basically makes pipelines, building pipelines,

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more accessible and flexible.

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So the whole, you're playing in the whole low-code, no-code space,

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more flexible for how you shape data, pull semantic modeling in stream,

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as opposed to, and data at rest in the data warehouse,

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lands that data in maybe a more usable format, comes to like time to value and

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avoidance, cost avoidance of some of the data processing.

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So super cool things to be working on in the data space.

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It is my obsession, my passion, and my life in that space.

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But as you know, because you met me during that time, my path here was anything

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but conventional. If I look back, I started out, I started out on the right path.

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Well, how biased am I? One of my first jobs in grad school was database programmer

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for a high volume shipping system that, you know, built databases through SQL server.

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I think that might be where my love of this might have been born.

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You want the whole dinosaur story. We also build in bb.net. Yeah, that.

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Played me in time, but it wasn't too long after that, that I moved into,

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I wanted to see the business side, understand really more about why we were

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building the things we were building.

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And I moved into management consulting and then it's services leadership.

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And I spent the preponderance of my career on the business side,

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solving things from a business standpoint and running PNLs and all of those things.

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It was always tertiary to data and analytics as in every company I worked for,

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I always worked for software companies, had data products or analytics product.

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And so it was never too far, but definitely not on the technical side.

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And then really wasn't until age started to take over and you start asking yourself

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the questions of like, am I really doing the things I love that I gravitated back towards data?

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It was right around when you and I met that I was getting my data science master's

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big old leap after that, which is, well, let's go see who's going to buy into

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the fact that I can run on that side fully.

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And luckily, a great startup, Outlier AI, did and let me spend a couple years

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mired in the modern technologies and really hands-on stuff and my time preparing

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data from all different kinds of industries and companies for time series modeling.

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And I haven't really looked back since. That's my origin story.

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Such an interesting story. And I definitely want to dive into kind of your focus

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today and talking about data.

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But before we get into that, I'm just kind of curious, like what keeps you busy

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outside of the exciting world of data?

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Yes. So I am the mom to an 11-year-old boy.

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So that is the primary thing that keeps me busy.

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You know, as most parents know, your life revolves around things like common

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core math and mastering YouTube videos of common core math so that you can help

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with math worksheets and,

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you know, practicing spelling words of words that you thought you knew how to spell,

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but you look at them and go, maybe I don't know how to spell.

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How many M's are in commemorate? Right. So a lot of my time is taken up with those things.

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But when I am not basically revolving my life around him, I have two dogs, love them.

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My one dog's my jogging partner. I was going to say running partner,

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but I think my days of running are past. Definitely jogging territory now.

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I like to hike and I still love, as I have since probably the day I picked up

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my first book, Fantasy and Science Fiction.

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So read a lot of that. Well, read and listen to audiobooks because I could do that while I'm jogging.

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Combining multiple loves all at once, right? You got to be efficient, right?

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Well, being a busy mom, it's the efficiency, right? Right. I'm curious.

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I'm really into science fiction, mostly consuming it on media television.

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But is there a science fiction book that you're reading right now that you would

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recommend or a recent one?

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So recommending, I would recommend everything I read. Generally,

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I've actually mastered the art of putting a book down that I don't like.

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That took me a long time, but I will. If I don't like it, I'll put it down.

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So anything I read, I would recommend.

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But I definitely have interesting tastes. So right now I'm reading the Crossbreed

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series from Danica Dark.

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So I tend to gravitate towards strong female characters, usually in some type

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of urban fantasy type novels.

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So she's definitely somebody I've read a lot, and I think she does a good job.

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And I have to mention Shannon Mayer. I won't go on forever because,

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I mean, a list of authors would be hundreds long.

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But I think she would be a favorite of mine.

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And also, again, really strong female characters.

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But she does a 40-proof series, which is,

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you know, her heroine is, you know, across the 40 and has to learn how to,

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like, be a hero and not be 20 and tough anymore. So maybe that's closer to my heart.

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That sounds fascinating. We'd have to check out the 40-plus heroine stories.

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It takes some inspiration, I think. But so, Rachel, you talked about kind of

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your your journey and your career

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path and how you went from data to the business and then back to data.

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And in that transition, were there any surprises now that you're back on the data?

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Yeah, I think that some of the biggest surprises that I had going from side

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to side like that is how much of a goal there is,

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like how big the chasm is and how with all the maturing of business strategies

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and all of that, that we really haven't made tremendous stride in closing that chasm so much.

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Or as much as I think maybe we have the opportunity to.

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Having worked on both sides, not only are the languages just,

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and I'm not talking about programming languages or anything,

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but the language you speak, the vernacular you speak, the concepts that you

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ideate on and really rally around are just very different.

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But the biases are super strong.

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You see it surface in memes and stuff on LinkedIn where somebody else,

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and they all make us laugh.

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Right? So we know there's truth in them. Like where somebody's like,

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hey, can you throw that data to.

328
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For me real quick and you know stuff like that and you're like oh yeah

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that's a that's a week long task that you just

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think it's going to take five minutes and and vice versa where

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you know the the business side is like i really don't understand

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how it could take you that long to get to this number there's at least been

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acceptance in the last you know half decade to decade that we all like there's

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no solve to this problem other than like we all work together but i don't i

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i was just super surprised to see that we're still so far from that yeah i mean

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as you said there's no simple

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solve to this problem besides collaboration and communication and openness.

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And I mean, without that, you might as well just pack up and go home.

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We spend all these times and, and by the way, I'm a fan of some of the things that I'm mentioning,

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like I'm a fan of thinking of things from conceptual or framework things like

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data mesh and stuff like that, and different ways that you can build out organizations

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and collaboration strategies and all this to aid in these things.

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But we spend so much time thinking about those things, conceiving them,

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articulating them, and braiding them down and stuff like that.

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And we kind of skip over the whole like work together part of it.

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But I feel like it's really weird because the business world is almost the antithesis of academic.

347
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But at the same time, we take these colossal academic approaches to like,

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let's define a whole new way of working as opposed to like, let's sit down and

349
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solve this problem together.

350
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Right. Which should be the new way of working, right? Let's collaborate and

351
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drive that forward. word.

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But I guess, you know, as you think about that chasm, right,

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the wider that chasm has gotten, I've experienced at least, is the wider the

354
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chasm, the more friction between the data teams and the business.

355
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So how are you navigating that friction and that chasm in your daily life?

356
00:24:08,660 --> 00:24:14,780
Yeah, that's not a simple question at all, because that, you know,

357
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the chasm is real, and the vocabularies are not the same.

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And even the slant on the same vocabularies aren't the same.

359
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So, and I'll give you an example. Data trust is super important to data teams

360
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and data trust is super important to the business, right?

361
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But if you're on the data team side and you're talking about data trust,

362
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you're going to talk about things like fidelity to source or observability or

363
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visible lineage or complete lineage or complete and transferable metadata. of data.

364
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And you're going to think about these things and you're going to think about

365
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the pieces and parts that give you trust that the data is accurate to your specifications. But

366
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You may be okay with things like 1% variances in certain things,

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right? And stuff like that.

368
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And you're going to maybe think of things like mathematically or statistically,

369
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but you're going to be okay with parameters like that.

370
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If you immerse yourself fully on the business side, you walk into a conversation

371
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that has similar things.

372
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You know, people are going to want things like clear definition of metrics,

373
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right? You know, how did you build this? What's the math?

374
00:25:18,378 --> 00:25:21,078
You know, show me the math, show me the path. Like, where did it come from?

375
00:25:21,158 --> 00:25:23,138
Show me, you know, how do you create that?

376
00:25:23,178 --> 00:25:27,678
And what's the math behind it? But I've seen whole conversations just completely

377
00:25:27,678 --> 00:25:32,898
implode when somebody's, oh, it's within the tolerance, the air tolerance.

378
00:25:32,958 --> 00:25:34,218
And the business side is like, what?

379
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Like, what's missing? Why is it missing?

380
00:25:37,418 --> 00:25:40,598
Where is it going? And how do I know that it's not something really important

381
00:25:40,598 --> 00:25:42,938
that's missing? Now everything's suspect, right?

382
00:25:43,278 --> 00:25:47,438
And difference in attitude that seems to cause a lot of challenges.

383
00:25:47,438 --> 00:25:52,178
Challenges and navigating that, I have found some success or at least learned

384
00:25:52,178 --> 00:25:53,998
some things about trying to

385
00:25:53,998 --> 00:25:57,038
avoid different trigger words that come off as kind of flippant like that.

386
00:25:57,218 --> 00:26:01,458
It's important if there's a rattle in your engine and you're driving your car

387
00:26:01,458 --> 00:26:05,178
down the road and you have no idea how engines work, you're going to be pretty scared.

388
00:26:05,338 --> 00:26:07,758
But if you're a mechanic and you're like, that's the heat panel.

389
00:26:07,878 --> 00:26:08,818
You're like, I'm driving along

390
00:26:08,818 --> 00:26:10,978
with my stepdad. He's like, that's the heat panel. Don't worry about that.

391
00:26:11,038 --> 00:26:14,338
You're fine. And I'm like, yeah, really? Am I? Is it going to blow up?

392
00:26:14,598 --> 00:26:18,958
Because I don't feel fine. That's a weird noise. And those types of like really

393
00:26:18,958 --> 00:26:23,778
relatable things are what's happening in these this type of dynamic between these teams.

394
00:26:23,798 --> 00:26:26,978
Again, to the thing that we seem to throw away super easily,

395
00:26:27,098 --> 00:26:31,498
which is the human connection of really trying to understand each other a little bit better.

396
00:26:31,618 --> 00:26:36,218
Yeah, I find that if you throw the jargon out the door and meet somebody where

397
00:26:36,218 --> 00:26:40,938
they're at and try to understand how do you think about this and what are the

398
00:26:40,938 --> 00:26:43,618
things that matter matter to you as an individual.

399
00:26:43,878 --> 00:26:48,218
It's hard as a group, but I think if that individual is a representative of

400
00:26:48,218 --> 00:26:51,638
the group, their peers, you can kind of try to get an understanding from at

401
00:26:51,638 --> 00:26:55,098
least that individual in terms of how they think, what is it that they're worried about?

402
00:26:55,318 --> 00:26:57,918
What is their tolerance level in business terms?

403
00:26:58,158 --> 00:27:02,278
And try to set that all aside ahead of time so that you kind of know where you're

404
00:27:02,278 --> 00:27:05,658
going to go as you engage with them and as you move forward.

405
00:27:05,878 --> 00:27:09,458
And I find that a lot of people miss that step. They go in and they immediately

406
00:27:09,458 --> 00:27:13,838
engage, assuming that they're on the same page, assuming that they understand

407
00:27:13,838 --> 00:27:15,058
what's important to one another.

408
00:27:15,218 --> 00:27:19,958
And that's a complete misstep for most people who are in this world.

409
00:27:20,018 --> 00:27:22,658
And that's why the chasm just gets bigger and wider.

410
00:27:23,096 --> 00:27:25,556
Because everyone's making assumptions about the other side of it.

411
00:27:25,636 --> 00:27:30,496
Yeah, the spectacular miscommunications that come from those assumptions.

412
00:27:30,636 --> 00:27:33,736
One of the biggest ones that I have seen, and again,

413
00:27:33,936 --> 00:27:39,376
you know my bias from being on the business side so much of my career and now

414
00:27:39,376 --> 00:27:41,176
being completely immersed in the data side,

415
00:27:41,296 --> 00:27:47,796
is the assumption that the business side somehow doesn't understand maybe the

416
00:27:47,796 --> 00:27:50,496
math of the situation as well.

417
00:27:50,496 --> 00:27:55,816
It's the rare individual on the business side that sits down and really likes

418
00:27:55,816 --> 00:27:59,036
to actually work out the math of a gradient regression.

419
00:27:59,716 --> 00:28:06,156
That's things that maybe a few amount of people like to do. That doesn't mean that they can't.

420
00:28:06,696 --> 00:28:10,856
Once they sit down and talk to one another, they're surprised that there's people

421
00:28:10,856 --> 00:28:14,176
on the business side that can work out supply chain forecasts in their head

422
00:28:14,176 --> 00:28:19,176
and really understand very complicated concepts at a very detailed level.

423
00:28:19,176 --> 00:28:23,876
And there's a lot more commonality there than you think there is.

424
00:28:23,976 --> 00:28:26,796
It's just coming from different angles. Yeah, absolutely.

425
00:28:27,136 --> 00:28:32,616
It was funny because I actually met someone who once said, he said that companies

426
00:28:32,616 --> 00:28:34,456
don't understand basic economics.

427
00:28:34,656 --> 00:28:39,036
And I'm sitting there going, wait, most people I meet who are on the business

428
00:28:39,036 --> 00:28:43,436
side either have economics background or an engineering background,

429
00:28:43,496 --> 00:28:47,956
like C-level executives I've met who have engineering backgrounds and they're business executives.

430
00:28:48,036 --> 00:28:51,516
And I'm sitting there going, do not dismiss people because of their title.

431
00:28:51,616 --> 00:28:53,396
They know more than people think.

432
00:28:53,596 --> 00:29:00,936
And I also think that reality is most of these concepts can be distilled down to very basic terms.

433
00:29:01,176 --> 00:29:06,296
Yeah. Yeah. And I think part of it is also around language, right? Language matters.

434
00:29:06,756 --> 00:29:12,556
And so one of the things that we've seen happening quite frequently is this movement of resources.

435
00:29:12,876 --> 00:29:18,936
So as one priority comes up, we're moving our people from, you know,

436
00:29:18,936 --> 00:29:23,276
from priority one to priority two, and they're taking the language and learnings

437
00:29:23,276 --> 00:29:27,236
that made them successful initially to this new role,

438
00:29:27,356 --> 00:29:28,936
new priority, new shiny object.

439
00:29:29,256 --> 00:29:31,176
So are you encountering that today?

440
00:29:31,736 --> 00:29:35,296
Yes. I, and I, I think that I would have answered the question,

441
00:29:35,436 --> 00:29:40,796
the, that question a little bit differently before Gen AI became a thing.

442
00:29:40,876 --> 00:29:44,736
Now, something that like keeps me awake at night, there's always been a bit

443
00:29:44,736 --> 00:29:46,536
of whole, like shiny new thing.

444
00:29:46,696 --> 00:29:50,036
And I think there's a couple of things that personally in my life have changed

445
00:29:50,036 --> 00:29:53,956
that maybe put me in closer into a group that does that more,

446
00:29:54,076 --> 00:29:57,596
you know, which is the startup culture and Silicon Valley.

447
00:29:57,756 --> 00:30:01,616
There's things you can't ignore. People give money to shiny new things,

448
00:30:01,776 --> 00:30:05,376
you know, more than they give money to boring old things.

449
00:30:05,476 --> 00:30:09,136
And that it is what it is. But there's a real cost to that.

450
00:30:09,416 --> 00:30:11,556
Context switching has a cost.

451
00:30:11,956 --> 00:30:16,956
It is a mental tax on the people who her context switching and it has efficiency

452
00:30:16,956 --> 00:30:19,456
cost and it has a tech debt cost.

453
00:30:19,910 --> 00:30:25,170
On things that are basically done short so that you can move on to that new thing.

454
00:30:25,370 --> 00:30:31,050
And all those things need to be talked about and honored within the context

455
00:30:31,050 --> 00:30:36,270
of what we've been talking about, which is that gulf between the business side and the data side,

456
00:30:36,410 --> 00:30:42,890
making sure that we really use our voices and try to articulate the cost of

457
00:30:42,890 --> 00:30:47,670
making those moves and the risk and find the right words.

458
00:30:47,890 --> 00:30:53,390
I see it as like a tug of war Never give up. Just always keep pulling and don't

459
00:30:53,390 --> 00:30:54,850
lose your ground on those things.

460
00:30:55,030 --> 00:31:02,050
Because the cost of allowing that to happen, especially if you hold innate knowledge

461
00:31:02,050 --> 00:31:03,970
of possibly can go wrong.

462
00:31:04,070 --> 00:31:08,770
And that's why I said Jenny and I kind of hold a scar that I think every single

463
00:31:08,770 --> 00:31:13,670
one of us who works in data every single day watched this whole LLM thing unfold

464
00:31:13,670 --> 00:31:16,350
going, what about the data?

465
00:31:16,350 --> 00:31:21,650
And all of us, it was almost like watching a slow-moving accident unfold.

466
00:31:22,490 --> 00:31:27,050
We all knew there was so much excitement and so much momentum.

467
00:31:27,070 --> 00:31:31,230
And I feel personally, in my circle, we just never found the words.

468
00:31:31,310 --> 00:31:34,690
As we all have found out in business, I told you, so it never matters.

469
00:31:34,870 --> 00:31:38,070
By the time you get to it, I told you, so you've lost the battle, right?

470
00:31:38,230 --> 00:31:43,070
If I look at that from a lessons learned standpoint of how we can learn to work

471
00:31:43,070 --> 00:31:47,210
together better, or like finding those words to articulate and not bore the

472
00:31:47,210 --> 00:31:50,970
people who are excited, you move the resources to the shiny new thing.

473
00:31:51,110 --> 00:31:56,490
You might have a super cool prototype that is flashy and everybody loves.

474
00:31:56,850 --> 00:32:00,970
And if the purpose of that, if the objective of that was to get like an investment

475
00:32:00,970 --> 00:32:02,330
or something, that's fine.

476
00:32:02,610 --> 00:32:08,850
If your purpose was to productionalize, was to get a business goal and business

477
00:32:08,850 --> 00:32:14,730
value out of it, you moved resources from the thing that was building the underpinnings

478
00:32:14,730 --> 00:32:16,870
of it. And now you've made your path longer.

479
00:32:16,990 --> 00:32:20,810
And being able to have those discussions is super important.

480
00:32:21,170 --> 00:32:26,130
Yeah, I was just gonna say, I find that to be the case regardless of whether

481
00:32:26,130 --> 00:32:31,930
it's JAI or any other new tech that comes along usually or concept or issue

482
00:32:31,930 --> 00:32:33,770
that an organization is trying to address.

483
00:32:34,410 --> 00:32:39,710
Orgs still have not gotten smart to the fact that you still have to run your operations.

484
00:32:39,710 --> 00:32:45,210
Innovations and if you're if you're going to do r&d innovation that should be

485
00:32:45,210 --> 00:32:49,650
a completely different arm of your business even in a data it should be separate

486
00:32:49,650 --> 00:32:53,090
because then you can make those calls and say all right we were going to innovate

487
00:32:53,090 --> 00:32:55,450
on x here was investment we made

488
00:32:55,865 --> 00:33:00,425
If we're going to shove that, we already have a sunk cost on that investment with that team.

489
00:33:00,605 --> 00:33:04,665
Do we spin up a secondary team to run this secondary investment so that we don't

490
00:33:04,665 --> 00:33:08,045
lose sight of the sunk cost we already have, you know, proposed value?

491
00:33:08,305 --> 00:33:10,125
Get in there, iterate real fast.

492
00:33:10,505 --> 00:33:15,885
You do have to R&D it. There's a concept that I'm sort of obsessed with on the

493
00:33:15,885 --> 00:33:18,325
side. It's sound metric trees.

494
00:33:18,685 --> 00:33:23,025
One of the things that I find very useful in a well-constructed metric tree

495
00:33:23,025 --> 00:33:25,905
is that That idea of isolating the

496
00:33:25,905 --> 00:33:30,105
portions of the business and the proportionality of the business impact.

497
00:33:30,225 --> 00:33:34,105
You can isolate the business impact at the right level.

498
00:33:34,805 --> 00:33:38,465
You can't drink two consecutive cups of coffee without somebody asking you about

499
00:33:38,465 --> 00:33:42,545
the value you're driving or articulating the value you're driving or quantifying

500
00:33:42,545 --> 00:33:43,625
the value you're driving.

501
00:33:43,705 --> 00:33:48,825
Finding ways to make you more immune to being led off track into shiny things

502
00:33:48,825 --> 00:33:52,625
that might be super cool, but are going to take a lot of time and impact a very

503
00:33:52,625 --> 00:33:56,225
small amount of the business seems like a survival skill these days. Yeah.

504
00:33:56,385 --> 00:34:00,945
So you, in order to do that, you've been using this idea of creating kind of

505
00:34:00,945 --> 00:34:04,645
a visual metric tree to state, really quantify the impact of the ask.

506
00:34:05,165 --> 00:34:09,785
I actually just wrote down metric trees because my brain just started firing

507
00:34:09,785 --> 00:34:14,445
on all cylinders as you said that, because there's always a challenge in terms

508
00:34:14,445 --> 00:34:16,225
of being able to articulate ROI.

509
00:34:16,445 --> 00:34:20,365
I've done some workshop on this. And the first thing I always ask is,

510
00:34:20,365 --> 00:34:21,985
is do you really understand what drives the business?

511
00:34:22,605 --> 00:34:26,645
Every ask out there has to help whatever drives the business.

512
00:34:26,805 --> 00:34:31,425
And if it doesn't naturally impact it and you're not able to quantify it, then you have no ROI.

513
00:34:32,090 --> 00:34:36,050
You can understand whether it influences some of that, but even the impact of

514
00:34:36,050 --> 00:34:37,670
the influence can be measured.

515
00:34:37,870 --> 00:34:41,490
I love that idea of metric trees, having that within an organization.

516
00:34:41,690 --> 00:34:46,450
And that's an opportunity for these data catalog companies to actually create

517
00:34:46,450 --> 00:34:47,450
it as part of the catalog.

518
00:34:47,690 --> 00:34:52,490
Data catalogs are actually pretty flat. There's no this metric impacts that

519
00:34:52,490 --> 00:34:55,310
metric impacts that definition, et cetera. Right.

520
00:34:55,430 --> 00:34:59,610
And it is that relationship that's key. And it's why I get fascinated by it

521
00:34:59,610 --> 00:35:01,830
and really excited by it.

522
00:35:02,090 --> 00:35:04,630
That you can create an ROI story about anything.

523
00:35:05,230 --> 00:35:07,950
You've got a whole concept of what are you trying to optimize?

524
00:35:08,110 --> 00:35:10,170
What are you trying to maximize? What are you trying to minimize?

525
00:35:10,470 --> 00:35:14,890
It's all interrelated and impacted by business strategy.

526
00:35:15,230 --> 00:35:17,910
You know, am I trying to, you know, increase my brand awareness?

527
00:35:18,110 --> 00:35:22,470
Am I trying to increase my local or high intent conversions?

528
00:35:22,610 --> 00:35:25,370
Like it's all in the context of business.

529
00:35:25,570 --> 00:35:28,990
And if you look at the metrics by, I like the word you used,

530
00:35:29,050 --> 00:35:35,810
flat, You've lost the context because you can be focusing on minimizing or maximizing

531
00:35:35,810 --> 00:35:40,670
a metric that actually is like three levels deep from a more meaningful metric.

532
00:35:40,750 --> 00:35:45,450
And you're focusing all your effort on, let's say, you know,

533
00:35:45,450 --> 00:35:48,850
minimizing something that's supposed to minimize costs and it's responsible

534
00:35:48,850 --> 00:35:51,630
for, you know, less than 5% of your overall cost.

535
00:35:51,730 --> 00:35:55,250
Until you map it out and really start to drive those relationships,

536
00:35:55,570 --> 00:35:59,030
you lose the context. Next concept's been around for a long time,

537
00:35:59,070 --> 00:36:00,390
whole balanced scorecard.

538
00:36:00,510 --> 00:36:04,550
Let's understand how our business actually runs and what are all the drivers

539
00:36:04,550 --> 00:36:05,670
to this part of our business.

540
00:36:05,870 --> 00:36:10,070
And that could lead to not only just for the sake of understanding the value

541
00:36:10,070 --> 00:36:12,950
of the data, the impact and the proportionality, as you said,

542
00:36:12,950 --> 00:36:18,270
of the value of those things, but also how we report on it and how we look at it.

543
00:36:18,350 --> 00:36:21,150
There's so much value in that and nobody does it.

544
00:36:21,210 --> 00:36:25,110
And then you can also almost circle around it because you have different parts

545
00:36:25,110 --> 00:36:27,550
of the the organization that all feed into the same number.

546
00:36:27,630 --> 00:36:31,750
So you can kind of start looking at things differently in that respect as well.

547
00:36:31,910 --> 00:36:33,690
These parts of the business work in isolation.

548
00:36:34,170 --> 00:36:38,650
And then somebody at the top who needs data from all different parties is sitting

549
00:36:38,650 --> 00:36:42,070
there going, well, I don't understand why I can't get this number.

550
00:36:42,230 --> 00:36:46,490
Let's go look at the metric tree. So let me tell you how data comes into a warehouse.

551
00:36:46,610 --> 00:36:50,710
Now I'm going back to that metric tree and telling you it's because I have 18

552
00:36:50,710 --> 00:36:53,490
different metrics that impact that number, and I can't get six of them.

553
00:36:53,849 --> 00:36:58,009
Right. Yeah, I can't get six of those. Or you show somebody at that level that

554
00:36:58,009 --> 00:37:01,189
you're talking about, you know, you take a C-level person, they can then do

555
00:37:01,189 --> 00:37:03,289
the kind of that mental math and be like, okay,

556
00:37:03,509 --> 00:37:08,649
there's some key areas of concern here in the pipes that are causing me to not

557
00:37:08,649 --> 00:37:09,669
be able to get this number.

558
00:37:09,789 --> 00:37:13,049
How does that help me contextualize my priorities? Exactly.

559
00:37:13,109 --> 00:37:17,509
Because we hear time and again, you know, I'm asked what the value of my data

560
00:37:17,509 --> 00:37:22,889
is, and organizations struggle to articulate that. So I think this is absolutely fascinating.

561
00:37:23,109 --> 00:37:28,509
I'd love to kind of pick it. How do we, you know, roll that out to our teams

562
00:37:28,509 --> 00:37:32,349
to ensure that they are looking at developing those metrics,

563
00:37:32,589 --> 00:37:38,329
developing those metric trees, and then actually communicating along those lines? Yes.

564
00:37:38,609 --> 00:37:42,569
So I think I've hinted at it. It's kind of where I'd stake my,

565
00:37:42,569 --> 00:37:46,389
you know, my flag is on almost like back to the basics type of stuff,

566
00:37:46,529 --> 00:37:51,229
which is dispense with a lot of the lofty ivory tower stuff and give people

567
00:37:51,229 --> 00:37:56,209
real project with clear boundaries and clear objectives to work on together.

568
00:37:56,209 --> 00:37:59,909
What I'm really talking about is the fundamental team building and trust building,

569
00:38:00,049 --> 00:38:04,329
the type of synergies that comes out of real teams solving real problems together.

570
00:38:04,629 --> 00:38:09,029
You sit down in a room together with a whiteboard and you spend a couple weeks

571
00:38:09,029 --> 00:38:10,429
really solving a tough problem.

572
00:38:10,609 --> 00:38:16,049
There's some real teamwork and trust and shared experiences that comes out of

573
00:38:16,049 --> 00:38:17,749
that. They're going to share information.

574
00:38:18,049 --> 00:38:23,009
They're going to learn to trust one another in the situations where they don't

575
00:38:23,009 --> 00:38:23,929
understand one another.

576
00:38:24,109 --> 00:38:29,129
You need that because you can't take somebody who's entrenched on the data side

577
00:38:29,129 --> 00:38:30,529
and teach them everything about business.

578
00:38:30,989 --> 00:38:34,149
I'm sure you can, given enough time and their inclination, you could.

579
00:38:34,149 --> 00:38:37,569
You don't have that type of time and money in business and vice versa.

580
00:38:37,649 --> 00:38:41,189
You can't take somebody on the business side and you don't have the time to

581
00:38:41,189 --> 00:38:43,089
make them a fully skilled data practitioner.

582
00:38:43,289 --> 00:38:48,189
You have to learn to trust others when you don't understand them and those real

583
00:38:48,189 --> 00:38:49,609
world practicum practices.

584
00:38:49,880 --> 00:38:55,080
Activities seem to be really key. And I would tell any data leader out there

585
00:38:55,080 --> 00:38:57,920
to find those opportunities and rally around them.

586
00:38:58,060 --> 00:39:04,000
Yeah. And I would add to that as well, making it okay to take a risk and potentially

587
00:39:04,000 --> 00:39:07,720
fail in that exercise. That's key. That's super key.

588
00:39:08,120 --> 00:39:14,040
But I heard the saying, you don't succeed or fail, you succeed or you learn.

589
00:39:14,240 --> 00:39:17,580
And I forget where it came from. I think it was either some commercial or some

590
00:39:17,580 --> 00:39:20,960
like athlete or something said it, but it really is true.

591
00:39:21,200 --> 00:39:24,680
If you look at it the right way, when you fail, that's it, you learn.

592
00:39:24,820 --> 00:39:28,700
And really engendering that within our teams is really core.

593
00:39:28,920 --> 00:39:31,760
Yeah, I think it was actually a Tiger Woods commercial where at the end,

594
00:39:31,780 --> 00:39:36,660
his dad, I think so, where his dad says, and what did you learn from that?

595
00:39:37,560 --> 00:39:42,620
So I know we're coming up on time. So just one last question for you, Rachel.

596
00:39:43,000 --> 00:39:49,560
If you can have dinner with anybody from history over time and discuss the future

597
00:39:49,560 --> 00:39:52,700
of data, who would it be and what would you ask them?

598
00:39:52,880 --> 00:39:56,100
This is one of the hardest questions that anybody ever asked.

599
00:39:56,240 --> 00:39:59,260
I think this might be a little weird of an answer because it really,

600
00:39:59,360 --> 00:40:03,280
in my opinion, doesn't have anything to do with data, except for in a tangential way.

601
00:40:03,580 --> 00:40:09,200
But I would pick somebody like Sophie Germain or Nellie Bly.

602
00:40:09,460 --> 00:40:13,100
There are people in history, little known, usually obsessed with,

603
00:40:13,180 --> 00:40:16,220
obviously, I'm a woman, And so I'm like, oh, what about the woman in the woman's

604
00:40:16,220 --> 00:40:18,160
voice and the lost woman's voice over time?

605
00:40:18,320 --> 00:40:23,320
I would want to sit down with them and I I wouldn't ask them anything about data.

606
00:40:23,400 --> 00:40:28,500
I mean, I would ask them, what do you do when it gets too hard?

607
00:40:28,880 --> 00:40:34,220
Because being in a technology space as it is, is tough.

608
00:40:34,360 --> 00:40:37,220
That's tough. Being in a space that is dominated.

609
00:40:37,889 --> 00:40:42,269
By other than women. And this isn't, you know, I'm speaking women-centric.

610
00:40:42,329 --> 00:40:45,329
This has to do with a lot of people who are underrepresented in this field.

611
00:40:45,809 --> 00:40:51,049
We've talked about so many tough concepts and challenges about getting people

612
00:40:51,049 --> 00:40:54,589
to work together and challenges about getting ideas across and all those things,

613
00:40:54,689 --> 00:40:55,989
right? They're all tough as it is.

614
00:40:56,089 --> 00:40:59,669
I think I mentioned earlier, I see a lot of things like a tug of war where if

615
00:40:59,669 --> 00:41:05,909
you let up for a second, you've lost ground that you now have to spend capital to make up.

616
00:41:06,029 --> 00:41:11,329
And there are days when you look around, you're like, do I have it in me to

617
00:41:11,329 --> 00:41:13,669
try to find the words that I can't find?

618
00:41:13,989 --> 00:41:18,949
Do I have it in me to try to really dig to understand that other position?

619
00:41:19,189 --> 00:41:22,589
Because I know that's what's getting in the way of this. And you look back and

620
00:41:22,589 --> 00:41:26,249
there's just these amazing people that did amazing things.

621
00:41:26,409 --> 00:41:30,669
Sophie Germain, she had no support, no representation, no models.

622
00:41:30,669 --> 00:41:36,829
She completely dominated a mathematical space, basically was able to bring to

623
00:41:36,829 --> 00:41:39,569
fruition a math theorem that many tried and failed.

624
00:41:39,569 --> 00:41:43,849
Nellie Bly picked one of the most unrepresented groups of the time,

625
00:41:43,889 --> 00:41:48,509
like mental patients, knew they would be treated unfairly. She was a journalist.

626
00:41:48,749 --> 00:41:52,669
She could have lived her life quite happily, I'm sure, reporting on things other than.

627
00:41:52,829 --> 00:41:57,569
She went undercover in a mental hospital where she knew people were being treated

628
00:41:57,569 --> 00:42:03,389
abhorrently and used that to drive amazing and lasting change in the world.

629
00:42:03,489 --> 00:42:05,769
Those people could really tell you what was tough.

630
00:42:05,969 --> 00:42:09,509
I'm not minimizing anybody's struggles, but I'm curious, what did they think

631
00:42:09,509 --> 00:42:13,609
about when the days were hard? Or did they just not think about it? Was that their secret?

632
00:42:13,809 --> 00:42:17,209
I want to know that. And it might not be the best dinner conversation.

633
00:42:17,629 --> 00:42:21,249
But I would love to have that conversation.

634
00:42:21,669 --> 00:42:25,569
I think that'd be a fascinating conversation. I love those figures.

635
00:42:25,749 --> 00:42:27,969
I love their stories and what they

636
00:42:27,969 --> 00:42:34,469
overcame to really drive that long-term improvement in people's lives.

637
00:42:34,469 --> 00:42:40,209
And so much of what we see in data isn't the technology, but it's the people,

638
00:42:40,309 --> 00:42:43,289
the people surrounding the data and the people working with the data.

639
00:42:43,289 --> 00:42:46,409
I love the data is going to change. It's going to get bigger.

640
00:42:46,489 --> 00:42:47,309
It's going to get different.

641
00:42:47,429 --> 00:42:50,189
It's going to have more privacy rules. It's going to have less privacy rules.

642
00:42:50,649 --> 00:42:55,429
But the people are going to stay, you know, the consistent part,

643
00:42:55,589 --> 00:43:01,949
you know, up until the generative AI LLMs are, you know, embedded in the cyborgs

644
00:43:01,949 --> 00:43:06,349
and those take over, you know, next week. Then we'll all be talking about it. Exactly.

645
00:43:08,249 --> 00:43:12,369
Well, Rachel, thank you so much coming in on to the podcast with us.

646
00:43:12,429 --> 00:43:14,349
We really appreciate it. And thank you both.

647
00:43:14,509 --> 00:43:18,209
I really appreciated the opportunity. And this was by far the best.

648
00:43:18,640 --> 00:43:34,817
Music.