Snowflake, What The Mesh?! Featuring James Anderson

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Welcome back. Sandy here, and I have some good news and some bad news to share about this episode.

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The good news is we have a guest joining us. The bad news is he's a vendor.

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But all jokes aside, Anjali and I had a fantastic time chatting with James Anderson.

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He's an ex-consultant and current director of cloud data architecture at Snowflake.

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James and I go way back, and I've always enjoyed our spirited debates,

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which is exactly why Anjali and I decided to invite him to the podcast. guests.

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We couldn't think of a livelier guest to kick things off with.

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Yes, we start by discussing Snowflake, of course, but then we move into other

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topics like data mesh, changing dynamics of dashboard usage and data analytics.

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And we have a quick debate on the future of data work. We hope you enjoy listening

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to this discussion as much as we enjoyed having it. So let's get started.

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Music.

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Nice. You know when your recording's on and you have the little red flashing

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light back in the studio back in the day? Uh-huh.

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This is outrageous. And I like that

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we're recording now, this part of this conversation. It makes me happy.

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I hope this makes it into the podcast. You said I didn't hit the button earlier. That's what I'm...

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All right. Are you mentally prepared? You want to get into this?

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Am I mentally prepared? I was on a red-eye flight last night.

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I'm never mentally prepared. So let's just... Let's roll.

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Red Eye. Where did you come from? San Francisco. I've been flying around a lot. Back and forth to HQ.

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So James, I've known you for a while now.

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For a long while. I don't even know how long. But how about you? Almost 10 years.

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I've been at Cervelo for 11. So that's kind of incredible.

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Now that I think about it. I started working at Cervelo in November of 2014.

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I didn't realize that for some reason. But why don't you introduce yourself

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to Anjali and our listeners while you're at it? Sure.

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Yeah. So my name is James Anderson. I used to work with Sandy and the team at

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Cervelo for I think it was about two years I was there.

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I then moved on to another consulting firm.

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You know, I started my career at Cervelo as a burst developer.

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I was a front end front end dashboard guy building very beautiful dashboards in burst as one one does.

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And then as I moved in through my consulting career, I moved myself down the stack.

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And by the time I left the consulting space, I was running the platform architecture

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team for the firm I was at focused on how do you build and deploy large scale

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enterprise wide data platforms?

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And how do you fit that into a broader enterprise architecture,

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I left consulting in 2020, right at the height of the pandemic moved over to

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snowflake because I had been doing almost exclusively snowflake implementations

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for at least the previous four years before that.

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So I've been in and around the snowflake ecosystem for a long time.

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And I have been at snowflake now for just shy of four years.

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For the first couple of years, I ran a number of different sales engineering

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teams focused on first large accounts in the greater New England area.

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And then for the last two years, I had been running the sales engineering team

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focused on large life sciences organizations.

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So pharmaceutical, medical device, medical distribution, CRO.

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And starting February, I moved into our new data cloud architecture team focused

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on how do you fit Snowflake into to a more broad enterprise architecture.

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That's my complete end-to-end life that I have had in the data space at this

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point. What do you do outside of the data space?

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What kind of hobbies or interests do you have? Or do you work so hard you have no hobbies, interests?

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Well, in the last month or so, let's see, I've been on 10 planes in the last 20 days.

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So I don't really have time for other things outside of that right this second.

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In general, I have two young children who I adore.

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Immensely and spend most of my time entertaining or finding ways to entertain.

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I also like to play golf like any good data executive likes to do.

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Those are my main hobbies if I have time for those things.

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And we'll see if that ever comes back, if I ever get more time for that.

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So is that a newly formed team at Snowflake? Yeah.

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So historically, when our customers had come to us and said,

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hey, what is our recommended architectural approach?

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Hey, should we run a data warehouse? Should we transition to to being more of

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a data lake type of construct?

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What do you guys think about data fabric? Definitely getting a lot of people asking about data mesh.

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In the past, our answer was, oh, you can do anything you want.

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As an organization, we're really trying to get our customers to think about

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Snowflake, not just as a cloud data warehouse or cloud data lake or something

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of that nature, but really understand that this is a full-blown end-to-end data platform.

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And so we've built an architectural framework that we call the data cloud architecture,

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to help our customers who want to truly adopt Snowflake as a platform and get

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better value out of the data that they have.

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A lot of this framework is around how do you tie value to what you're building?

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And how do you treat the things that you're building not as tools for decision

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making, but instead as an asset that has a measurable ROI?

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In the past, when a client says, says, hey, where does this fit in my larger architecture?

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The answer would naturally be go to consultant X who's worked across a lot of technologies, right?

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To help you pull that together. So now it seems like Snowflake's building a

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team that has an opinion, which is fine.

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Find it interesting to see how clients react to that because you're still getting

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the advice from the vendor at the end of the day.

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Yeah, so one of the things that I tell customers from the jump.

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Is that my team is certainly going to look at this from a snowflake first perspective,

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but we're not going to look at it from a snowflake only perspective.

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We are a little bit trying to provide some level of free consulting to help lay that foundation.

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Now, we're not going to then turn around and do your whole implementation for

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free for you for all the way through, right?

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We're going to rely on our partners like Cervelo to actually take this sort

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of architectural vision and implement it and put it in place.

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Because there's a lot, It's not just a technical underpinnings, right?

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There's a shift in how you think, and there's a change management process that

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has to be taken into account.

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So as you're talking about this, I guess because Snowflake made this shift,

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it sounds like that's been a big trend, right?

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Clients really trying to figure out not just what is this technology itself

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in isolation, but how does it interplay with all the other things I'm trying to do in my stack?

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The question you're trying to answer for them is, here are the other pieces

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of my stack. Here's the other things I'm trying to achieve.

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Help me put this wonderful modular puzzle together that has been created as

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the modern, quote unquote, modern stack.

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It's modular, right? That's usually what people push for.

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I know companies are moving in certain directions, but that's typically what people push for today.

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Are there any other trends that you're seeing out there? I mean,

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data mesh is the most popular conversation that we've had.

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I mean, is it not the most popular conversation you've had in the last year

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and a half? Like, really? No.

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Really? Yeah. That's surprising, because the number of customers that have come

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to me and said, Hey, I have this partner who's really pushing the data mesh paradigm.

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What do you guys think? And how can Snowflake support this data mesh strategy?

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Right? Is outrageous. I think last summer I had this conversation with more

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than half of the customers that I supported.

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Everybody likes this idea of domain ownership and putting the capabilities and

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the infrastructure in the hands of the people who actually understand the data.

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On the flip side, when it comes to a true data mesh, the idea of domain ownership

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includes the idea that you have engineers inside of each domain who will take

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who will ingest process and create these data products.

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And I have a whole beef on the term data product, which we can get into later.

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But there's a level of sort of organizational change that goes into how you

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staff. Yeah, or how you organize period, right.

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So from our frameworks perspective, from this sort of data cloud architecture

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perspective, we're taking a lot of the positives of data mesh in terms of,

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we want to enable that free flow of data and applications across your ecosystem,

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we want to make it easier for you to collaborate with your business and with your partners.

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And with your customers even, but not do it in a way where you have to actually

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transform how you run your business.

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And I understand that there are consulting firms out there who specialize in

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business transformation.

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So this is very appealing to them, because there's a huge project that comes behind this.

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So I think that's one. The other is obviously generative AI and everything around

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the whole generative AI space.

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And I know when I was listening to your first episode, I was texting Sandy with

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all sorts of opinions on this.

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Well, that's how you ended up on the podcast. You were texting me right after

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our first episode, So you're sending me these rant texts about Gen AI.

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And I was like, wow, we got to get him on. He's ready to go.

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I was live texting you as I was listening with my live feedback. Yes.

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But would you expect me to do it any other way, Sandy? I mean,

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come on. No, absolutely not. That's what I love about you, James.

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I think when it comes to data mesh, yes, we do have clients who are thinking about it.

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We don't push it as a consulting firm. And I think because we know,

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we know what it takes. And when I can't get a company to even think about how

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to model data for consumption, why am I going to start pushing a concept that they're not ready for?

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As a consultant, I'm not even going to talk about our company,

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but as a consultant, I personally have a challenge with that.

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With the push of new concepts to clients when they are not mature enough to

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get to that space, that's something that I holistically will not do.

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I want people to be successful in what they're trying to achieve.

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That's my goal. So yeah, I don't push it personally.

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I think data products, I know you don't like that term. I don't like many terms

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because people define things however they want to define them.

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So I always say, you have your definition, I have mine. Let's just make sure

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we all understand where we're coming from is kind of my goal in life.

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But data mesh, yeah, we have clients have talked about it.

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And most of the time, they're looking at it as a tooling question.

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What are the technical pieces I need to create a quote unquote data mesh? And I tell them,

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That's nice. That's not going to solve your problem. Well, yeah,

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I mean, I agree with the maturity aspect, but there are companies out there

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who are structured in this way.

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Extremely large e-commerce retailer here in the Boston area is structured that

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way, right? That's how their business is set up.

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So fine, you know what? That makes sense.

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Good for you. Good job. For everybody else, yikes.

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So my BeatBoot data product, in my opinion, a data product is something that

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you build in order to sell to the rest of your mesh.

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You don't know that anybody's going to use it. You just think that it is.

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But you have to like spend cycles building it in such a way that it feels like

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an actual product, which in my mind actually lowers the ROI that you might get

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out of that because your input costs are higher.

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Your resource costs are higher. The technology costs that you're using in order

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to make this thing look like a product are probably higher.

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And you could probably do something with lower input costs and get the same

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level of adoption across the board.

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That's why we're really focused on this idea of assets sets and tying everything back to ROI.

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Because then it's really about are you building the right things?

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Like previous consulting firm, we did a project for a large hospital network

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in general Massachusetts area.

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And we built them a dashboard. It was there was a lot of data that came into

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this and it was a pretty hefty project and program that we did.

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It was a bed management dashboard where they would be able to see sort of the

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layout of how many beds were available and so on and so forth,

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and some level of a sort of a light forecast against sort of where the beds

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would be, so that they could do their staffing in such a way that they saved

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something, they told us they saved something in the realm of like $70 million in staffing costs.

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And the fact that they like made an effort to track that number tells me that

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they actually understood the ROI that they were going for.

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Yeah, I think I look at it similar, but different than the approach you just

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laid out, right? I guess, James, like the example you gave, right? It was a.

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There was a specific ROI that we were targeting, a very pointed product that was being created.

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I look at something like that. I'm like, all right, that's the goal of the business.

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That's your business strategy, right?

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And ultimately, there's a product that's tied to that.

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But I look at all the data elements that are tied to that product.

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And I think to myself, this concept of, because this is what happens in organizations,

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right? They have this ROI item that they're trying to achieve.

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And they're like, all right, we need to get all this work done for this one

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use case. And then all the work that is done for that use case is only for that use case.

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Everything around it gets orphaned. That big data product probably includes

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20 others underneath it, pieces and components of that product that are very

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specific to domains that other people can re-leverage.

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So you need to distill down what you're actually trying to do into components

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so that people can re leverage that information.

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And then the ROI goes way up. And not only for that one project that you just

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finished on the bedside, but the reuse of those analytics and those different

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data products that made that larger one, right?

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That's the way I look at it. But the problem is people don't think that way. Right.

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Agreed. And this is why, this is why we're, again, we're focused on assets and

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why we're focused on ROI, because to your point, there's 20 different other

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things that are part of that, that are part that go into building this,

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this more broad, larger asset.

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This is, and my issue with data mesh becomes that you end up actually building

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in silos and then trying to share at the end. Yeah. Right.

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Versus like building in a collaborative fashion is is ultimately how you're

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going to drive the most efficiencies across the board.

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And so part of our goal as a data cloud architecture team is to help to drive

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those efficiencies and help reduce that and drive that collaboration by making

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it clear who owns what and what's out there.

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Anjali, from a data mesh perspective, have you been hearing anything around

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that? I hear a little bit of chatter, but I think it's similar.

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Sandy, to the approach that you laid out in terms of.

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Our clients aren't ready for

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kind of what it takes to be successful with data mesh type of approach.

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And one of the key foundations for being successful with the data mesh is having

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a highly governed approach to your data, setting up a standard to say,

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in order to be considered a data product in our mesh,

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you need to meet these handful of criteria and actually track against that.

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In the consulting space, I always remember governance being the third rail of a proposal.

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Don't put governance in there because nobody's going to buy the project or it's

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going to be the first thing that gets pulled out.

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MDM and governance were the two things that always got stripped out.

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It's just like, I just don't even touch it. Those who toil in the governance

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space on the consulting side, I feel the most sympathy for ever.

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It's not- You're talking directly to Anjali. I know. That's why I'm talking.

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I'm sending this at Anjali and telling her how much, how bad I feel for her

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in this particular scenario.

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Thank you for that. You know, a lot of our clients are actually struggling with,

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you know, with data challenges and have opened up their eyes to the need for

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finding accountability and focusing on the confidence and fidelity of their data.

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So I think that as we kind of look forward, maybe governance will no longer

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be that forgotten forgotten child and really be the golden child of the family.

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You know, back to the other topic that's been very popular around generative

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AI and everybody needing an AI strategy.

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So at our summit last year, Frank Slootman, our former CEO on stage said, you can't have.

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An AI strategy without a good data strategy. It doesn't surprise me,

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Anjali, that governance is less of a problem and people are looking at it more

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because they're being asked to build an AI strategy.

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And you can have the best LLMs and the best generative AI capabilities and the

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best chat bots in the world.

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But if your data quality isn't there, you're training models against trash.

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So you're going to get trash back.

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The good thing about Gen AI is that it's

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highlighted highlighted the fact that good quality sound

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data and mastering of metadata is

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critical to be able to do something like that right

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now everybody wants it because it looks so cool and i'd love

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to chat with my data someday hopefully we'll get there

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you can't get there if your data is out of quality you can't get there if your

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your context around your data is aloof right this is the conversation i had

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yesterday with the ceo everybody wants to everybody wants a chatbot And I refuse

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because they don't trust the data underneath it. And then they all go off and

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build in their own little world.

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And, and then there's four different definitions of ACV or whatever it is that

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they're looking at and revenue and or, you know, four definitions of churn and none of them are right.

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And then when you ask them, well, how do you define that? I would say maybe

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not none of them are right. Maybe all of them are right.

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It's just a matter of the intent behind it. What's my intent?

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What am I trying to drive?

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It's intent, it's context, and it's quality of the data asset itself that you're going after.

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Ultimately, none of this is going to work if companies don't bring together.

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Governance with the data teams and the AI teams and the digital teams into one

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package and figure out how do we collaborate to solve these problems.

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Because if everybody keeps working in isolation, A, they're only going to have

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part of the building block.

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No one's going to know how to use it. No one's going to trust it.

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And it's just going to be a mess again, except on modern platforms.

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Well, you keep saying modern platforms. And this is a conversation I've been having as well,

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where people in the sort of mid 2010s started to migrate off of their on-premise

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data centers into the cloud with the intent of, oh, we built all these silos on-prem.

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We're going to move into the cloud and and the silos are just going to magically disappear.

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And they picked up and moved their silos yeah and then they kept being siloed in the cloud so

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modern stack doesn't always apply if you're if you're not breaking down the

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silos and i think a good and you know back to the whole governance aspect like

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a good governance is a double-edged sword here right there is such a thing as

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too much governance right you put you make it too hard to participate everybody's

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going to go off and build in their own silos and shadow it runs runs rampant.

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My whole thing around this modern stack has to be in conjunction with a modern

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way of thinking, right? Modern ways of working too.

287
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Yes. A hundred percent. Right. And you can't. Yeah. I mean, you're talking about

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governance and I agree with you, right?

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I look at this and I know Anjali agrees with me as well is governance should

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be focused on outcomes and impact that you're trying to achieve.

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At the end of the day, it's an enablement factor, right? What am I trying to

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enable and building a process that supports that use case or enablement that

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you're trying to go forward with, whether it's mastering of data,

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you know, reviewing quality and building a process around that,

295
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or even security and masking policies, etc.

296
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But if you're creating processes that restrain the business from doing things,

297
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then you're doing governance for the sake of governance.

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And you're actually, you know, not doing it for the sake of the outcome.

299
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And I think people go one way or the other on that. And it's tough,

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because the first conversation people typically have is.

301
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Yeah, what's the organization I need to have for governance?

302
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It's just like, I don't want to answer that question. I actually don't care.

303
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The question I want to answer is what do you need to solve for and put the right

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process in place for that?

305
00:19:33,243 --> 00:19:37,683
Yeah, I mean, ultimately, governance needs to be an enabler for innovation.

306
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It cannot be the inhibitor to innovation.

307
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It has to be flexible enough to allow for innovation.

308
00:19:43,943 --> 00:19:47,183
Exactly. One question I had, you know, I've been reading a lot about some of

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the bold moves that Snowflake has made around MLOps, LLMs, etc.

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You guys have kind of taken a shift into the world of advanced analytics, data science, etc.

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So I'm curious, what's the thinking behind that?

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And how do you envision that shaping the future of data management?

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Because historically, right, and I love this idea, by the way,

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because I'm all about bringing these worlds together.

315
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As I just said, these teams have to collaborate, they have to be working on

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the same platform, not platform necessarily, but the same data sets,

317
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right, the same data products.

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So is it really just a shift in that direction of trying to bring these worlds together?

319
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So I think there's a couple of different answers. And I don't,

320
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I don't promise to speak for our product organization and for our leadership in that way.

321
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But yeah, you know, we've made a lot of strategic acquisitions in the last 18 to 24 months, right?

322
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Companies like Neva, which is where Sridhar, our new CEO came from, right?

323
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And And partnerships as well, where we're trying to, again, be a more robust

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platform and bring these capabilities.

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But at the end of the day, I have never worked for a company or even when I

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was on the partner side, worked with a company who listens to their customers

327
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as much as Snowflake does and makes decisions in that direction.

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Many people in the sales organization who have been here for a long time can

329
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point to specific features and then point to the specific customer who asked for it.

330
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And this goes back to things like Snowpipe. You know, data sharing came out

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of a very specific ask by one of our largest accounts.

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We want to be able to make this data available to our customers.

333
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This shift that you're seeing comes from a desire for our customers,

334
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for some of from some of our largest customers to consolidate from their tooling perspective, right?

335
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You know, you look at that slide that gets published every year of the tools

336
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of the modern data stack, and it just keeps getting bigger, right?

337
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It's just like every tool under

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the sun all of a sudden fits under this concept of modern data stack.

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And there was a scenario in sort of the early 2020s.

340
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So in the last kind of couple of years where everybody was buying all these

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like bespoke tools to do this job, and then it spend was running rampant,

342
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and then the pandemic hits, and then everybody's trying to like, save money.

343
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And they're like, wait a second, why do we own data IQ and data robot?

344
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What is happening here? This doesn't make any sense. So from a Snowflake perspective,

345
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right, for many of our largest customers who have consolidated and brought a

346
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lot of their data estate.

347
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To be stored and processed using Snowflake, they wanted to say,

348
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okay, wait, I want to be able to to code in Python, right?

349
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I want to be able to write data engineering pipelines in Python,

350
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here is Snowpark, right?

351
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So you have this ability now to code in the language that you feel more comfortable

352
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to build those declarative pipelines.

353
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But now my data science teams want to use Snowpark and, and we're not necessarily

354
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optimized, you know, our warehouses aren't necessarily optimized for those types of workloads.

355
00:22:39,312 --> 00:22:42,652
Okay, here comes Snowpark optimized warehouses. And then now we've We've been

356
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building applications. We love using Snowflake to power some of our analytical applications.

357
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We want to be putting more of our applications and consolidating this into Snowflake.

358
00:22:51,292 --> 00:22:52,492
Great. You guys build with containers.

359
00:22:52,912 --> 00:22:57,292
Awesome. Snowpark Container Services. It's very much a feedback loop with our customers.

360
00:22:57,632 --> 00:23:01,112
And yes, all of these things have the word snow in them. And we all have to be okay with that.

361
00:23:02,312 --> 00:23:06,832
They will always. And we're getting over it. Yeah. I think about 1999 moving

362
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forward in the on-prem world, let's say Oracle, right?

363
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The big challenge back then was that Oracle went off and bought all these companies

364
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and tried to integrate it.

365
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And as a consultant, I remember saying to them, yeah, they bought all this stuff,

366
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one tool, but you still have to self-integrate everything.

367
00:23:24,392 --> 00:23:27,452
It's painful, right? It's painful to integrate all these platforms.

368
00:23:27,832 --> 00:23:31,212
And what I find intriguing and interesting about where we are with cloud today

369
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is that everyone's pushing for at least the idea of these modular solutions

370
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where everyone has to have all these, the best. and I don't want vendor lock-in.

371
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I want to be able to swap things out.

372
00:23:42,712 --> 00:23:46,152
You're not going to do that. Like I look at it, I'm like, you're not going to do that.

373
00:23:47,012 --> 00:23:50,012
You're just not. Vendor lock-in is my least favorite term.

374
00:23:50,192 --> 00:23:54,572
Vendor lock-in is the funniest conversation I think I have because I'm just

375
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like, you're trying to say the problem we had in 2000 was vendor lock-in.

376
00:23:59,952 --> 00:24:00,892
That was not the problem.

377
00:24:00,972 --> 00:24:04,672
The problem was technology was not advanced enough to allow you to change.

378
00:24:04,912 --> 00:24:07,712
It's advanced enough now, regardless of what vendor you use,

379
00:24:07,752 --> 00:24:10,012
as long as it's cloud-based. It's actually pretty easy to move.

380
00:24:10,152 --> 00:24:14,472
I do love the idea of the consolidation that is starting to happen in the cloud

381
00:24:14,472 --> 00:24:17,852
where companies are getting smart about, all right, how do we do this?

382
00:24:17,932 --> 00:24:22,652
But they're trying to do it. Some do it well, smartly in terms of if I'm adding

383
00:24:22,652 --> 00:24:25,012
capabilities, it's built through our platform.

384
00:24:25,352 --> 00:24:29,252
Some people are acquiring tools and solutions and trying to integrate them in

385
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the cloud. And then it gets a little clunky and scary.

386
00:24:31,572 --> 00:24:35,812
We're now in a place where things are starting to get consolidated because of what you said.

387
00:24:35,992 --> 00:24:40,132
Look at the math. There's a billion little little dots on icons of companies

388
00:24:40,132 --> 00:24:44,652
on this giant capability map, and people are overwhelmed and confused.

389
00:24:44,912 --> 00:24:49,032
And I think Snowflake is and will always be a single SKU.

390
00:24:49,152 --> 00:24:52,952
When we make acquisitions, and when we build out net new features and capabilities.

391
00:24:53,432 --> 00:24:57,492
It's not something that you then have to turn around and pay for another license for.

392
00:24:57,592 --> 00:25:01,292
You buy your bucket of credits, and then you use them as you see fit.

393
00:25:01,412 --> 00:25:05,252
So back to your question about these decisions around LLMs and MLOps and and

394
00:25:05,252 --> 00:25:06,132
all that kind of stuff, right?

395
00:25:06,372 --> 00:25:10,072
It's it's for us, it's all about how are we driving that

396
00:25:10,511 --> 00:25:14,731
customer satisfaction in that NPS score, right? Like, we at Snowflake do not

397
00:25:14,731 --> 00:25:17,331
have a customer success org that does not exist at Snowflake.

398
00:25:17,471 --> 00:25:22,731
I didn't even, you know, I've worked with you guys since you worked with us years ago.

399
00:25:23,071 --> 00:25:27,971
And I didn't realize that this entire time. So that's yeah, yeah.

400
00:25:28,031 --> 00:25:32,851
And there's customer success office, like the idea of a customer success team

401
00:25:32,851 --> 00:25:34,911
not being there is support.

402
00:25:35,011 --> 00:25:38,411
Yes, success. No. And that's, that's an interesting differentiator,

403
00:25:38,471 --> 00:25:39,611
actually. so I appreciate that.

404
00:25:39,691 --> 00:25:44,171
I do want to pivot and give you the opportunity to continue the conversation

405
00:25:44,171 --> 00:25:46,511
we had over text regarding Gen AI.

406
00:25:46,811 --> 00:25:51,211
We have 15 minutes and I think it's important for us to cover this ground.

407
00:25:51,391 --> 00:25:55,591
When we were going back and forth, we were talking about the change in work, right?

408
00:25:55,671 --> 00:26:01,211
How is Gen AI actually going to impact work and maybe not just work for the

409
00:26:01,211 --> 00:26:03,831
general masses, but work for data teams?

410
00:26:04,031 --> 00:26:07,171
Yeah, so I want to go there with you. I'm curious of your opinion.

411
00:26:07,171 --> 00:26:12,691
I'm reading back through our texts to make sure I'm tracking to what I say.

412
00:26:13,051 --> 00:26:17,171
I think you've made a comment because people think that they're going to lose their jobs.

413
00:26:17,411 --> 00:26:22,311
Yes. But this is also what people who had tied their whole life to Oracle or

414
00:26:22,311 --> 00:26:27,911
Teradata, the DBAs of the world, felt when people started moving to the cloud.

415
00:26:28,251 --> 00:26:31,811
They pushed back really hard because they felt threatened. It is okay.

416
00:26:31,931 --> 00:26:33,491
It is okay to feel threatened.

417
00:26:33,651 --> 00:26:36,771
It is okay to feel like your job might be at risk. it's

418
00:26:36,771 --> 00:26:39,691
not okay to stop innovation because you don't want

419
00:26:39,691 --> 00:26:42,391
to change yourself right i think the the best advice i could

420
00:26:42,391 --> 00:26:45,231
give to to anybody in this space at this point is to be

421
00:26:45,231 --> 00:26:48,591
flexible in my own career right i started as a visualization guy

422
00:26:48,591 --> 00:26:51,491
i was a front-end bi analyst kind of person

423
00:26:51,491 --> 00:26:56,811
right and as the cloud became more prevalent the the bi end of the spectrum

424
00:26:56,811 --> 00:27:02,811
became less relevant and for me it it became important to diversify my skillset

425
00:27:02,811 --> 00:27:10,031
and be able to get an understanding of how ultimately do these BI products get fed with data?

426
00:27:10,367 --> 00:27:15,167
So I would say to anybody who is feeling threatened by generative AI that,

427
00:27:15,227 --> 00:27:17,987
but somebody's got to build the model. Somebody's got to tune the model.

428
00:27:18,207 --> 00:27:22,487
Somebody has to deploy the model. Somebody has to own the application that then

429
00:27:22,487 --> 00:27:24,427
the users interact with.

430
00:27:24,547 --> 00:27:27,227
If you feel like your job is going to be eliminated because you're not building

431
00:27:27,227 --> 00:27:33,227
ETL pipelines and you're not building dashboards anymore, then go take a course on LLMs.

432
00:27:33,287 --> 00:27:37,147
Go take a course on GPUs. Like, I understand that these are extremely technical

433
00:27:37,147 --> 00:27:40,667
constructs. And so it takes a lot to learn about them.

434
00:27:40,747 --> 00:27:45,827
If your other option is to just be a curmudgeon and not let innovation happen,

435
00:27:46,007 --> 00:27:50,847
because you're worried about your job, that's not ideal either.

436
00:27:50,947 --> 00:27:54,367
This these things will happen whether you get in the way or not.

437
00:27:54,367 --> 00:27:56,567
I'm going to print t-shirts curmudgeon.

438
00:27:57,007 --> 00:27:59,827
I totally agree with you.

439
00:27:59,907 --> 00:28:07,067
And I kind of mentioned this right during the first episode when I was a front

440
00:28:07,067 --> 00:28:11,547
end developer, I built BI solutions from scratch.

441
00:28:11,547 --> 00:28:14,587
And the pain and agony of

442
00:28:14,587 --> 00:28:17,827
seeing a what you see is what you get drag and

443
00:28:17,827 --> 00:28:21,887
drop lizzy wig bi platform in 2003

444
00:28:21,887 --> 00:28:28,067
i recalls when i first saw it from oracle yeah i almost had a heart attack because

445
00:28:28,067 --> 00:28:35,367
obi it was pre-obi it's kind of like it was it was the precursor to obi and

446
00:28:35,367 --> 00:28:39,087
i i still remember that i almost had a heart attack i didn't let anybody know

447
00:28:39,087 --> 00:28:41,687
that i was scared out of my mind and i I pivoted,

448
00:28:41,687 --> 00:28:47,487
I think the thing here is for people to realize that somebody has to design these solutions,

449
00:28:48,240 --> 00:28:51,340
you have to understand how these things are going to work. And you have to understand

450
00:28:51,340 --> 00:28:53,040
data quality is part of the problem.

451
00:28:53,360 --> 00:28:56,900
All the issues surrounding the work we do are still going to exist.

452
00:28:57,260 --> 00:29:00,480
It's just how we solve those issues are going to change. And you have to get

453
00:29:00,480 --> 00:29:03,900
uncomfortable more often now than we did in the past.

454
00:29:04,140 --> 00:29:10,740
I had a customer, he basically was like, so can I just train a model and get rid of ETL entirely?

455
00:29:11,280 --> 00:29:14,500
And I was like, no. I was like, Like in theory, sure.

456
00:29:14,700 --> 00:29:18,020
But is the cost of training that model going to be so great?

457
00:29:18,120 --> 00:29:21,420
And oh, by the way, is it going to perform like crap? Yes.

458
00:29:21,600 --> 00:29:24,720
So you're not going to get rid of ETL. And in the same way that,

459
00:29:24,720 --> 00:29:27,900
you know, Sandy, you and I had this debate over text about dashboards, right?

460
00:29:28,060 --> 00:29:33,720
I understand that the chat interface will make it easier for users to interact

461
00:29:33,720 --> 00:29:36,380
with the data in a way that is more exploratory.

462
00:29:36,600 --> 00:29:40,780
But what that's really going to get rid of is more of the Excel type of model

463
00:29:40,780 --> 00:29:44,480
of exploring and slicing and dicing data in certain ways.

464
00:29:44,600 --> 00:29:47,960
I pray to God that that happens. Your lips to God's ears, honestly.

465
00:29:48,040 --> 00:29:52,020
But there is still going to be a space for dashboard.

466
00:29:52,200 --> 00:29:54,660
It's not going to eliminate other ways of interacting with data,

467
00:29:54,760 --> 00:29:58,700
except for obviously in the more of the exploratory style, right?

468
00:29:58,780 --> 00:30:01,460
You're going to get a lot more people who are going to ask questions of data

469
00:30:01,460 --> 00:30:06,300
themselves and not go ask a BI analyst to go pull a report for them to answer

470
00:30:06,300 --> 00:30:08,720
one specific question. I think those days are gone.

471
00:30:08,940 --> 00:30:11,480
I think those days are, they need to be gone.

472
00:30:11,960 --> 00:30:17,240
I want everybody to move towards decision intelligence, where you're actually

473
00:30:17,240 --> 00:30:21,820
supporting decisions people have to make in the workflow in which they're making them.

474
00:30:22,300 --> 00:30:27,780
That's where I think this all needs to go towards. And the days of I go to a

475
00:30:27,780 --> 00:30:30,220
dashboard need to go away. way.

476
00:30:30,280 --> 00:30:34,480
I spent probably the first 10 years of my career doing nothing but front-end

477
00:30:34,480 --> 00:30:37,780
solutioning. And all I could think about is getting rid of it now.

478
00:30:37,940 --> 00:30:41,340
I'm just like, I'm so sick of it. I just want to help people make the right

479
00:30:41,340 --> 00:30:44,600
decision at the right time, in the right workflow, et cetera.

480
00:30:44,800 --> 00:30:47,240
I think that needs to start happening sooner rather than later,

481
00:30:47,300 --> 00:30:52,340
but there's so many barriers to that because BI teams are still working in isolation

482
00:30:52,340 --> 00:30:55,100
from the AI team, from the digital team.

483
00:30:55,360 --> 00:31:02,140
I think I understand where you're coming from, But I think that the dashboard

484
00:31:02,140 --> 00:31:04,500
paradigm is never going to go away, right?

485
00:31:04,620 --> 00:31:09,100
And even if the dashboard paradigm shifts to being basically,

486
00:31:09,160 --> 00:31:11,520
how do I embed KPIs into your workflow?

487
00:31:11,740 --> 00:31:15,740
That still has a dashboard-like feel, right?

488
00:31:15,900 --> 00:31:18,880
And part of our debate over text was around operational, right?

489
00:31:18,940 --> 00:31:21,420
The operational dashboards will continue to exist, period.

490
00:31:21,740 --> 00:31:26,820
That's just how it's going to work, right? So I get decision intelligence and

491
00:31:26,820 --> 00:31:32,000
I get making that point. But like, when it comes to a user interacting with

492
00:31:32,000 --> 00:31:36,180
data, you have to make sure that it fits in terms of their own maturity.

493
00:31:37,429 --> 00:31:43,089
Putting it all in chatbots is good, except for the users who don't know how

494
00:31:43,089 --> 00:31:47,009
to ask questions, who just want the specific answer to the specific question

495
00:31:47,009 --> 00:31:50,289
that they ask themselves every day to do their job. It's that bad.

496
00:31:51,129 --> 00:31:56,269
You don't know how to ask questions? Wow. I mean, come on.

497
00:31:56,309 --> 00:32:01,229
You and I have spent enough time with enough clients in this world who ask the worst questions.

498
00:32:02,249 --> 00:32:07,129
Let's be clear. here. I plead the fifth on that one. I am not subscribing to that, James.

499
00:32:07,489 --> 00:32:10,329
Well, you still have clients to deal with. I get to be off the center side.

500
00:32:10,449 --> 00:32:12,969
I don't have to do that. There are no dumb questions.

501
00:32:13,689 --> 00:32:15,809
Well, I do tell people there's no such thing as a dumb question,

502
00:32:15,869 --> 00:32:19,069
only a dumb answer. But it doesn't mean you don't. It doesn't mean you know

503
00:32:19,069 --> 00:32:20,049
how to ask the question, right?

504
00:32:20,249 --> 00:32:24,309
I mean, that applies to to generative AI as well, right?

505
00:32:24,409 --> 00:32:29,029
I mean, there are dumb answers that come back from from these models as well.

506
00:32:29,089 --> 00:32:31,849
We have to be able to one, except you're going

507
00:32:31,849 --> 00:32:34,749
to get a dumb answer but to put guardrails in

508
00:32:34,749 --> 00:32:37,569
place the human in the loop to to make

509
00:32:37,569 --> 00:32:40,729
the decision around whether or not that's the answer we can actually use

510
00:32:40,729 --> 00:32:46,009
and trust yeah i could not agree with that statement more like i as much as

511
00:32:46,009 --> 00:32:49,769
as much as ml and this idea of prescriptive analytics in terms of like actually

512
00:32:49,769 --> 00:32:53,729
letting the computer make decisions i like there's a world where i understand

513
00:32:53,729 --> 00:33:00,269
why that that that happens so like google maps right nobody's in nobody is sitting at the terminal.

514
00:33:00,989 --> 00:33:03,949
Seeing all the google maps requests in and then telling them

515
00:33:03,949 --> 00:33:06,709
which directions to go right a

516
00:33:06,709 --> 00:33:09,849
computer is making that decision i i get that and guess

517
00:33:09,849 --> 00:33:12,749
what google maps does it wrong a lot and so

518
00:33:12,749 --> 00:33:16,129
you have to you as the user has to decide i trust

519
00:33:16,129 --> 00:33:18,949
this or no i really don't so we were we

520
00:33:18,949 --> 00:33:21,749
went up to north my my family and i went up to northern maine for

521
00:33:21,749 --> 00:33:24,789
the eclipse last week it was beautiful it was it

522
00:33:24,789 --> 00:33:27,469
was a beautiful day it was the coolest experience i've ever had but

523
00:33:27,469 --> 00:33:30,929
it took us what should have been a at most five

524
00:33:30,929 --> 00:33:33,589
hour drive took eight hours to get home google maps just like

525
00:33:33,589 --> 00:33:36,569
didn't work and everybody ended up on the the area at

526
00:33:36,569 --> 00:33:39,489
franconia where 93 and three come together and they went

527
00:33:39,489 --> 00:33:42,429
five miles in five hours i i i

528
00:33:42,429 --> 00:33:45,629
agree i think it was we can find instances where

529
00:33:45,629 --> 00:33:48,789
it's not going to work when there's an event that is so mass

530
00:33:48,789 --> 00:33:51,469
in terms of the number of people jumping on the highway and the

531
00:33:51,469 --> 00:33:54,509
information doesn't get back fast enough for it to update its

532
00:33:54,509 --> 00:33:57,129
algorithm yeah of course but i

533
00:33:57,129 --> 00:33:59,969
but i i looked at the map and i said i don't

534
00:33:59,969 --> 00:34:04,789
like what this route is and i had to go manually put in a point on the map to

535
00:34:04,789 --> 00:34:10,609
force google maps to take me in that direction right so like that's that's sort

536
00:34:10,609 --> 00:34:14,369
of the manual intervention that you're talking about anjali of like i as a user

537
00:34:14,369 --> 00:34:18,869
have to be smart enough and say this doesn't make any sense Generally,

538
00:34:18,889 --> 00:34:22,869
I might get rid of some level of IT jobs in theory.

539
00:34:22,989 --> 00:34:25,569
Yeah, I don't think the jobs are going to go away. I think they're just not

540
00:34:25,569 --> 00:34:28,709
going to scale the way they've been scaling. Sure.

541
00:34:29,029 --> 00:34:31,669
That's kind of where I'm at. The scalability is going down a bit.

542
00:34:31,669 --> 00:34:35,909
Like you don't have to have thousands of data engineers if you're a data heavy company.

543
00:34:36,321 --> 00:34:39,481
That's going to probably simmer down a bit. You'll be able to do a lot more

544
00:34:39,481 --> 00:34:42,961
with less. So I don't see anything completely disappearing.

545
00:34:43,221 --> 00:34:49,921
However, I will say the logo for their podcast, a logo for a friend's Facebook group.

546
00:34:50,001 --> 00:34:53,221
She was pinging me yesterday, just two days ago, asking for a logo.

547
00:34:53,441 --> 00:34:57,021
Hey, you did this AI thing for your podcast logo. Can you go do one for me?

548
00:34:57,201 --> 00:35:00,041
And it was like around the barrel, I think it's called. She's like,

549
00:35:00,061 --> 00:35:01,901
it's a new Facebook, but I'm trying to do it. I was like, fine.

550
00:35:01,961 --> 00:35:03,901
So I literally opened up my phone.

551
00:35:04,001 --> 00:35:06,441
I didn't even use my computer. I had chat GPT on my phone.

552
00:35:06,581 --> 00:35:11,061
I asked it and within the third hit, it had it. Five minutes later, she's got a logo.

553
00:35:11,301 --> 00:35:16,221
And so I feel bad for all the artists out there on Fiverr that was creating

554
00:35:16,221 --> 00:35:19,901
logos for five bucks or a hundred bucks a pop. That industry is gone.

555
00:35:20,081 --> 00:35:26,601
So apologies to them. But I think that's going to be the tough one for us.

556
00:35:26,781 --> 00:35:31,081
I do have to wrap this up. I have a client call that I had scheduled after this,

557
00:35:31,101 --> 00:35:32,521
unfortunately, so I got to run over there.

558
00:35:32,601 --> 00:35:36,721
But James, James, what is your last piece of advice to anybody who's scared

559
00:35:36,721 --> 00:35:40,181
to death in terms of, I'm a data engineer, I'm just starting my career.

560
00:35:40,381 --> 00:35:42,161
What should I focus on? Learn Python.

561
00:35:42,461 --> 00:35:45,441
There's a lot of people who will tell you, oh no, SQL will never go away.

562
00:35:45,581 --> 00:35:46,861
Yeah. They're not correct.

563
00:35:47,141 --> 00:35:51,961
Python is, from a declarative engineering perspective, Python is where people are moving.

564
00:35:52,361 --> 00:35:56,441
And Python is super flexible. I said earlier, being flexible is the most important

565
00:35:56,441 --> 00:35:57,421
thing that you can do for yourself.

566
00:35:57,661 --> 00:36:00,741
And I think, at least in terms of what's happening in the next five years,

567
00:36:00,981 --> 00:36:05,641
having an understanding of Python is is going to allow you to do a lot of things

568
00:36:05,641 --> 00:36:07,381
and be and help be successful.

569
00:36:07,621 --> 00:36:12,141
So if I had any advice for a budding data engineer, the first thing I would

570
00:36:12,141 --> 00:36:14,181
tell you is to go learn Python, learn Python.

571
00:36:14,421 --> 00:36:16,881
I love it. Well, thank you, James, for being on.

572
00:36:17,181 --> 00:36:20,861
Of course. Thank you for having me. I mean, it was exactly what I expected.

573
00:36:20,981 --> 00:36:23,221
I expected this from you. So I appreciate that.

574
00:36:23,361 --> 00:36:27,821
And we probably need to have a round two in 10 years to figure out was I right

575
00:36:27,821 --> 00:36:29,481
about operational dashboards?

576
00:36:29,561 --> 00:36:32,021
Or was James's right about operational dashboards?

577
00:36:32,161 --> 00:36:35,981
I'm putting the that down now. I'll Venmo the money to you and let's do it.

578
00:36:36,041 --> 00:36:40,141
Done. Thank you. Well, have a great weekend. Bye, Anjali. I'm off.

579
00:36:40,400 --> 00:37:08,668
Music.