This week on The Data Stack Show, Eric and Kostas chat with Zack Hendlin, CEO & Co-Founder of Zing Data.
During the episode, Zack discusses his time building newsfeeds and curation at LinkedIn and Facebook, how Zing is using BI and analytics to give users the ability to query on mobile devices, and more.
Highlights from this week’s conversation include:
Eric Dodds 00:05
Welcome to The Data Stack Show. Each week we explore the world of data by talking to the people shaping its future. You’ll learn about new data technology and trends and how data teams and processes are run at top companies. The Data Stack Show is brought to you by RudderStack, the CDP for developers. You can learn more at RudderStack.com.
Welcome to The Data Stack Show today Kostas with this is going to be a really interesting one. We’re going to talk with Zack Hanlon from zoom, and zoom brings BI and analytics to mobile devices, which is a really interesting concept. It sounds really simple, but anyone familiar with a world of BI, especially traditional BI, you know, has access to probably exclusively to a desktop environment, and zip is trying to bring that to mobile devices, which is fascinating. I have so many questions. I think my main one is really simply why mobile? You know, do you really need to access these numbers on the go? What are the big trends that are driving the need for this? Right? I mean, I understand, you know, getting notifications or looking at, you know, Salesforce, you know, pipeline generated for a b2b company and cetera, you know, or sales for an E commerce company, that actual BI with the ability to do some query on the mobile devices is a pretty provocative concept, at least as far as my experience with BI. So that’s what I’m going to ask him.
Kostas Pardalis 01:42
How about you? Yeah, it’s, it’s very interesting to hear, you know, 2022, about the need of BI to the mobile experience, we would expect that this is like a solved problem already. Right? BI has been around forever, like mobile devices have been around for a long time. But it seems like that’s not the case. And we have someone here who’s really passionate about doing these rights. I’m pretty sure that there are many difficulties in delivering, like the right kind of experience on a mobile device for something that it’s so complex in terms of interacting with the visual side of data. So I will, first of all, like I really want to talk with humans, who are the difficulties of doing that, while it means to take dashboards and interactive dashboards and strip them down, like to the sides over mobile phone, right? And do that like effectively? Let’s see what he has to say. I think that’s going to be like, super, super interesting.
Eric Dodds 02:54
I can’t wait. Let’s dig in. Zach, welcome to The Data Stack Show.
Zack Hendlin 03:00
Thanks for having me. Great to be here.
Eric Dodds 03:02
All right, give us your background,
Zack Hendlin 03:04
I started a company called Zing Data, where I’m co-founder and CEO, and we make it so you can do BI on your phone. So easily query data, visualize it, set up alerts, save questions and collaborate with colleagues. And before that, I was VP of product and built out the data infrastructure at a company called One Signal that pars about 10 billion notifications a day, if you get news or sports scores, it’s probably coming through them. And then before that, I shipped books first work in speech recognition, actually, their first mobile ads format, which was a big motivation for trying to make experiences with data better on mobile, and also worked on translation and newsfeed ranking and relevance at LinkedIn.
Eric Dodds 03:48
Very cool, man, what, uh, you’ve done a huge amount, will give us the quick background on. So you’ve obviously had a ton of product experience. And in doing that, I’m sure you’ve been exposed to all the different kinds of needs for data doing your job. Is that where the need for zing came from? What’s the Genesis story? Or maybe tell us about when you sort of had the first idea that led you eventually to saying,
Kostas Pardalis 04:17
Yes, so the Genesis was a was working at this
Zack Hendlin 04:22
kind of series B company at the time. And we had sales folks out in the field. And the cool thing was, we had gone from literally one production database, where if you wanted to run a BI or analytics query, you were logging into the server, running it on the production database, and, you know, production traffic rates slid out here. And I was like, hey, we want to know how many users are taking which actions who we want to upsell to just like, all this stuff, like a company. Once you have users, and some of them are willing to pay you. You want to figure out how to optimize that. And so built out through the basic infrastructure with one of the engineers on the team, and got super basic BI in place. And then the great thing was like salespeople were like, Oh, great, this tells me who I should be targeting who signed up for free that we could be upselling marketing was like, This is great. This tells us which features people are using and what resonates with them, product love because they could see, oh, here’s the feature I built, but people aren’t using the sub part of it. And so it was great. But then we realized once we like, open this box of insight, that it was actually really constrained as to how people could use it, and who could use it. And it’s something that no SQL, if someone was on the go, all those questions ended up basically getting funneled to one or two people who could go write some SQL for them. And you bump up against the limits of like, simple counts and sounds really quickly with the drag and drop WYSIWYG tech tools. Yep. And so the Genesis was, we now had all these people who wanted to use data, they saw how useful it was. And the capacity constraint was still like the data team having one person who was available and had a queue of a ton of stuff. And he said, What is it that makes it so these people can’t do it on their own?” And it didn’t work where they needed it to, the tools weren’t flexible enough. And it was pretty much single player. So it was really hard to start from somewhere. Someone else had done most of what you wanted, and just tweaked it. And so that was kind of the genesis plus, the other sort of inspiration was a really bad meeting, where I was sitting with other folks. And we were trying to decide where we would like hire salespeople where we make certain investments. And during this meeting for like an hour, and we were cutting Gesine at where are we growing free users fastest? Where are the biggest new sources of revenue? Where’s the biggest turnoff? Just like all these kinds of questions. And everybody here, they had their phones, nobody had their laptops. And so we ended up having this whole hour-long meeting where we’re making important decisions. And I knew all the data existed because I built this data infrastructure. And we were just, well, three months ago, I heard that it was the UK or whatever it was. And it was just like there has to be it was that like, born out of that frustration? Was this supposed to be a 10 Second question on your phone? Yeah, I was the one that would have made the meeting 10 minutes instead of an hour that wanted us to have a faster, better decision. And oh, by the way, if we did that, and that was available to everybody. sales folks could know it for the customers that they were going to meet. Zack could know it for the partners, they were gonna go meet. Even engineers who were on call could when they get paged, instead of having to run on to their laptop, they could dig into, hey, is this peak of the spike and bug reports due to not being able to connect to a database or having something else fail or have been building fail, and they could triage and do all that without having to be too stressed or run back to the computer. And so that was the genesis really realizing that out of all the tools out there, and we evaluated a ton of from free open source stuff all the way to a bunch of enterprise he type offerings, then none of them had really nailed, easy to use mobile experiences, not just waiting, but asking from scratch. And non limiting really nailed collaboration. And a lot of that was because they were building for a small number of data people. So collaboration didn’t matter as much.
Zack Hendlin 08:41
You didn’t need to set up a real-time alert, because you weren’t on your phone. And only a small number of people had access to Tableau or whatever, because it was expensive. And so the whole model of the products that were built, were focused on selling a ton of features to people whose primary job was doing stuff with data, business analysts, data scientists, and if you really wanted to make it open, and really make it easy to use, you had to kind of go back and say, Well, what is a less feature rich, but radically easier experience? It can work anywhere.
Eric Dodds 09:14
Okay, I have so many questions about that. But I want to look back into your history a little bit. And ask a question about your work on relevance and feeds, right, or even like ads for Facebook. So that’s fascinating to me, because there seems to be on the surface, an interesting corollary between, you know, figuring out what’s important to people and trying to surface that and when you think about the world of BI being compressed, you know, the world of BI as we traditionally know it, which is, you know, a big monitor, a sequel editor, you know, sort of unlimited real estate, like when you think about condensing that down out into a mobile experience, you have to answer this question of what do you actually expose to the end user? Right? And so maybe, you know, tell me if this is not true, but it seems I’d be interested to know, did you draw on any learnings from the LinkedIn and Facebook experience in terms of determining I mean, those are obviously consumer focused businesses. But really with data, you’re talking about consumers, that’s who you seem to be serving. So any learnings that influenced the way that you thought about bringing curating BI in a way into a mobile experience, which is a pretty difficult challenge? Yeah. So
Zack Hendlin 10:41
the way the commonality, we wanted to build BI that you didn’t need to read a manual for or undergo a long training. And it’s kind of the same way, if you open up the Facebook app, or the LinkedIn app, at least in an ideal state, where maybe they say, hey, here are people that went to your school, here are things that are probably relevant for you. If there’s an article, they’re not showing you the whole article, they’re showing you a preview of that link. Yeah, in fact, one of the things I worked on, when I was at Facebook, which I think is still there, is if you go through your newsfeed, and someone has posted, like 50 pictures, I was looking at the data when it was the pm on a news feed there. And I realized it’s something like 12345 pictures, it had a really high click through rate, people were likely to click into it or like it or comment at a really high rate. But if I had six or more pictures, the likes and comments shared on that post were way, way lower. didn’t make much sense to me initially, so dug into it. And it turns out, even though there was text that said, Hey, John had shared 45 photos, the link, the preview of those photos was just like the first five photos. And so if it was a wedding, it’s five photos of shoes or flowers, it’s actually not the photo with the bride and groom, right. And so what we did, and we did all these fancy experiments, and we could automatically re rank the photos and clever ways based on comment count on an individual photo level. But basically, what we ended up realizing was people just weren’t aware that there was more stuff they weren’t reading, they were just looking at graphics. And so in the lower right corner, we put a light gray, Washington said plus 10, plus 45, or whatever. And that dramatically increased the likelihood of someone to go and actually see all those photos. And then they were much more likely to, you know, take actions on. And so that was kind of like a lesson in how do you just make a thing make sense, visually, without necessarily requiring someone to understand when we tried this thing, where we automatically rewrite photos based on like, in common, it was just super confusing to people, they had no idea where it’s just got out of order. And it was like, that was a really complex system that we tested. And an easy thing that just did, the stuff that makes sense by default, was the right answer. And other examples, like translation of posts, if we know from your LinkedIn newsfeed, that you don’t have this language on your profile. And you’ve never posted content in that language, but maybe one of your contacts has, we’d automatically translate it. And then if you want, you could change your translation sentence. But by default, we had a sense of what languages you spoke, and you know what language the content was in and would automatically translate. And so all these things were like big engagement wins. And they’re great metrics. That was Dad’s sake, the improvements that they had. But the underlying idea was, how do you build a product that someone doesn’t really need to read a manual to use, and it does a lot of the hard or more complex stuff, kind of automatically, and if they want to change it, they can. So when I was translating that over to building Zayn, an example of a thing that is a big pain with SQL is dates. I could probably ask, you know, even the most seasoned data folks, you know, maybe know all those like dates and truncation functions and interval functions to say, hey, I want the last seven days of data. And I want that by minute or by hour. And we said, well, like you shouldn’t need to know that. If you have a timestamp or a date field will automatically cast it to a date. And we’ll show you we did that. So if you want to top you know, down arrow and configure it, you can, but instead of it, like failing by default, or showing you millisecond timestamps by default with, like, different time zones. Let’s just do what you were I would do if we were, you know, smart data scientists trying to create a useful graph or another example of a thing we did is maybe you end up quitting the really big data’s And in the background, you know, that might be a billion rows that are returned when you don’t want to return a billion rows to a phone. So by default, we say, Hey, we’ve limited it. And we don’t just limit, we don’t just add like a limit 100, or limit 1000, or whatever, we actually do a windowing and ranking function in the background, to show the biggest contributors based on the group bys you specified and the metric that you’re looking for. And that’s basically what a data scientist would do. If you said, Hey, show me the biggest revenue products by region, right? They would say, Okay, I’m showing you the first 10 contributors based on the metrics you care about. And then if you want, you can get everything else from there. And so it took a lot of thought around what the right experience is. And I think this is something that like, you know, Airbnb does, well, a figma does well, it makes the simple stuff simple. And then, you know, we actually have a full custom SQL Editor if you want, that you can do from your phone and say, and we have type ads and all that. That’s not what you see, when you go to query something, if you want that’s very been in three dots, and all that power is there. But by default, we want to make the simple stuff, hey, how many of my users did this over the last week, split by day, and type? Right? Is that a 10 second query where you literally just tap and drag for fun? Yeah, you don’t need to know. Time operators, you don’t need to know relative dates, you don’t need to know exactly what the syntax needs to be. We just handle that. And then if you want, you can change it.
Eric Dodds 16:39
Yep. Okay, so I want to dig into the mobile focus a little bit. So, you know, BI, on mobile is a fascinating concept, right? And so, you sort of have, I would say, generally, you think about this, and you think about, you know, BI, sort of on the spectrum of the person who’s building the BI, they are in data tooling on a desktop, hammering on sequel, you know, in the, like, the guts of the BI tool, sort of like building BI, right, building intelligence, etc. And, you know, when I think about consuming that on mobile, the ways that you generally think about that are sort of an executive who can, like, look at Google, like a basic, like basic Google Analytics stats, or, you know, use tableaus mobile phone to look at their KPIs in a mobile app to look at other KPI dashboard, or use the Salesforce app, you know, and they favorite sales pipeline report, you know, refresh it when they’re bored in the meeting, or whatever. And so it’s highly consumptive. And so it seems like there’s this, at least practically, in my experience, which is limited, but this huge gap between, I’m producing BI in a desktop environment with pretty heavy duty tooling, or I’m consuming and generally what’s a pretty bad, like mobile experience, if you think about, you know, I mean, Tableau on the phone just isn’t great, right?
Zack Hendlin 18:10
I mean, Jay, you don’t want to worry too specifically about competitors, I think they each have values that they are. Tableau is super flexible and stuff like that. But Tableau, Power BI, even thoughtspot, which is one of the somewhat newer companies. They are viewed only on your phone. Some of them have very limited NLP, but you need to pre specify all these aliases, and they don’t work very well. Right? But for Power BI and Tableau, like if you click on a number, it just makes the number bigger. It doesn’t actually, yeah, there’s no interactive query in there. Aside from this, like, very limited, what I’ve heard, they’re probably going to deprecate someone who works at Tableau, like NLP type thing. That doesn’t work. And thoughtspot, you can’t ask a question from scratch on your phone. Same with sort of creating dashboards in Power BI, or sigma is built to, in fact, for Tableau, their web version is much more limited than their windows and mac version. So if you actually want to use the full power of Tableau, you need to run it as a desktop app. Yeah. on the local machine. Sure. Yeah. And on Power BI, it’s even worse, you have to run on a Windows machine. You actually there’s not a Power BI, Mac. And so I think that’s what was built roughly 15 years ago. Yeah, because that was the world. And now, the world has shifted. So let’s look at Slack, which is workplace collaboration. More than 75% of slacks weekly active users use Slack on their phone in a given week. Or let’s take something that’s not even a great experience on your phone, Google Sheets or Google, Google Docs, it’s okay. Those have more than a billion downloads on Android. Each of those are more than a billion downloads on Android. And if you look at it, you know how people send emails, right? It used to be ETL Sent from my iPhone, and that was like a novel thing. Now, more than 50% of emails are read on your phone for the first time. And people actually send a lot of emails from mobile phones. And you know, if you think about that, initially, like shot on my iPhone is video or sent from my iPhone, for email, even video editing, like TikTok, you literally can edit a video with iMovie or TikTok on your phone and do something like reasonably good reasonably quickly editing video editing photos with Instagram, some of Adobe’s tools there Canva for design, right, like, all these other parts of getting work done, have built good mobile experience, you could read stocks on your phone, you can buy a car on your phone, and data because it was built largely by legacy companies and sold to data teams where your investments your time, that’s basically where the feature set has remained even of the newer entrant. And so our view was, you have to start from a place that is different, which is, you know, if you think about it this way, if you’re TikTok, and you’re trying to take every feature that’s in Premiere Pro, or Final Cut Pro, and make that work on a phone, that’s not going to work well. So you’re like, Oh, you just view stuff. That’s the easy way. But if you say we’re actually gonna make this a lot simpler, so way more people can do it. And we’re going to use these interactions that make sense. Like, being able to send you a push notification when your data hits a certain value or changes by a certain amount, making charts interactive. All these things that natively make sense when you tap a screen. But it doesn’t make sense when you’re kind of at a big, big computer. Those are the things we started from. So we actually built mobile before we ever built web i because it’s way harder. For instance, if you have a huge results set, we don’t show you a billion rows, we do windowing and ranking functions in the background to limit and show the biggest contributors based on the metrics you specified. Or if you have an iffy internet connection, we will run the query, when it’s ready, send down, push notification to background load that query result, when you have an internet connection. Everything else will just timeout. And so what that means is the experiences that everybody else built on mobile didn’t work well. So people didn’t use them. And so the perception was, Oh, if I want to do something with that, I can’t do it on my phone, because it just didn’t work well there. And that’s because you were trying to take Final Cut Pro and jam it onto a phone, instead of saying, Let me build the right 10 Things you want to do on your phone. We talked about like BI for 80 people, that company or 90 people for the rest of the company, right? The folks whose primary job is not creating dashboards, but who still want to use data to get decisions made. And so that was really the starting point.
Eric Dodds 23:12
I love it. Okay, I have one more question. But I’ve been, I could go on for hours. That’s it for every episode. But I know Costas has a ton of questions. One thing I’m really curious about is, if you know, like one thing that I think is interesting about, let’s use the, you know, Final Cut Pro versus TikTok example, right? One interesting thing is that, you know, you could argue about the quality of the input, but basically, you have like raw video footage, right. And so that’s the input into either, either program, right? So you know, you have a less sort of fully featured, you’re not going to make, you know, a movie that goes into an IMAX theater on your phone necessarily mean, you’re gonna be able to get there one day. So I totally get that. But what’s interesting about, like BI and analytics is that the inputs vary widely, right? And so I mean, from the quality of the underlying data to the modeling that’s done on that data by a team that makes it, you know, sort of possible to even do some of those queries, or like, see charts and stuff like that. So how do you think about the input, right? Because your success, it seems to me, would be highly dependent on the input, whereas with a Final Cut versus TikTok, you really just sort of need video footage.
Zack Hendlin 24:36
Yeah, I mean, I look, I think the input matters even in the Final Cut Pro TikTok, for example, so if it’s super shaky, and there’s a lot of wind noise, that’s gonna be not a great video. Either way. If I put my phone on a tripod, or if it’s realized in some way, maybe having a gimbal actually can make probably a pretty cool skating video. Go, even with my iPhone or a skiing video with a gimbal or something. And so the inputs do matter. I, the nice thing about data though is if your data is in Trino, Starburst, Snowflake, right? You’re gonna want some data quality checks there, regardless, right? Yeah, whether you’re putting that video into Final Cut Pro or TikTok, like you want it to be good at video. And I think the same thing is true with data, whether you’re going to analyze it insane, or whether you’re going to analyze it in Tableau, or Power BI, or wherever it is, you want to know that it’s up to date, you probably want some aggregates or views of those tables that give you like roll ups that are more useful. And so you’re absolutely right, like the input quality does matter. The nice thing, though, is you can have that same underlying kind of data store data warehouse, Lake House, depending on where you’re using, and hook zing to it. But also, maybe you hook your Python notebook where you’re doing and build a machine learning model. So usually, the infrastructure is already there. And what Zoom is doing isn’t necessarily trying to replace the finance team using Tableau that has, you know, 50 different numbers on it. But it’s trying to radically extend the value of the investment that you’ve already made in getting your data warehouse up and running, and getting your real time data streaming in. So you can update user attributes. And taking that and saying, Well, that’s the thing that doesn’t just need to be visible on your computer. Or if you want to query it, you have to kind of wait for someone to create a dashboard for you. And they have 10 Other things on their to do list, or rather, a thing where they can then use that on their own. And so that investment that you’ve made, is, if you think about it amortized over, over a much greater number of people. So we don’t expect that for most companies. In fact, some research from Gartner last year said they expect most enterprises are going to have multiple BI tools. And that reason is they serve different needs. Tableau is 740 bucks a user a year for that license, you’re probably not gonna give that to everybody in enterprise. And their mobile form factors are not good at all. It’s like this is the only. And so that solves a certain set of needs for maybe creating very specific charts for certain exact presentations. That doesn’t sell solve the need, though, of a salesperson who’s on the go, who wants to know if a customer is using the product, and doesn’t serve the need of a PM, who wants a real time product analytics and cut it by something for a new product that they launched this morning, but they’re flying today. And so we think that there’s going to be a set of tools that serve different needs. And where we see us kind of really tapping into a thing that we had no idea we had, when we built this, we had no idea these folks were tied up. We had a when we launched a product. We had a trucking company sign up. We had one of the biggest event companies in the world sign up. We had a retailer sign up. And you might say, why are they saying to you, a relatively young company, when actually a lot of them already had Power BI when we hopped on phone calls later had Power BI or had Tableau? And they said, Yeah, but it’s not useful to people in the field. Or it’s always wanting to work on an older extract of data, or I can’t get real time alerts in the way I want them. Or I have a set of dashboards, but I have a pipeline of, you know, two weeks of dashboards that I want to get created. And we said, well, what if you didn’t need to create a dashboard? What if it was literally a quick question, you could ask tap and drag count to people grouped by this over this time period. And that was a 10 Second thing, and it radically opens up? You know, for an energy company. They can know what wells are pumping right now, for an agricultural company that recently signed up, they can know, you know what sales they have lined up. So if they should be picking fruit, that might be better if it was on the trees for another week. And they want to know that when they’re figuring it out, literally in the field with fruit pickers, and a retailer wants to know where they’re getting low on inventory, and they’re not Walmart scale, they don’t have these huge systems. And they just have a Postgres database and they hook us into it. And they can know when inventory is running low on Pasolini products.
Eric Dodds 29:29
I love it. So fascinating. Costas please jump in here. I’ve been monopolizing the conversation as I always do, but that’s the show right? I’m monopolizing the new monopolize. And then Brooks tells us you’re done.
Kostas Pardalis 29:42
Absolutely. No, it has been great so far. So Zach, I have a clarification. Let’s equation. We are talking all this time about mobile, but there are different evils of mobile rights. It’s a different thing to have a phone is a different, completely different experience to having a tablet for example, right? And without even going into, let’s say more different sizes or forms, obviously, right? So when you say that you want to deliver a mobile first like experience, do you focus on this basic device? And does the device have to make that much of a difference when we are talking about BI? Because just to give an example, like I’ve seen, like design tools, like on an iPad, it’s a completely different experience, compared to sounding like an iPad, iPhone, right? Like you have print inputs, tools to use mechanisms, the real estate is much bigger. So tell me a little bit more about that. Do you focus on a specific device type?
Zack Hendlin 30:56
No, no, we think about the primary thing we think about is like, how do we have these bridges between mobile and web just like work? There’s literally a conversation I had with some engineers and designers on my team earlier this morning, where if you have a dashboard, how do we reflow it in a way that makes sense. So let’s say on the desktop, I have four charts at the top, one quarter wide. If I try and squeeze that down to your phone, it’s going to be probably illegible. And that’s basically what most of the legacy players. And so we reflow it, where we maintain the top left to right top to bottom border. But where we have rules and logic that makes more sense. So on mobile, we’ll show max two things side by side. And we’ll reflow it, maintaining that order in a way that makes sense. That’s like a small example. And that kind of extends across different screen sizes. What we do think about though, is kind of where users are primarily. And so what we’ve seen is, it’s actually primarily mobile phones, and then web. So we also have a web client. And we haven’t seen a ton on the iPad we were playing on on iPads and Android tablets. But this is actually very similar to what we saw when I was at Leap Day. And Facebook and all the other places are like, tablets definitely have a place. But really, if you’ve optimized for a really small screen, and you have a way that it can scale up, all the way up to the desktop, and you have that logic that actually handles you pretty well through the range. I think the challenge that most of the like legacy tools have is they thought about desktop, when you have this thing that is, you know, 50 different
Kostas Pardalis 32:43
Zack Hendlin 32:44
and you squeeze that down and make it super small. None of that is legible. And that’s not very usable. And so what we’ve done is said, Well, okay, how should that scale down? Well, you know, maybe you sample it, but if you sample it, then you need to tell someone, you’re sampling it, right, and you’re kind of downscaling it. So they really want every point that they have, you know, one tap ticket. And so a lot of it comes down to what I would call his progressive disclosure, we show you sort of the windowed version, the aggregated version, whatever it is. And then if you have the space, we automatically show you the whole thing. And if we’re showing you a subset, we tell you that very clearly, so you know exactly what’s happening. And if you want, you can say, hey, always show me the whole thing, even though it might visually look suboptimal. But that’s just what I want. So for instance, if you always want to table the 100 most recent user actions, not a sample of that, not just the most 10 reasons, you could say, Hey, are we showing the poll results? And we’ll do that. Because we recognize that, ultimately, a user needs to be in full control. And then we just tried to do the sort of things that you would probably do if you were building this as a data scientist on a project.
Kostas Pardalis 33:56
Yeah, that makes sense. So, okay, what are the limits on what can be done on a phone screen? Right? We because, okay, when we’re talking about BI, we’re talking a lot about visualization, right? Like people. I mean, naturally, they want to visualize things, right? Okay, like they will soon we just crawl like some tables, which, on its own, it can be like a pretty tough thing to do like cron, phone screen, right? But what are the limitations that you have certs so far? Yeah. So
Zack Hendlin 34:34
there’s a bunch of limitations, and a bunch of unique things that are great. Limitations are really big data sizes, you need to be clever about how you handle them. So if you have a result set that’s really huge. By default, we actually limit that in clever ways. But if you want the whole thing, you can get that. It’s just what we do if we progressively load that result. So instead of sending a billion rows to your phone, we see that, you know, there’s only 10 rows left. And when you get to row 90, we load the next 100 rows, right, we just do that stuff to make it make sense. Or if you want to filter, you know, you can just tap the column header and filter it. But for a big query, for a big result set, where there’s, you know, a million rows, you don’t want to do all that filtering on a phone necessarily. So over a certain size, that filter should be applied server side, and we should send the results. So to make all that stuff work like around those limitations, in a way that doesn’t feel limited, we spend a lot of a lot of time on that said, the realistic limits, are, you’re probably not going to build a machine learning model on your phone, maybe you want trendline or something simple like that. But, you know, if you really want to run psychic, learn, and develop a new Python package, like that’s not a thing you’re gonna do on your phone, at least at this point. We think that if you have really complex joints, simple joins are fine. But if you literally have, you know, 10 different tables, and you’re trying to manage them together in a very nuanced way, where you’re parsing some JSON from one, and then using that to join to something else. And all of that, that, that’s probably not the best use case for doing it on a mobile device. But frankly, the same would be true, if it was Tableau thoughtspot Power BI, you’re still basically going to want to do the really heavy lifting stuff in a precomputed table, or in a view or something like that. And, you know, that’s where, oftentimes, and, you know, Trino, or Starburst, or Snowflake, or BigQuery, someone has set up pipelines or data flows that make it more usable. The fact is, if you’re frequently querying that stuff, that probably should live in a layer before to BI. So hardcore machine learning, huge complex joins. And then I think the third area where I would steer away from mobile, is if you really want to do like, very unique, bespoke kind of graph types. So I don’t know if you’ve ever seen like the New York Times where they have these, like, really cool custom infographics. And as you scroll, there’s these diagrams that will show like a company’s earnings, and then it will show goes into a narrower funnel, and then splits in various ways. That’s like a thing where, you know, there’s a lot of nuance that goes into setting those up, they’re somewhat non standard, you probably want a lot of configuration options. And that’s probably not best done. On mice, where you have a lot of stuff you want to set up. So that’s probably where I would sort of not steer someone towards zing, or kind of mobile UI, we think about the kind of problem we solve is like the easy 80% of data questions. We’ve heard from data teams that at least half their time, hey, can you cut this by this? Can you graph this? Can you set up an alert that emailed me when this thing happens? And we want to make that stuff easy. And then we’ve been data scientists should, right be figuring out the right way to structure the data, the right way to get real time streaming data. And so then, you know, users in the field can set up alerts. So right, maybe they, you know, maybe they pump their data through RudderStack into Trino, or Starburst, BigQuery, or whatever it is. And then all the people in the field can do this really cool stuff on it. So we think about it as letting data scientists do more of the cool, interesting work of building machine learning models, building, you know, maturity models, building retention models, all that kind of more interesting stuff. And spending less of their time like, Hey, can you regenerate this dashboard with this one other filter?
Kostas Pardalis 38:56
Yeah, yeah. So Okay. Let’s see if I understand correctly. Okay, someone can do it. But first of all, like we did many different things with data in general, it’s from writing and building complex pipelines in Python, training models, and of course, like doing stuff like exploratory analytics, and dashboarding. And like, all these things, from one of these five things that someone can do with data, like from what I understand when we’re talking about a mobile experience, we are talking more about consuming processed data. It’s like we have some kind of report already made by an analyst, right? And then you want to have, let’s say, some level of control over that down or winterize it somehow like it’s not just a passive consumption of PDF file with graphs, right. It’s much more interactive, but still, we’re not talking about Putting someone on his or her phone and asking them to go and do something like their motor League, right?
Zack Hendlin 40:06
Right, you though can ask a question from any raw database, getting it hooked up to so I literally can go into a database of my user events and say, hey, I want to know the number of events split by type that happened over the last day or the last week, split by minute and set up an alert when that happened. And I can do all of that from the raw table, or raw view. Now, what I’m not doing is, and I can even define calculated fields and metrics that I want to reuse from Moodle, what I’m not doing is saying, Hey, I have these 10 different data sources that I need to manage together and create a table that’s going to update every day, I’m not doing that I’m still going to be your sort of traditional kind of data engineering. Yeah, even function. But that, frankly, what you’d be doing, kinder to prep your data, even if you’re going to do analysis in, you know, a Python notebook, or any other kind of BI tool. And we think that’s actually a really good use of a data team. Why? Because that’s the stuff that’s reusable. pretty broadly, that granular element for zoom, is not a dashboard, it’s actually a question. And the questions if you share them through your organization, are visible to anybody who you’ve given access, and you can search across any. So what that means is you’re not starting off with, Hey, what is this dashboard that needs to have been built? To answer every question specifically for this function, if Hey, someone has already looked at this, I can click that, I tap three dots to see make a copy and I cut it anyway warm. So I’m not limited to the filters someone has put on that. I’m not limited to how they decided to visualize it, I literally can’t change that tape, we still need good input data, as does any tool.
Zack Hendlin 42:05
We think about it as you actually should be able to ask any question from any of your data. That sort of relatively straightforward, and I would think about that, in a more quantitative sense is like, probably 30, or 40, lines of SQL tops. It’s more than that, you’re probably doing something pretty fancy, right? So it’s 20 lines of SQL, maybe you’re defining some calculated field, you’re doing some group bys, you’re applying some conditions, maybe you’re doing a couple joins, totally fair game to do insane. And we make that pretty easy. But if you’re if you literally have 10, different tables, you’re munging together, and you’re doing lots of complex casting. And one thing is a timestamp. And another thing is a date, and you need to harmonize those, match the names, do joins, there’s lots of incomplete stuff, right? That that you probably want to have handled kind of earlier on in the process. Yeah, it makes sense. Does the user have to actually write by phone? Sure. Well, so so so so in fact, the primary interface is you see, you open up the home screen, you see a list of all the questions your colleagues have asked. And dashboards they’ve created, if they have, and you can search through any of them, also see a list of all the tables at your organization they got access to. And so you can just tap on one of those tables and query those tables, and start asking a question from scratch. So your starting point is not one of necessarily just viewing as, hey, I want to see what a colleague did and build on top of that, or it’s I want to see a table and go interrogate something there. And so it’s very much focused on showing you what is possible, and what others have done, instead of just empty, like starting you off in a empty SQL Editor window. And then the whole UI is, you know, tap and drag, you tap a field and drag down to get the summer tap to drag to get the you know how to add a filter condition or exclude something or whatever. So you can do a lot of stuff, you can do regex, and all that kind of fun stuff. But we make it that level of complexity buried kind of one one level deeper, you could run full SQL if you want. There’s actually a full SQL Editor. But we bury that behind the three dots for sort of, you know, the DevOps use case, or the engineer who gets in pain and wants to go check something in the server logs, and they want to parse some JSON. And so by default, like, we don’t show parsing JSON, as one of the kind of top level things when you’re using the Visual Editor isn’t, that tends to be in the bucket of one level, more hardcore, one level more technical, like the typical business user, but if you want to do that, you tap the three dots you go to Custom sequel And you can parse your JSON fields and pull up the stuff you want for yourself.
Kostas Pardalis 45:06
Yeah, that’s interesting. And okay. You mentioned at some point like earlier that we are, I think you said like Gartner was saying that organizations and enterprises are going to be using, like multiple different BI tools, right? Yep. So, uh, how do you see this happening? Like, how like, first of all got a BI tool to operate between them? Can I have an analyst who creates, let’s say, a report on and is using Tableau for example, or leukemia, and somehow, these can be exposed, like through zinc, or vice versa? Because I’m not aware of like how this is, right now, we
Zack Hendlin 45:46
haven’t indexed on that. And, you know, if it comes up as a more frequent customer ask, we will, we’re not sure that the right answer, though, is saying, hey, go create a complex dashboard somewhere else, and then try to jam it out? If we actually think there’s value to say, Hey, what did someone in the field need? And how is that different from what someone, you know, on the finance team with 50 different metrics needs. So, you know, we could build a, we could build a way to pull that in, if there was demand there. But we actually think there’s a lot of value in saying, Hey, we’re gonna make it so easy to create something that you actually don’t need. That, in some sense, it’s more work to connect these things up, then just to say, hey, how many people did this kind of group buy this? Oh, I want to cut it some other way. Let me give you that. And we focused a lot on making creation easy. And the reason is, if you look at, even in the US, a lot of people, more Americans, according to Pew, 85% of American adults have a smartphone 77% of American adults have a desktop or laptop. So more people have a smartphone than have a computer. And it’s kind of like saying, hey, let’s try to bring everything in Final Cut Pro and jam it onto a phone, you still probably want to think through Well, if this was going to be a vertical video, if this is going to be a thing that’s supposed to be snackable. And more lightweight, instead of a feature film, like, what do we want to do that’s a little bit different about that. So it kind of makes sense. I’ll give a quick example, on Zayn, you’re on a phone, you could do it on the web, we’ve really optimized the mobile experience. And so what do we know about what exists on your phone, GPS, right, so you could get a location, if you get permission, and say, Hey, instead of Let Me query by warehouse and have a drop down, which is maybe what I would do in Tableau, to show me inventory for the warehouse that I’m in, that’s running low. And so that’s actually a way better experience that uses the fact that there’s information there sensors on your phone that you don’t have on your computer. Another use case is, hey, send me a push notification. And we’ve actually already built something that lets you do this, send me a push notification, when I tap on this chart, any value that’s above x of 10 units, or, you know, when it changes more than Y percent or whatever, minute by minute or day on day, and send me a push notification. And so those types of experiences are ones that really, really make sense to do from your phone using the sensors and the unique attributes of it being with you all the time and real time stuff, making more sense than it might have been a desktop. And so we focused really on making that a great experience, rather than necessarily kind of vacuuming up everything from every other tool, because a lot of dashboards, they don’t get used that much. And so we want to reduce the friction of asking a question, instead of a dashboard, being a week long project or longer at some organizations to create, we want it to be I think you can kind of quickly ask a question, instead of needing that, you know, layout and design the dashboard of
Kostas Pardalis 49:05
50 questions? Yeah. Makes sense. All right. One last question from me, and then I’ll give the microphone back to Eric. So, there are many things that are happening later like in technology that have to do with how like it, a user can interact with technology, right? We have all these AI things like generating models bookabach older stuff, you are building the products, again, like trying to utilize let’s say unique and new ways of dealing with information assuming also that like, we are talking about someone who is in motion or a bike, they are out there. And that green flag of you know, like unique environments to ask questions. So, as someone was, I’m pretty sure you’re spending like a A lot of time thinking about that stuff. Is there something in the near future that you’re excited about something that you feel like might change the way that we consume data, or we work with data? And I’m talking about like, from the perspective of the data consumer, the main user of the API to write. So what did you think that the next big thing will come from?
Zack Hendlin 50:27
Yeah, I think there’s two really exciting areas. The first is some sort of large language model, such as stable diffusion and open AI. They both have kind of ways to query across all of a huge number of data points on how people use language. So what does that mean? It means that if you say, hey, I want revenue by country, but your database only has sales, and it’s by region, maybe you don’t want to directly resolve that query. But you might say, hey, when you say sales, you may mean revenue. And when you mean country, but we are the only region we’re going to, we think that’s the only good you want. Does that make sense to you? Historically, you had to manually create all those aliases. And then there was stuff with Word to veck and vector embeddings. And kind of an understanding of how words were related with words like Tyvek, from Google and TensorFlow and stuff like that. And I think it’s getting good enough, where you’re going to be able to, and this is actually something we’re working on as well. Say, well, you’re asking for sales by region, but we have TX N, underscore MT, maybe for transaction amount. And oh, by the way, we have all these other questions that people have saved and tagged, or TX and underscore MT when they save the name of the question, they actually say, sales. And so we can build those linkages and build that understanding. And we think that some of what is happening with open AI and stable diffusion, and some of these, like large language models, will make that radically better than it has been historically. That’s one area I’m super excited about. It’s a little bit less like purely generative. And I’m a little bit skeptical . Hey, we will magically show you every interesting insight about all your data. I have never seen that work, well. Maybe it will get to the point where that is. And there are a bunch of companies that tried to do that. I just haven’t ever gotten everything I needed from that. And I think it’s hard to build trust in those systems, I think a much better way. And almost the way you think about building a newsfeed is to get some understanding of language. Let me take an understanding of your social graph within a company and use all that context to show you stuff. Here’s a question your colleague recently asked. And oh, by the way, you comment on a lot of stuff they comment on. And it’s related to topics that you frequently query, that is a great thing to proactively show you. So I’m not trying to do it in a kind of a vacuum, I’m trying to do it on top of this, these sort of large language models, and on top of the Knowledge Graph or social graph that you might have. That’s one really interesting area. And then the second, I think, really interesting area is A, like a more localized context. And that’s where you are, that’s what kind of you’re doing. So for instance, if I’m an engineer, and I have alerts set up, maybe I only want alerts when I’m on call for this given set of data. Or if I’m an inventory manager and a warehouse, and I frequently visit multiple warehouses, show me the stuff for the warehouse that I’m in right now. Or the really high priority stuff for a nearby warehouse, right. So your location, their alerting their, who you’re connected to in all of that context, I think is going to make queering data using data much more natural, much more organic. And you can imagine, hey, sales of a fast selling item are going so quickly. And you don’t have any more inventory coming in until a month from now, I probably want to let you know about that. So you take it off your website or so you can try and find more supply more rapidly. So I think those are two really interesting areas, large language models. And then there’s moving towards all the cool things you can do by connecting with the sensors in your phone and the kind of social network if you will, of the people you work with. And the questions they ask them to show you stop that’s way more relevant.
Kostas Pardalis 54:49
This is great. That’s all from my side dairy. I’ll give the microphone back to you. So we are alive. Again. I love it. Well, we’re
Eric Dodds 55:01
close to the brothers. So I only have one more question. But in this will veer more into maybe I could call it like, the world of ethics than, you know, data. And, you know, and BI. But when you think about mobile, it’s kind of interesting, right? There are all sorts of statistics about screen time. And, you know, how, you know, whatever, you know, engagement in these different apps, you know, spending too much time on that, and it’s like, harmful, all that sort of stuff. I’m really interested in, I mean, you can kind of have two different reactions. When I think of driving more experiences on mobile. My first one is, is more screen time on mobile a good thing, right? I’m sure there’s all sorts of statistics and arguments, I don’t know. But then I think, well, actually, like, that’s probably a much more effective way to use my time on my phone than, you know, scrolling through a Reddit feed about, you know, a stock crisis that I’m completely disconnected from, you know, just to get this spicy takes from people who I don’t know if have any authority subject. But anyways, I’m just interested to know, like, what do you think about that? Do you think about that? Yeah. So I think what
Zack Hendlin 56:23
What we’re trying to do is much more like high utility, right? We don’t think that I mean, if you’re a data nerd, and you love me, I’ll give an example. I used to travel with my laptop. And even when I was on vacation, whether it was management consulting, and even when I was at Facebook, because it was like, Hey, maybe I want to know, or maybe something breaks, or maybe I need to do something I can’t do on my computer. And we view kind of what we’re doing is freeing people from, you know, if you’re a DevOps engineer, and you’re on call, you actually can dig into the data, you can run the query, you can figure out what’s going on, without needing to run, you as a salesperson can go, you know, that client meeting and look at how many, you know, users are using your product or whatever, and be prepared, without necessarily needing to like tether your laptop, to your phone and try to figure it out, in a harder way. So we think it is like free? I think it’s probably more conflicted when, you know, at Facebook, or LinkedIn, or you know, some beetroot with YouTube or TikTok, then you’re like, people are spending hours a day on that stuff, collectively? And is that a good use of their time? And I think that’s fair, and it’s a much deeper question. And, frankly, I think the answer is that a lot of those systems are engineered to capture as much of your time as possible. Yeah, I don’t think that sales and marketing data is likely to be something you’re going to be spending three hours a day on your phone looking at. And I’d rather that, you know, if you’re at your kids soccer game, and you need to take some time to reply to a client, that you can do that in 30 seconds from wherever you are, instead of having to log on and take 10 minutes to do it there. So with that way work has evolved, it is such that people want to be able to get stuff done from wherever they are. And if you do, that’s actually very free. So I’ve been able to go skiing, and say, Hey, wait, something is broken with the city, look at these airs. Let’s fix that without having to like, literally, get off the mountain, go grab butter because I saw some planning via email, I’m actually able to go do that in the field. And I think that actually is very freeing. So yeah, macro question. Are people spending too much time on their phones? I think it’s a question of like, how is that time spent? And if that is spent by giving them more flexibility, helping them learn, helping them make better decisions, which is what we think Zynga is doing. Yeah, that’s great. And if that spent, you know, responding to every push notification that comes in on Wall Street bets on Reddit,
Eric Dodds 59:08
a, I think that’s less unequivocally clear. Yeah, totally. Well, no, I think, you know, I, one of the things that I would love to see result from from Zang is actually saving a bunch of time on, like executive meetings or even, you know, you’re on slack on your phone. And one of the challenges with decision making is that everyone has a hypothesis or an opinion. And it’s actually hard to access that data. Right. So I definitely see a world where Zinn can create that freedom by helping you be decisive and accurate. You know, in decision making, even when on the go, so very cool. Zack, this has been such a fun show. We learned a ton. And you’re doing great work. So yeah, we’ll have you back on soon.
Zack Hendlin 59:52
Thanks for having me. It was great talking to you guys.
Eric Dodds 59:57
What an interesting conversation Kostas had. I think my big takeaway is that I think Zack is onto something in that he recognizes the sort of undeniable trend of accessing business information on mobile devices, especially relative to, you know, the rise in remote work as a result of COVID general trends towards mobile usage. You know, I mean, are people going to start developing, you know, complex software, you know, and writing closure on their iPhone, clean, not. Because the ergonomics, you know, are just so, so difficult to distill. But consuming and filtering data really does make a lot of sense and a lot of ways for the use cases that he talked about. And it is surprising to your point in the introduction, that this really hasn’t been addressed well, before. So I’m excited. I think that I think they have a huge potential for success, just based on the macro trends.
Kostas Pardalis 1:01:12
Yeah, and I will add to that, that it’s also like his passion to make this happen. But it’s also important. There’s definitely a lot of opportunity out there, but there are opportunities that require a lot of focus, stamina, and like the wheel to make it happen. And he seems to be like the person that’s come to that who? Yeah, I feel like we are going, they’re going to be part of, let’s say, the next wave of BI tools out there. In 2000. You said like, it’s like a surprising part of conversation with him. There were many use cases, or having the data. Well, URLs are quite important. Which is indication of like, also how big this market is, right.
Zack Hendlin 1:02:05
Oh, yeah. Let’s see.
Kostas Pardalis 1:02:08
I think I’ll go and try it. Occasionally. Very curious.
Eric Dodds 1:02:13
I need to as well. I really do. All right. Well, thanks for listening in. Subscribe if you haven’t told a friend if you enjoy the show, and we’ll catch you on the next one. We hope you enjoyed this episode of The Data Stack Show. Be sure to subscribe to your favorite podcast app to get notified about new episodes every week. We’d also love your feedback. You can email me, Eric Dodds, at firstname.lastname@example.org. That’s E-R-I-C at datastackshow.com. The show is brought to you by RudderStack, the CDP for developers. Learn how to build a CDP on your data warehouse at RudderStack.com.