Nitay Joffe - Co-Founder & CTO at ActionIQ

(Joel Beasley at 00:00:00) Hello, my friends. I am very excited. Some special news today. We just finished recording our first masterclass, which covers the five biggest actions you can implement today to kickstart your leadership transformation and is jam-packed with insights that you can leverage to propel yourself and your team forward. Visit LeaderBits.io.

(Joel Beasley at 00:00:22) You can click on it. It's on the homepage, and it's absolutely complimentary. Today, we are talking to Nitai, the co-founder and CTO of ActionIQ, and we discuss activating the next generation of data, the binary nature of most decisions, and how making small incremental improvements over time is the key to long-term success. All of this right here, right now, on the Modern CTO Podcast. Here we go.

(Joel Beasley at 00:00:52) This is the Modern CTO Podcast. Where are you located? Where are you calling in from today?

(Nitai at 00:01:07) I'm in our office in New York.

(Joel Beasley at 00:01:09) Oh, you're on the East Coast? Nice. We're down in Florida.

(Nitai at 00:01:14) Oh, okay. Actually, going down there tomorrow, I think, actually.

(Joel Beasley at 00:01:18) For what? A customer meeting?

(Nitai at 00:01:20) Yeah. A sales meeting.

(Joel Beasley at 00:01:22) Nice. So you get involved in sales, CTO talk. Right?

(Nitai at 00:01:25) I do. Yeah. Kind of have to.

(Joel Beasley at 00:01:28) You know, it's funny. When I was first engineering, I thought, like, managers, the higher—they don't do the important work. Right? I mean, and then I realized, that's what drives the business forward and it's incredibly important. And without them doing that, being responsible for our actions, you have, like, 30 or 40 engineers, right, at your company?

(Nitai at 00:01:51) Yeah. We're about, yeah, 35, I think, now. About that.

(Joel Beasley at 00:01:54) So you're ultimately responsible for them.

(Nitai at 00:01:57) Absolutely. And you're absolutely right that, you know, it's funny. Each new role I've taken on—and I say new role because I think, as you've seen, CTO role definitely tends to change a lot. I would say every six to 12 months, it's a completely different role. And each time I'm in a new role, you have a newfound appreciation for other people who do that role. So kind of like you're saying, the managers and everything, same thing.

(Joel Beasley at 00:02:20) So where are you at right now? How are you spending your day?

(Nitai at 00:02:23) Yeah. It's interesting. So, you know, I would say the biggest change happened about a year ago, which was a fantastic, great change, which is we hired a VP of Engineering. Really, really amazing guy. In general, our whole executive team, I would say, is really fantastic. And so he came in to really kind of own and run and manage engineering and take that to the next level, you know, in terms of the growth of all the engineers, how they level up, organizational efficiency, all these kind of things that, you know, I had been doing. But let's be honest, between you and I, it's not really the number one thing that I'm best at, and it's probably not also the best use of my time, I would say, in terms of all the ways that I could be at. I was doing it kind of because it had to be done. And so now I would say I've shifted to more of an architect and product focus. You know, in my experience, there's roughly speaking kind of three or four types of CTOs, and I'm sure you've heard a lot of this on your show. You know, Werner Vogels from Amazon writes about it, and a lot of other people kind of write about this. Right? There's kind of the sales evangelist types. There's the product types, and there's the architect slash engineer types. And I've kind of been moving back and forth, I would say, between them.

(Joel Beasley at 00:03:52) You have to.

(Nitai at 00:03:54) You have to. Exactly. Absolutely.

(Joel Beasley at 00:03:56) Elon Musk, I like what he says. He says, you know, founding a company is like staring into the abyss and eating glass.

(Nitai at 00:04:04) Because you're always working on the most difficult problem in a business, and it's never what you want to be doing, but it's where you have to apply your abilities.

(Nitai at 00:04:13) And then, you know, it's funny you say that. I've actually literally used something related to that, related to Elon Musk quote, a few times, which is I've told people we're getting really, really good at chewing glass.

(Joel Beasley at 00:04:21) Yeah. Right? We're getting good at chewing glass. Yeah. Right now, I'm at the phase—we have a company that came out of the podcast. And so in the past year, we've grown to 11 people. So it's not bad. It's pretty cool.

(Nitai at 00:04:37) That's right. Yeah.

(Joel Beasley at 00:04:37) And my job has obviously been changing quite a bit as the founder of the company. And you're a founder too. Correct?

(Nitai at 00:04:46) Yeah.

(Joel Beasley at 00:04:48) So right now, I'm at that stage where we have a great product, people are buying, we know our sales conversion rates. And so the thing that I'm walking around literally with a note in my pocket, right, just because I hold the most important thing with me all the time, is increasing our lead generation. Because now that we have something that's good, it's a repeatable process, we know how to onboard customers, we know how to make them happy, we know how to show value, we know how to do everything. We have sales teams in place, and so now it's like, okay, everything's good. Everything's operating, but we're all sitting around like, let's get—

(Nitai at 00:05:21) Time to do more of it.

(Joel Beasley at 00:05:24) Yeah. Because we had it going. We got it going really strong with the meetings and the leads, but I was the salesperson. Right? So that's not scalable.

(Nitai at 00:05:32) Yeah. Founder selling can only go so far.

(Joel Beasley at 00:05:35) Right. And then, well, then also it changes. For you, the customer you spend your time with changes. Right? So now that deal that you're going down to Florida to talk with is a very different deal than probably your first deal or two.

(Nitai at 00:05:49) Absolutely. You're right. It sounds like yours is bootstrapped, you guys? Or are you a bit—

(Joel Beasley at 00:05:56) I bootstrapped the first couple months of it until we got $100,000 in revenue. And then after that, I went and got some venture capital.

(Nitai at 00:06:06) That's great.

(Joel Beasley at 00:06:07) Yeah. Because I was like, I can't do this myself.

(Nitai at 00:06:10) No. It's good to have the funding for sure. I think that's one of the common first-time founder mistakes, is trying to hold on to every little percentage and trying to kind of hoard everything and be like, no, this is my baby, and being kind of stingy with every little offer you make to an employee or a candidate or whoever, or an advisor, whoever it is. I think one of the things that we've kind of learned well is actually, it's much better to share, bringing in as many people as you can.

(Joel Beasley at 00:06:40) Yeah. Because you need a lot of great people to change the world.

(Nitai at 00:06:43) Exactly. Exactly.

(Joel Beasley at 00:06:44) So do you have a co-founder?

(Nitai at 00:06:46) I do. Yeah. Great guy.

(Joel Beasley at 00:06:48) What's his name?

(Nitai at 00:06:50) His name is Taso Argyros, and he's also very technical background, you know, ex-PhD Stanford, very, very sharp guy. At the same time, great business sense. Second-time founder as well.

(Joel Beasley at 00:07:03) Nice. So let's talk a little bit about, you know, what's your early days of technology? What's your earliest memory of technology?

(Nitai at 00:07:12) For me personally? Probably the first one would be programming the—you remember the TI calculators?

(Joel Beasley at 00:07:20) Yeah. Oh, yeah. The graphing calculators.

(Nitai at 00:07:22) Yeah. Yeah. So probably the earliest thing I can remember is programming those to do various things in math class much, much easier. I remember specifically, Euler's method. You know, this—random things that I remember.

(Joel Beasley at 00:07:37) The 8085 method?

(Nitai at 00:07:40) I'm joking.

(Nitai at 00:07:45) It was, yeah, it was like the calculus thing where you had to take a derivative, but then use it to approximate the next iteration of the function and so forth. And there was this thing that you had to do very manually, and they made you write out tables of this. But they let you use a calculator. So I said, why not just have the calculator do it for me? So you just input all the numbers and boom, you get all the iterations. And I'd just be sitting there copying the numbers from the calculator and be done 10 minutes before everyone else. So that was fun.

(Joel Beasley at 00:08:14) How silly is it thinking back to those days when they wouldn't let you use the calculator on certain tests?

(Nitai at 00:08:21) Mhmm.

(Joel Beasley at 00:08:21) They just made us be the computer, and it's like, there's no point of this.

(Nitai at 00:08:27) Oh, it's mind-boggling. Even some of the things I look at today—we were talking about kids. I was thinking the other day, I was literally wondering, do they still teach cursive in school? Is that actually a thing? That's good. It's not. Because I was thinking back like, man, that was such a waste of time. Yeah. Other than signing my name, what use does that have? Right.

(Joel Beasley at 00:08:50) So you program these functions. You use the graphing calculator, and so you start learning about some basic programming. And then you end up as a software engineer at Google. You must have been pretty stoked because that's—I don't know. I felt like caveman when I first wrote code. And so to get a job at Google would be 10 times that.

(Nitai at 00:09:13) Yeah. I mean, that was for sure a door-opening moment, and I actually got that—I was very fortunate to have a great mentor early on. So in high school, I was on the robotics team. I was part of the US FIRST. I don't know if you've heard of that.

(Joel Beasley at 00:09:24) Yeah. I do, actually.

(Nitai at 00:09:25) Yeah. It's a great program. Fantastic. Really fun, but also really engaging and stimulating and everything. And so through that, I kind of got friendly with one of the mentors, which was a guy from kind of the industry that would come in and help us with building a robot and everything. And we became very, very friendly, and he became kind of a personal mentor of mine. And he was the one who actually really helped me get my first job at Google. He kind of put in a good word for me, and that really was a door-opening moment for me. Absolutely. I mean, it was an amazing experience. And from there, it kind of just went on to more and more and greater things.

(Joel Beasley at 00:10:03) Now did you grow up in New York? Or—

(Nitai at 00:10:05) No. I actually grew up in the Bay Area. Well, originally from Israel, but most of my growing up was in the Bay Area.

(Joel Beasley at 00:10:12) When did you get to the States?

(Nitai at 00:10:14) This was in '93.

(Joel Beasley at 00:10:17) So you were like—

(Nitai at 00:10:18) I was around, yeah, like eight, nine years old.

(Joel Beasley at 00:10:20) Okay. So you're pretty young.

(Nitai at 00:10:22) Yeah.

(Joel Beasley at 00:10:22) Very cool. So then you moved to SF, you grew up there, and then you make your way to New York?

(Nitai at 00:10:27) Palo Alto, but yeah.

(Joel Beasley at 00:10:28) Oh, okay. Sorry. I've got SF on my mind because I'm going there in two weeks.

(Nitai at 00:10:32) Oh, nice.

(Joel Beasley at 00:10:33) We were just doing all the tickets and all the booking and everything like that, getting to speak at Williams-Sonoma. You know, the company. They own Pottery Barn and a couple other things.

(Nitai at 00:10:42) Yeah. Yeah. I know them.

(Joel Beasley at 00:10:43) Oh, okay. Yasser is their CTO. And he's a really cool guy. Came out of Walmart Labs and then Macy's, the CTO, and then came on the show. And I'm giving this talk all around the world this year about what I've learned from the podcast. And so I reached out to him and a couple of the people, and they're like, yeah, come on out.

(Nitai at 00:11:04) That's great. Yeah. That sounds like an awesome opportunity.

(Joel Beasley at 00:11:07) Right? So you do the robotics. You get in. He puts in a good word for you. You're at Google. Are you in apps division, payments division? Where are you at?

(Nitai at 00:11:17) Much, much, much lower than that. So I started in the hardware. We were doing the—if you remember back in the day when Google was doing the whole container data center thing? This was when they were just starting that. So I was on the server side doing, optimizing the server battery usage and the universal power supply, all this kind of stuff. And so I like to tell people I started very, very low, and I've only moved up the stack since. So my second—actually, we did a second internship at Google, then I was on the compilers team. And then I moved on to search engine at a startup I was at. And so from then, I've only kind of moved higher and higher up the stack.

(Joel Beasley at 00:11:57) And then to Microsoft?

(Nitai at 00:11:59) So Microsoft acquired the startup. The startup called Powerset, which was building a natural language search engine. Essentially, we were trying to take on Google and do a kind of more semantic-based search engine that would really understand, you know, nouns and verbs and adjectives and sentence structure. It was a very, very cool company. It was a great—you know, I think if there's one theme I've had is that I've been very fortunate to work with some really phenomenal people through my life, and Powerset was certainly one of those places. You know, some great companies came out of there too that many people are familiar with as well, like GitHub, for example, was one of the guys that we had.

(Joel Beasley at 00:12:44) Microsoft acquired them too now.

(Nitai at 00:12:46) That's right. That's right. Yeah. So then Microsoft bought us and we became part of Bing search.

(Joel Beasley at 00:12:54) Then you made your way to Facebook.

(Nitai at 00:12:56) Mhmm.

(Joel Beasley at 00:12:56) Were you doing search at Facebook too?

(Nitai at 00:12:59) No. So at Facebook, I moved more to the data side. I started in ads. I did ads infrastructure. I've always been kind of a systems guy. You know, I think that I've been doing—I love kind of optimizing deep low-level things at the same time, thinking about distributed systems problems and things like that. So at Facebook, I started on the ad side, and then I moved to data infrastructure. You know, one of the other things that I've had a pleasure to do a couple of times is work on some great open source projects. So when Microsoft—back to the startup I was talking about, so Powerset created technology called HBase that many folks have probably heard of, and open-sourced it, obviously, and it became kind of a great big data database. And we particularly used it to store crawled data, but lots of other people use it for many, many purposes. And at Facebook, I worked on another open source system called Giraph, which was to do large-scale graph computation. That was also something that we open-sourced. So I've been kind of in and out of both the deep corporate world, but also open source world. It's been interesting to see both sides.

(Joel Beasley at 00:14:09) But one thing they all have in common, though, is customer data. Right? And so that's where you've ended up here at ActionIQ. So tell me a little bit about what your company does today.

(Nitai at 00:14:19) That's exactly right. Yeah. So, you know, the theme of one thing that I saw through all these experiences was, I would say, a couple of things. One is I've been in the big data Hadoop, whatever you want to call it, world long enough to have a good hypothesis of where it's going. And, you know, in particular, using a lot of newer in-memory technology and newer advances in terms of how you organize and manage data and distribute it and so forth.

(Nitai at 00:14:47) That's theme number one: where the data world is going. Theme number two is, although the technology is getting more and more impressive and more amazing, the actual reachability of that and the access—I guess rather, better word—the access to it is probably only diminishing. Meaning you have the very, very top companies, you know, the Facebooks, the Netflix, the Amazon, the Google, and so forth, that are at this cutting edge of technology and then are able to actually utilize this technology to actually drive their business, to do much, much richer personalization, to really understand their customers and give them a whole new level of experience and a whole new level of engagement that consumers are kind of becoming accustomed to. Right? When you go to, I don't know, if you go to just some random mom and pop website to buy something, you almost expect the Amazon-like experience.

(Nitai at 00:15:44) Or if you go to some random channel to view, it's like YouTube, you're almost like, "Wait, why isn't this like Netflix? Why isn't this, you know, understanding me and recommending things for me?" So that's kind of theme number two that I've noticed is the lack of access at what I call the other 99% of the world. Right? The non-1% sliver of high-tech companies.

(Nitai at 00:16:05) And thing number three, kind of going along with that, is the access, but then also how much it actually makes it over to the business side and drives the bottom line. And so we started ActionIQ to really help dramatically improve on that problem. And what we're really doing with ActionIQ is building this kind of next-generation data platform, but for the business. Specifically, where we've started is in the marketing landscape. And there's just an enormous, enormous need, you know.

(Nitai at 00:16:41) And I think you can, if you go back, you can almost predict this because you can see the evolution of data in these big environments. Meaning, you know, if you go back a few decades, you had the world of Oracle and IBM and Teradata and so forth where businesses were just starting to put in some central data warehousing. Then after that came the wave of, actually, you should be capturing everything and there's actually a thousand times more data out there than you're necessarily capturing. So this came the world of capturing—sorry, capturing every web click or every mobile tap or what have you.

(Nitai at 00:17:22) And suddenly, the businesses are becoming inundated with the amount of data that they actually can have about their own customers. It didn't used to be that way. It used to be everybody did everything based on, "Well, I know you're Joel, and I know you're a male, and I know you're, you know, between the age of whatever, 25 or 35," whatever it is, and then that's kind of all I know about you. Now all of a sudden, there's this wealth of information. And now we're in this kind of next generation of that, which is, how do you actually activate that data?

(Nitai at 00:17:48) And by activate, I mean, how do you unleash it on all the rest of your business, not just the deep IT analytics? How do you enable your marketing folks, your analytics folks, your data scientists, everybody to really access and utilize that to do very, very deep level of personalization and understanding about your customers? So that's kind of the high level of what we do. Where the rubber really kind of meets the road is, you know, we tend to work with both, I would say, the medium to large companies. You know, some of our customers include companies like Verizon or New York Times, but also some of the more mid-sized retailers as well.

(Nitai at 00:18:38) And there, um, you know, it's interesting because they're all kind of having the same problem of, you know, they're somewhere along the spectrum of going through what you might call the digital transformation. Right? And wherever they are along that spectrum, they're all wanting to do utilization, get to a lot more in-depth, and essentially be able to act like a Netflix or Amazon with their own data. And the reality is, you know, it's interesting because every day there's probably 10 new articles about how can retail compete with Amazon, for example, or how do you stay relevant in this kind of new consumer age.

(Nitai at 00:19:22) I think the reality is actually that there are companies out there that are able to do it, and the ones that are able to do it are actually even able to not just compete, but really be very, very competitive, I guess, because they're realizing that actually they have years and years, decades even, of first-party data about their customers, you know, like offline transactions, what they've done in the store, how they've interacted with the customer rep or the call center, things like that that are actually there. It's like these data silos that are waiting to be unlocked and utilized. So long-winded answer.

(Joel Beasley at 00:20:05) No, I love it. It gives a—like, I understand because, well, I'll back up. So you guys offer some sort of consulting approach with this, right? Like you come into the company, you have to look at their data. Sometimes they know what they want to do. Sometimes you have to take a look and give them some ideas because you help them activate the data. Correct?

(Nitai at 00:20:27) Yeah, a bit. Yeah. So I would say, I think it's important to understand we're very much a product-oriented company. And it's important to make that distinction because many, many of the companies in our space are much more consulting and services based. Right? Like they literally—and in fact, that's a lot of the approach that current large enterprises take is, "Let me bring in a big consulting firm, and they'll just tell me what to do." And the problem with that approach is it really doesn't scale. And by scale, I mean it's not really repeatable. Right?

(Nitai at 00:20:55) Like they solve the current problem that you have, but they don't necessarily set you up to solve a thousand more of those problems because they're just throwing people at the problem, right, as opposed to throwing better technology or better solutions. And sometimes they do bring those in, but you really need the holistic—you need both of those worlds. Now to be clear, you know, it's impossible, I think, to have a pure product company that does nothing but product, especially in a space like ours. The reality of selling to large enterprises is you have to do the services too. Right?

(Nitai at 00:21:28) Like it's—one thing I always say is there's, you know, there's that famous saying, I think it was Benjamin Franklin or whoever that said there's only two certainties in life: death and taxes. Yeah. I think there's actually three certainties. There's death, taxes, and data is messy. And so you need to, of course, solve that problem. And a lot of actually what we've built in terms of, you know, if you kind of peel away the layers of the onion and you get to the core of the ActionIQ database and the technology underneath it all, a lot of it is improving that and understanding that data is messy from the get-go and building a lot of technology to make it very, very easy to do those things. And so, yes, absolutely, we have services, and it's definitely a core part of what we do because it's a necessity. But it's product-focused.

(Nitai at 00:22:16) And at the end of the day, the main difference is bringing in and establishing a better product, a better technology, a better process for the company that they then can go and own and, you know, improve the way they do business and the way they personalize to their customers and the depth of data that they're able to utilize. You know, one of the examples I always give to go back to Netflix or something is, you know, the reason they're able to make such good recommendations is, again, not because they know that you watched two movies in the last month. Right? It's because they know that you watched this particular movie that is a thriller at 8 p.m. on a Thursday, and then they know that the next week you watched another thriller at 8 p.m. So now they're seeing some pattern of, like, okay, Thursday nights are your nights where you like to really engage with something thrilling and you're able to watch the full two hours, whereas on a Sunday, you only watch 30 minutes, maybe you come back to it.

(Nitai at 00:23:15) Right? Like that level of deep nuance is the thing that really lets you personalize. It's the thing that really lets you actually use your data to make better decisions, to make your product better and to engage with your customer better and to have a better experience, which is ultimately what it's all about. And that requires a fundamentally new way of thinking about the problem and fundamentally new technology as well. The other thing to realize, you know, we were talking about the data evolution. In parallel with that, I think the interesting thing to realize is—and, you know, I'm sure you've seen one of those iconographs with the PDF with, "Here's the 6,000 companies in this landscape." Right? It's like, go ahead. Good luck picking one. Right?

(Nitai at 00:24:00) You know, it's interesting because the vast, vast majority—I mean, literally 99.9% of those—were very much built in the old school way of doing things, which is before every company was collecting every single click and every single mobile tap about their customer. And so the data technology that they were built with just can't handle that scale. And so it's just not able to actually get to that level of depth and that level of nuance. Whereas ours absolutely can, again, because it comes from years of my own learnings from doing Facebook scale computing and my co-founders as well. And so that's the difference, I would say, the big difference in what we do.

(Joel Beasley at 00:24:43) So a minute ago, you were speaking about becoming better, right? And we have a large part of our audience is that mentality, people that are always looking for an edge, always looking to become better. So let's imagine that some people are listening that say they're maybe where you were at Google or a software engineer and they're going to move into a team lead position, right? Start leading their first team. What advice from your past? Like, what comes up? What would you tell that person?

(Nitai at 00:25:15) That's a good question. You know, I think there's—I don't know if you call this as much advice or learning, but I'll say I have a couple of things that come to mind that I guess would have been things that I would have loved to have known as early or much earlier on that I know very deeply today. So for example, one is, you know, I think early on in my career—early on, I think, in many people's career—you kind of go about the decisions that you make or the meetings that you're in thinking that everything is important and some things are just more important. But everything has a base level of, "No, no, no, I gotta be—I'm the lead. I gotta be in this meeting. I gotta be in that meeting. Like, wait, you gotta be careful with that decision and this decision." And it can easily get into micromanagement and all that. And the reality is—and it's interesting because I think most things with how you run a business or how you lead an engineering team have a spectrum to it. But when it comes to decisions, I actually find more and more it's much more binary. And by binary, I mean there's actually the vast majority of decisions really don't matter that much.

(Nitai at 00:26:18) Meaning, like, it's not that they're not important at all, but in the long term of things, if you start to think six months out, a year out, two years out, they're not actually that crucial, and you can more often than not delegate those decisions or let, you know, one of your senior tech leads or whoever it is or senior engineers on your team own that and just be more on the periphery of it. But on the flip side, there's a few decisions that really matter, right? And they matter and they matter a lot more than you thought they did. So it's actually kind of more bifurcated, I think. And I think over time, you kind of learn to recognize which decisions, you know, you need to step in and say, "Okay, hold on. Pause. This is an important decision. You know, I'm not saying you're doing it right or wrong or whatever, but we just need to take a little bit more time with this because I know that this decision is going to affect us for the next two to five years."

(Nitai at 00:27:11) Like, there's a decision we're making right now that we're not going to back out in a month or two. Whereas a different decision, you realize, actually, whatever. No big deal. If we do it this way versus we hack it that way, three months from now, won't really matter. So I think that's one good learning I've had. The other one—and this is actually an interesting talk that I have with pretty much every new person we hire—is I have this talk of when you, especially the more senior engineers, when you go about a project, how long are you building it for? And what I mean by that is, you know, in my experience, essentially, when you build a new project, you build some new piece of technology, you bring in some new libraries, some new open sourcing, whatever it is, you can really do it for kind of one of three time scales. And, obviously, I'm grossly approximating here. But roughly speaking, you can build a new project for us that will last six months, that will last around two years, or that will last five-plus years. And I think it changes a lot which of those—I think it's important to be mindful and actually go into it thinking, "We're going to do this thing to last X," because that shapes all of the decisions around it. And suddenly these decisions that, if you didn't have that mindset going in, you would think, "No, no, no, no, that's way too hacky. No, no, no, no, no, that won't scale," or on the flip side, "No, that needs to scale more. Like, no, that's actually a really important piece because we're building this to last X amount of time."

(Nitai at 00:28:49) And so I think going into it, setting the right expectations, both for yourself but for others, and aligning on, "This is the expectation of what we're building this for"—you know, a lot of times having a, if it's a project that has a, for example, a product manager involved, that can help a lot because they should be helping a lot with the requirements. But from the engineering aspect, what I found is interesting is, especially for—and I think, again, it depends a lot what scale of the company you're at. Like, so for example, for a startup, it's almost never correct to do something to last five years. It really isn't because nothing actually lasts five years. And if you're at that scale of company, right, when you're small, when you're five, 10 people or whatever it is, there's no way that five years from now, this thing will be just as valuable as what you initially intended it to do five years ago. The rules of the game, the requirements, the usage of it is going to change so much that I guarantee you within two years, you'll probably end up rewriting it. On the flip side, often, you know, so that may mean that—so that really brings you more to, "Do I build something for two years or do I build something for six months?" And usually, if it's a big project, in my experience, usually two years is roughly the right time. If it's a quick project that you're just trying to get out of the way and get it done quickly, then you go for the six months.

(Nitai at 00:30:09) But the two-year time frame, the six months, if it's a big project can end up being a mistake because the reality is you spend three months designing it. You spend three months researching and doing all the kind of necessary upfront work, and then you only got something that actually lasted you three to six months in terms of actual usage before you're like, I need to redesign it. But two years is about the right time because two years is about the right time frame where you go and rewrite things. And I saw this at the startup I was at before, and I've seen it even here. You know, one of the things that I'm very proud of for us is, if you look at ActionIQ today, we're around four or so years old.

(Nitai at 00:30:47) And indeed, some of the biggest systems that we have have been rewritten once and are now in their second iteration of rewriting. And I'm incredibly proud of my team because we recognize that that was the right thing. The fact that we're now four years in and this key system that is the key delivery data system pipeline, whatever, is now on its third iteration meant that it exactly served its purpose. It did the right thing for two years, and now the requirements have changed completely because there's a whole—like you were saying before, we're talking to new kinds of customers, there's new kinds of use cases, and so forth. So now it's time to go and rebuild it.

(Nitai at 00:31:24) And so that's the other big learning. And it takes also the right kind of team and the right kind of leadership to also not have the fear of that. Right? Like I said, I'm incredibly proud of the fact that four years in, we're still taking on and will continue taking on these kind of big, massive projects, not just doing these tiny incremental improvements.

(Joel Beasley at 00:31:47) Nice. I like that. I like the separation between the time frames. So as we begin to wrap up, I'm curious to know, what specifically are you most excited about today? Like, when you get out of bed in the morning and you're going to go in and do some great work, what's on your mind?

(Joel Beasley at 00:32:03) What project are you most excited about today?

(Nitai at 00:32:08) Yeah. So I'd say there's two parts to that. So one is on the business side because I do end up spending a lot of time there. You know, it's really fun to see how your product actually really changes the day in the life of somebody. Right? It's really, really cool to see some of these businesses from large to small and along their journey of this, again, kind of digital transformation we talked about, and being able to help them along that. It's also really cool, and I enjoy a lot, some of the interactions I've had with some of the CTOs of larger companies and so forth. And it's really cool to see that everybody's figuring it out. Meaning, whether you're a very, very large Fortune 500 company or whether you're a small retailer, everybody's going through that change and figuring things out. And I'm having conversations with them like, okay.

(Nitai at 00:33:04) So should we be building out our own data science team or should we hire out for that? Or should we bring in consultants and so forth? I think part of that is really fun and really cool, and it's really cool to see how technology can drive—and technology and the right product and the right business solution can really drive change and improve these people both from the day-to-day of the marketer's life, like, just the feedback we get of, like, oh my god. I didn't even think this was possible. How do you guys do this? All the way to the senior executive who is like, we've got ActionIQ, now our world's changed. Right? So that is incredibly rewarding for sure. From the technological aspect, you know, I've always been a huge believer in team and people. I spend a lot of, I would say, a lot of my own just kind of personal mental effort to make sure we hire the right people.

(Nitai at 00:34:00) And because of that, we've ended up with a really, really phenomenal team. And so the thing that excites me from a technological perspective is, yeah, I mean, again, some of these bigger architectural projects we're taking on, just sitting in the room with the engineers that we have and talking it out and talking about the latest trends of databases and distributed systems and how—and should we utilize this versus that and where should we be doing our own innovation versus pulling in open source things and so forth. That is really, really fun.

(Joel Beasley at 00:34:30) Yeah. It's like one thing to be in a room of experts. It's another thing to be in a room of experts who all work for your company. And that feels really, really good.

(Nitai at 00:34:40) It does. And especially, you know, one of the things that I think I've been, again, fortunate with is—and it takes a certain kind of personality—I really enjoy feeling like the dumbest person in the room. I really do. Like, it's the most rewarding thing to be in a room of, like, there's nine people here who are better than me in some way, shape, or form, and I have something to learn. Right? That's the really rewarding part. The other part is, you know, I think you kind of touched on in the beginning when we started talking is, obviously, startups and companies have their ups and downs and everything, and you know, one day is crazy this way, one day is crazy that way. No matter what, it's not boring. Like, if there's one thing you could say is it's never boring.

(Nitai at 00:35:20) There's always something new. There's always a new hole to plug somewhere. There's always some new fire. There's always something fun and interesting going on. I think as long as you have the right perspective about it—that reminds me, by the way, my thoughts tend to jump around—a question you asked about kind of what would I tell someone who's a new lead or a new manager. You know, the third thing is, I think, related to the timelines is the perspective of thinking, meaning the ability to think long-term. You know, I think as you step into more and more leadership roles and eventually get to the executive and directing multiple teams and so forth. The really, I think, one of the biggest things that changes is the time frame that you need to be thinking at versus your people need to be thinking at. Right? So your team might be executing on the particular project right now, and they have kind of—what's in their mind is, okay, I'm working on this particular feature or, you know, here's kind of our next three to six month roadmap or whatever it is.

(Nitai at 00:36:25) But you need to be thinking six to twelve months out or twelve to twenty-four months out, whatever it is. So I think a lot of that tends to change, and you tend to have kind of a different perspective of how far out you should be thinking. I think it's something good to be aware of.

(Joel Beasley at 00:36:40) That's a good one. I like that. It reminds me of Jeff Bezos. I

(Nitai at 00:36:45) listen to

(Joel Beasley at 00:36:45) a lot of his interviews and things of that nature, and he says I'm always living twenty-four months in the future. He's like, whenever my team needs me to pull me into the present, I'm like, what's going on? What fire? Like, why are you pulling me into the present? Because I'm living a year or two in the future.

(Joel Beasley at 00:36:59) And I was like, oh, there you go. That made me really happy.

(Nitai at 00:37:04) Exactly. Exactly. And that—yeah. I mean, he nailed it. That's exactly right. The other thing I'll say, actually, I was just reminded of this too, is, you know, there was a—I think it was a Google guy that said this, and I really liked it—was, you know, Google is obviously a fantastic company. I have nothing but love for the company. I think they really do some amazing things, and they have some amazing people. And I absolutely love my time there. And I think one of the cool things is, you know, you see all these achievements that a company like Google does, and you think, man, they're—or like an Elon Musk with SpaceX and it's like, man, they're reaching the moon.

(Nitai at 00:37:39) Right? They're going to the moon or whatever. And the reality is, nobody actually says—even Elon Musk, I don't believe says, I'm gonna go to the moon, and then they go to the moon tomorrow. That doesn't happen. Like, the reality is you set a vision and a goal for where we want to go to the moon, but in order to do that, we're gonna go to the next rooftop. And there's actually a famous paper about this from one of the Google guys that said make rooftop—rooftop or roof shots, sorry. Make roof shots, not moonshots. And this whole thing was about how it was—actually it came from the hardware side. It came from the data center side of things where it was kind of analyzing how is Google's data center so efficient. Right?

(Nitai at 00:38:16) Like, they've optimized the airflow through the servers and the wiring, every little tidbit. And so today, their servers and AWS as well, right, like these cloud providers are so far beyond what if you're thinking, oh, I'm just gonna go grab a couple servers and do my own data center colocation. They've reached so far beyond that. And why is it? Because they made all these incremental small improvements that compound and add up greatly. And that's the other thing, it's actually a big theme that we have here at the company. We call it—there's a Japanese term for it called Kaizen. Kaizen means essentially, it's kind of the art of incremental improvement, and we're big believers in that, you know. And actually, this goes all the way back to where we started with kids. You know, I've even seen this with kids. The amazing thing is the thing that I've realized is, I don't need to teach my daughter astrophysics today. But if I teach her one plus one today and then tomorrow she comes, and that's already something that she knows, then tomorrow you can teach her two plus one, and then you can go to multiplication, then you can—it keeps building and it's this amazing thing that you realize the whole world actually functions that way.

(Nitai at 00:39:25) Companies function that way. People learn that way. Like, everything builds on the previous. And so if you actually—you'll see kind of the far vision, but at the same time are able to break that down into incremental steps. That's how you actually get there. And from the outside, all the person sees, like, you know, a customer or somebody reading the news or whatever. All they see is two years from now, you reach the moon. That's amazing. How did you do it? Clearly, you're a magician.

(Nitai at 00:39:49) Right? And the reality is no. It was just a lot of just grinding and glass, but getting there step by step by step. So yeah.

(Joel Beasley at 00:39:56) It's difficult because our minds are wired linearly, but things compound and progress exponentially. So when we imagine work, right, when we imagine where we wanna go and the work, it's—how do I say this? I've never described it against work before, but we just think—our natural—when you ask a normal person to imagine where we're gonna be in five years, they go back to where we were five years ago. Mhmm. They analyze where the progress you made in the past five years and they just push that forward.

(Nitai at 00:40:32) Right. They draw linear as opposed to exponential.

(Joel Beasley at 00:40:34) Yeah. Right. But that's how it happens. So in the next five years, it's going to go way—I always like to bring back, and I'm like a broken record with this. We got electricity, like, a hundred and ten years ago.

(Nitai at 00:40:45) Mhmm. And

(Joel Beasley at 00:40:46) there's people that are a hundred and ten years old on this planet.

(Nitai at 00:40:49) Mhmm.

(Joel Beasley at 00:40:49) And look what we have today.

(Nitai at 00:40:51) Right. It's

(Joel Beasley at 00:40:52) insane. Like, that's insane. And so it's like when people start talking about, oh, we're not gonna have general AI for a hundred years. I'm like, probably like twenty-five.

(Nitai at 00:41:04) Probably like

(Joel Beasley at 00:41:04) twenty-five, thirty years we'll have it because we are just going so fast. So

(Nitai at 00:41:10) I mean, fifty years, you know, computers in a whole room. Yeah. No. Absolutely. I agree with you. The other thing that, actually, I was reminded is, you know, more and more as I've led teams, led companies, and so forth and so on, you really realize how much a technology endeavor is really more a people endeavor than a technology one. Meaning, it's so much about the people that you work with day in, day out, and so much of your success is really about the people than about the particular project and whether you had the particular right patent or technological innovation. And to get that formula right, I think you really have to treat people right. You know, we have a saying here that I—the way I like to summarize kind of the ActionIQ culture is we treat people like adults.

(Nitai at 00:42:02) And it sounds kind of simple and stupid. Right? It's like, well, of course, you treat people like adults. Everybody's an adult. But the reality is few people, I think, few companies, few teams, I think, actually treat people like an adult. Because when you treat people like an adult, what it means is, you assume that, for example, they can take good news just as well as bad news. Right? So you're able to be very transparent and open with them. They're not just gonna go cry in the corner or kind of, you know, put their tail between their legs and run away or whatever. Right?

(Nitai at 00:42:34) So you're able to be very transparent. At the same time, you're able to give an adult—as somebody who's able to take a lot of accountability and be very upfront and candid about things. And at the same time, because of that, they're able to take on a lot of responsibility. So you're able to delegate a lot and they're able to really own a lot of things. You know? So it kind of, when you're in that mentality, you really steers you away from micromanagement and dating people and all that, and it really kind of pushes you to treat everybody in that mindset. And then even the amazing thing about it is that even the people who are not necessarily fully there yet, it pushes them to be like that. And it creates this culture of, like, no. Actually, you know, whether it's a good day or bad day, we talk about it just the same, and we analyze it just the same. And we're not shy about being very, very transparent about everything going on with the company, and I think people have appreciated it.

(Nitai at 00:43:28) You know, we have—we have an all hands, a weekly all hands, and we're very open at that all hands. Like, we have basically an open Q&A with myself and my co-founder, and we talk about everything from, you know, whether it be the deal that we won or lost or funding or whatever it is and anything. I think that creates a great culture and a great place that people really enjoy because they feel like they're really part of the team.

(Joel Beasley at 00:43:52) Now are most of your team—most of your team is on-site in New York?

(Nitai at 00:43:56) Large portion. Yeah. Yeah. So we have sales kind of throughout. We have a few sales all over North America. But the vast majority of the technical team, yes, and the customer team, the engagement team, the accounting, account management, is also here as well.

(Joel Beasley at 00:44:12) Excellent. Well, the next time I'm in New York, I'll let you know, and I'll stop by and say hello.

(Nitai at 00:44:17) Absolutely. Always welcome. Happy to have you over.

(Joel Beasley at 00:44:21) Alright. You have a great day, and I'll let you know next time I'm in New York.

(Nitai at 00:44:24) Sounds great.

(Joel Beasley at 00:44:25) Talk soon.

(Nitai at 00:44:26) Alright. Take care.

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