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Leveraging Data Analytics for Business Growth (Episode 114)

Tarush Aggarwal is the Founder and CEO of 5x, which offers data reporting as a service. 5x allows users to make data-driven decisions faster, which are necessary to succeed.

Tarush is also one of the leading experts in leveraging data for exponential growth in the world!

Previously, as the Head of Data at WeWork, Tarush scaled the system to support 12k employees and 100 data team members. Tarush was also the first data engineer at Salesforce in 2011.

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What you will learn

  • How Tarush transitioned from a Data Engineer at WeWork to building his own company
  • How Tarush discovered his love for data
  • How companies can build and use data analytics in an efficient way
  • Modern Data Stack: one of the biggest movements in the data space
  • How 5x can help companies implement data strategies
  • The 3 main things companies care about when collecting data
  • How your company can become more data-driven

Transcript

Jeff Bullas

00:00:06 - 00:01:30

Hi everyone and welcome to the Jeff Bullas show. Today, I'm recording from Adelaide in South Australia, which most people haven't heard of and I'm having a chat today with Tarush Aggarwal. Now Tarush is currently in India. He sometimes lives in Bali and sometimes he lives in New York. That's what's great about being a digital entrepreneur is that you can do that sort of shit everywhere. So Tarush is the Founder and CEO of 5x which offers data reporting as a service so users can make data driven decisions faster, which are necessary to succeed. Tarush is also one of the leading experts in leveraging data for exponential growth in the world. Previously, as the Head of Data at WeWork, and we've heard of WeWork most of us anyways, Tarush has scaled the system to support 12,000 employees as well as 100 data team members. Tarush was also the first data engineer at Salesforce in 2011,

Welcome to the show, Tarush. It's an absolute honor to have you here. And I look forward to finding out your experiences and insights about this digital world we live in and how, as entrepreneurs, we can actually take advantage of this.

Tarush Aggarwal

00:01:31 - 00:01:37

Awesome, Jeff. Thank you so much for having me on the show. Very, very excited to be here and hopefully add some value to your listeners.

Jeff Bullas

00:01:38 - 00:01:54

So Tarush, you’re originally from India, you've been to Carnegie Mellon University, you've been also to a University of, summer, I think a summer camp for the University of Melbourne, which is in Australia.

Tarush Aggarwal

00:01:55 - 00:01:56

Yep.

Jeff Bullas

00:01:57 - 00:02:51

You've also spent and worked in Bali, which for those who don't know is just north of Australia. It is one of the most popular destinations for Australians. It's this incredibly tropical sort of freewheeling, soulful place where you can actually experience beach tropics. It's just a fabulous place to be. I've been there once, I want to go back there again. But Tarush spends six months of the year there. So Tarush, let's talk about how you fell in love with data. Where did that idea come from? Like you got hired by WeWork, you've worked at Salesforce. So how did you fall in love with data? And it's important and then discovered how important it was.

Tarush Aggarwal

00:02:52 - 00:05:27

That's a great question. You know, I, like many others, I went to school for computer science and was fortunate enough to get a job at Salesforce.com after graduation. And you know, my first day of work, I very distinctly remember knowing that, you know, this is not something, this is not something I'm going to enjoy. So, you know, work for many years. Finally got there and just realized very, very quickly that holy shit, I made a big mistake and you know, a big part of that was, you know, the playbook on how to do software engineering for the most part was already established in years, how you do it, you're the best practices, you know what success looks like and not to say that that's not very important. Obviously it's super, super, super important that software engineering, that, you know, folks are into suffering during help build new products. You know, it wasn't something I particularly was interested in and you know, it happened to be a sort of classic case of being in the right place at the right time. Back into, back in 2011 when I started, no one was really talking about data and no one in the valley had a data team and I met this path manager.

Yeah, it's sort of social media in a Facebook and all of these tools that kind of had, you know, came out in like 2007ish, right, at least that's when they started getting popular. What was happening was we were transitioning from having information on our personal computers to having information in the cloud and because of that, you know, became this need of how to be analyzed and storing and sort of query and build analytics and all of this information in the cloud. So, you know, in around 2011, some of these conversations were going on, it was very, very early in this sort of data sort of landscape. And I, I was able to, you know, I was introduced to a sort of product manager at Salesforce who was interested in working on this and and two of us partnered on this and you know, that was the, you know, early, early, early data team at sort of Salesforce and kind of, that's really where it came from. And you know, ever since then, I've sort of never looked back. So I've been in the data space since back in 2011, but truth be told it was just an accident.

Jeff Bullas

00:05:28 - 00:05:32

Well, I think almost every entrepreneur is an accidental entrepreneur.

Tarush Aggarwal

00:05:34 - 00:05:35

I love that.

Jeff Bullas

00:05:37 - 00:05:47

So we sort of stumble into it. Steven Spielberg says, what should be doing in life will show up as a whisper. It won't shout at you.

Tarush Aggarwal

00:05:48 - 00:05:48

Yeah.

Jeff Bullas

00:05:49 - 00:06:04

Yeah. So you heard a whisper, which was a really, and I think we need to discover, not, not just what we love doing, but quite often we only discover that by discovering what we hate doing.

Tarush Aggarwal

00:06:05 - 00:06:52

Yeah. I fully agree. Like, you know, after a slightly different way of doing it is, you know, I had the opportunity of doing something in the data space a few times over. So I was able to do a Salesforce and moved ever since and then, you know, it's sort of eventually joined WeWork and sort of now at 5x and you know, every time I moved from, from like one journey to the other, all I sort of realized I know is not what to do, it's, it's more so what not to do and you know the sort of attorney of what not to do at at like every time is in particular as helpful if not more than what to do.

Jeff Bullas

00:06:53 - 00:06:57

Yeah. So when you're saying what not to do is that something you don't enjoy doing?

Tarush Aggarwal

00:06:59 - 00:07:44

More so on like, you know, you learned like, I've learned from experiences that yeah, ultimately when you're trying to add value, there are like 100 different ways of doing it. And you know, at a particular point, we pick one of these ways and we sort of go down deeper and you know, it's impossible to like look at, you know, all of the factors looking forward, but you know, later on when you're able to look backward and kind of figure out what, what didn't work. I think very often realizing that you know, this might not be the best way to do it and they, and they kind of have to exist a sort of a better way to do. It is often valuable enough because it saves you going down the same rabbit hole again.

Jeff Bullas

00:07:45 - 00:07:58

So what you're almost saying is it's actually you learn more from the mistakes and what you should be doing because those disasters are revelations quite often, aren't they?

Tarush Aggarwal

00:08:00 - 00:08:19

I mean, I think in my experience it's been quite true, right? Like I think experience for the most part is knowing what not to do instead of really knowing what to do. It's like knowing what not to avoid. It's almost as I said, I think it's like almost more valuable than knowing what to do.

Jeff Bullas

00:08:19 - 00:08:46

Yeah, well you don't learn from pleasure, you only learn from pain.

So we're at, you're going to Salesforce, tell us a little bit about Salesforce and then we'll move on to the next part of your journey, which is that WeWork. So you started doing data analysis, Salesforce, what was that like?

Tarush Aggarwal

00:08:47 - 00:10:36

I mean, it, you know, at that point we didn't really have a data team materials for us, right? And like obviously Salesforce was still a massive, massive company with 10,000 employees and you had all of these different products inside Salesforce, like the seals, cloud and the services cloud and unlike the marketing suite and the help desk and all of these different tools and, you know, one thing which sort of Salesforce is trying to figure out is how our different customers using Salesforce and you think about it, you know, back then there wasn't a very trivial problem to go figure out. It's hard to segment and users into an episode of different categories for every different type of user, how they're using Salesforce. So, you know what, what sort we focus on is it's actually analyzing log data. So logs were, was sort of what was sort of storing exactly what a customer did inside Salesforce, you know, all of these log files would be shipped over from these application servers into a sort of central hub. We basically build passes for that. We used to go do this and extract metrics from logs and then we'll sort of later be able to analyze these metrics and sort of figure out benchmarks. So customers in this industry and this size on average are using XYZ features, and that became in sort of incredibly helpful for all of the different product owners at Salesforce to go figure out, hey, what's working, what's not working? You know, where can we add some personalization, some sort of customization and then which kind of features need to be rethought. So this sort of later became the product analytics team at Salesforce. And I'm sure that team today has got hundreds if not thousands of engineers.

Jeff Bullas

00:10:37 - 00:11:15

Right. I also discovered that initially when you're trying to capture data, it's rather messy. You're trying to work out. So where did this customer come from if you're talking to sales and marketing role because you've got so many inbound types of sources of data that it's hard to tell where it came from. And the other challenge for us as entrepreneurs is that where was the first click? And where was the last click?

Tarush Aggarwal

00:11:16 - 00:11:16

Yeah.

Jeff Bullas

00:11:18 - 00:11:26

So what do you think about that? And the challenges. So what you're doing is almost starting with collecting data and it's messy.

Tarush Aggarwal

00:11:27 - 00:14:13

Yeah. So, you know, it's really interesting, right? Like the average business today has got 10-12 different sources of data, right. So, you know, your average small business today, it's got a sort of backend database, they might have a CRM, they've had the software, they're using Facebook and Google and LinkedIn, they might have some, you know, online offline stuff for sure. They have tons of Google Sheets and you know, they might be using Xero or you know, or Stripe or Square or some sort of financial tool, right. So it's a lot of different data sources and if you try and get value from these data sources in an ad help manner, but exporting a data set from one tool to sort of to another by trying to maybe imported inside Salesforce or sort of trying to build something or like trying to build something dashboards based on what we have, then it gets very, very complicated very quickly and all of a sudden it's really important what the first click is and what the last click is and you know where is this data coming for, you know, how messy this data. So, you know, in that paradigm of the world we add so many layers of like complexity and the fundamental reason for this complexity is we have data coming from different data sources and the data from different data sources, it's got different structures and it's modeled inside a different way and it's not modeled in a way to answer business questions. So you know what we're in the middle of right now. It is one of the biggest movements on data. It's called the modern data stack. And you know what the modern data stack is a set of technologies when or a set of tools when worked or when sort of joined together, it becomes incredibly easy and powerful to be able to you know, build self service analytics, build answer to all of these sort of different, to basically answer all of these sort of questions and kind of build recommendations and insights.

And the fundamental premise of the modern data stack is being able to extract data from all your different tools from like your 10-12 different data sources in an automatic manner and ingested into a central warehouse and be able to analyze data over there. And when you're able to really do that and have all of this data going into real time and then build the analysis later on as a transformational step on top of the raw data becomes really easy to, you know, build analysis in a very sort of automated scalable manner. And that's really kind of where the industry is going.

Jeff Bullas

00:14:14 - 00:14:27

Okay so you're, let me try and sum that up, #1: Just get the data first and the #2 makes sense of us, make sense of it next.

Tarush Aggarwal

00:14:29 - 00:15:36

Yeah I mean it's not going on the data first because if you're, you know, extracting data from like 12 different sources and you're doing it in a sort of manual manner, you know, it's really difficult to kind of build on top of, right. So it's in some ways, it's like building a sort of skyscraper and you know if you're trying to build a skyscraper you need to go dig up the earth and build some foundation. You can't just start to, you know, build on top of and I think the biggest mistake companies make today is they is they focus on answering questions up front, which without building a sort of foundation and that foundation is more about automation, you know, is being able to ingest these data sources automatically to then build our reporting on top of that. But if you try to just go build out ad hoc analysis that's like, you know, having cement and sort of concrete and sort of building up and down, you know, very soon You might add 2 or 3 layers, but you're not going to be able to go build a skyscraper.

Jeff Bullas

00:15:37 - 00:15:42

So what you need to do is need to build a strong foundation and framework first, is that correct?

Tarush Aggarwal

00:15:42 - 00:16:36

Exactly. So you know, you might have heard of, you know vendors like Snowflake and sort of Tableau, which are you know, a few of like the data vendors, what sort of snowflake. Snowflake was the largest IPO in tech history and it happened to be a data company, but in the modern data stack movement, it's just one layer, it's you know, one of like five layers, you need to go bare your first analysis. So you know the sort of layers of data collection ingestion storage, which is what Snowflake is on top of that, you have data modeling on top of that, you have reporting. So there are five different layers and really what I call this is the sort of foundation, if you don't have, you know, these very very foundational layers that's like trying to build a skyscraper without building without digging up the earth and building foundation.

Jeff Bullas

00:16:37 - 00:16:54

So you're a Salesforce and you obviously learned a lot there, what was the biggest insight you had at Salesforce before we move on and talk about WeWork?

Tarush Aggarwal

00:16:55 - 00:17:33

I mean at Salesforce I think we're just getting interesting because you know, I think by the end of it we sort of, you know, by the end, you know, I joined Salesforce as a software engineer and I sort of ended my stint at Salesforce as a data engineer and that was you know, the very infancy of this new title of data engineers and data scientists. So I think by the end of Salesforce, we knew that this sort of data thing is sort of going somewhere and all of a sudden it became really interesting to see what's going to happen over the next few years.

Jeff Bullas

00:17:34 - 00:17:43

Yes, so what you're doing is you're noticing a momentum, a data momentum.

Tarush Aggarwal

00:17:44 - 00:17:45

Exactly.

Jeff Bullas

00:17:45 - 00:17:51

So from that you made a move then to WeWork, how did that happen?

Tarush Aggarwal

00:17:53 - 00:19:03

In between there was a smaller company I was part of, it happened because you know, a part of me also wanted to live in New York and all my friends were living there and I decided to go live in New York for a few years and just as I was getting ready to move back into San Francisco, obviously WeWork magically sort of showed up and you know, I still remember walking into the office for the first time and just feeding this, you know, intense energy, which was like nothing I'd ever felt before inside corporate Silicon Valley or or sort of corporate New York. And I knew right then and there that there was something special. At that time we were, you know, had a very very small data team, there was, you know, 2-3 person data team and you know, I think for me at that point I sort of, was just very on the same page on the mission sort of, WeWork with sort of up to and you know, I sort of decided very soon that that's what I was interested in doing.

Jeff Bullas

00:19:04 - 00:19:55

Right. So you started WeWork because WeWork is an interesting company in that it's renting an office and then sub-renting it out to independent freelancers, entrepreneurs, startups and so on. WeWork had an interesting journey in terms of huge, but then blew up as well. So we involved in sort of that you felt the energy which is true about startups, it's like there's this energy, it's all working, it's all pumping, it's growing, it's going fast, so how long are you in WeWork and what did you learn from WeWork?

Tarush Aggarwal

00:19:56 - 00:21:27

I worked for WeWork for over 3.5 years. It was one of the most exciting journeys to be part of something which is being built so quickly and I think, you know, very, very, I'm extremely grateful for my time over there. I met some incredible people who are still advisors, friends, mentors till today, so nothing but nice things to say about my time over there. And you know, I think a lot of my learnings over there came from really sort of learning how to scale things up in a sort of extremely fast manner. And also, you know, I think data challenges were really interesting because, you know, it was one of the first few companies which had both online and offline data sources, you know, I sort of mentioned that the average startup today has got between 10 to 12 different sources of data and in WeWork, we had 100 and we sort of that 100 pretty quickly, right. So you know, being able to, you know, build out data capabilities at a company of that size was a nontrivial task and the sort of learn and the sort of learnings that came along with it, we're also you know, pretty fundamental in sort of how 5x looks at the world and, you know, how we wanna how we want to build and scale products.

Jeff Bullas

00:21:28 - 00:21:42

Okay, so you went from a software engineer to a data engineer at Salesforce because you realized that being a software engineer just was something you hated doing, but discovered something that you love doing, which is data engineering, is that correct?

Tarush Aggarwal

00:21:41 - 00:22:03

Yeah, I think you know, I think you know, I've worn many hats in my career in the data world, but the one I did start with was sort of data engineering.

Jeff Bullas

00:22:03 - 00:22:29

Okay, so WeWork and obviously an idea or an opportunity came up to start and do your own thing, was that an aha moment for you? Like I've been doing this for other people, it's time to do it for myself and you came up with an idea how to do that. How did that happen?

Tarush Aggarwal

00:22:30 - 00:25:25

It didn't come up and it didn't come up anything like that. I, you know, towards my end of my WeWork, we moved to China with WeWork to go focus on WeWork’s platform efforts in China. What sort of China is is a completely different animal. If you want to be successful in China you have to build, you know, you have to build on the China cloud, Amazon and Google aren't really very prevalent in China. So you have to build them like Alibaba cloud and you have to rebuild things in a certain different way and WeWork was very very interested in the China market in a different context. WeWork only entered cities with more than a million people.

America had 14,China had a 108. So that's how big the opportunity was in China for WeWork and you know as they were getting started with this, you know, WeWork I feel sorry to fail and you know when that happened a lot of the leaders were sort of called back to America. At that point I had no interest in sort of moving back to America. So I decided to go on a 10-day vacation to Bali and that happened to be there one of Covid and you know as soon as I get to Bali very very quickly the sort of borders locked up, I can't go back to China and I'm there with a small handbag and you know, I sort of proceeded to spend two years over there. I sort of, you know, help with some transitions that WeWork but wind myself off the business, I took a few months off and you know It was a very creative time in my life where I got to double down on a lot of like personal pursuits which I just hadn't had the time to do sort of spending 10 years in Silicon Valley and in New York. And you know this journey ultimately led me to form 5X, which really came from you know, it really came from the fact that in the next 5 or 10 years, every company is going to be a lot more data driven, right. Just like 10 years ago, no one was doing digital marketing, today every company is doing it. In the next 5 to 10 years every company is going to be on the modern data stack and it's gonna be a lot more data driven. And unfortunately 90% of businesses are not going to hire 50 data engineers like Salesforce and WeWork. But 90% of businesses need to be data driven. So I basically started 5x to help 90% of businesses have the same insights and recommendations and analytics as the companies that WeWork and Salesforce in a much more effective and self-scalable manner.

Jeff Bullas

00:25:26 - 00:25:51

Okay so what you're saying is that 5x can help companies outsource their data analytics and collection because they won't be able to afford to have 15, 20 data engineers. So you're helping companies get insights quickly about data without actually having to hire data engineers but outsource that expertise to you guys, is that correct?

Tarush Aggarwal

00:25:53 - 00:28:13

So you know what fundamentally 5x is we are a sort of platform where, you know, the most important thing is if you want to get it, if you do want to get, you know, if you want to get any insights from your data as I said you need to set up. You first need to have the foundation and you know if a business wants to go do it today they need to go to each of these five vendors. Each of them is a sort of billion dollar vendor right? And you have to go sign an enterprise contract and you have to go stitch it together and that takes months and you know you now have these multiple enterprise contracts you have to manage.

5x to the first company which like stitches together the modern data stack and sort of gives it to you as a service. So on day one, you have all of the different vendors, you don't have to worry about enterprise contract, you get one monthly bill based on how much you use. So you know on day one itself you have that entire foundation which was, which I was mentioning now you can choose to go operate this yourself and you know, sort of now you have the foundation, you can choose to build the analysis on top of this if you want and a lot of companies do that but if you don't have that expertise, what we also do is we give access to really smart or really smart qualified engineers. So at the moment, we probably interview about a thousand engineers a week in nine countries, we can go higher. The top 0.2% of these engineers, we can then pre-train them on the platform because we know exactly what the platform is and now we have some extremely sort of qualified engineers. What we can do is give you access to them and you can embed these engineers into your teams and you can build out analytics, dashboards, reporting insights. So we don't actually, you aren't outsourcing any work to 5x. You're using this sort of 5x platform to number one build your foundations and then if you need to gain access to basically high quality talent, does that make sense?

Jeff Bullas

00:28:14 - 00:28:21

Okay, so the next thing I ask you, can you sum up what 5x does in one sentence?

Tarush Aggarwal

00:28:22 - 00:28:32

Yes. Absolutely. So 5x is an end to end data platform built on top of the best in class vendors on the modern data stack.

Jeff Bullas

00:28:34 - 00:28:36

Okay. And how do you help businesses?

Tarush Aggarwal

00:28:38 - 00:28:47

Businesses using 5x can implement entire data strategies in weeks, not years.

Jeff Bullas

00:28:47 - 00:28:56

Right. So they can use you guys to actually make sense of data about their market that helps them actually make more money, is that correct?

Tarush Aggarwal

00:28:57 - 00:28:59

Absolutely.

Jeff Bullas

00:29:00 - 00:29:04

Cool. So how long has 5x been running now?

Tarush Aggarwal

00:29:05 - 00:29:07

We're nine months old.

Jeff Bullas

00:29:12 - 00:29:20

And how many like, is this a sub-monthly subscription product? How does that work?

Tarush Aggarwal

00:29:21 - 00:31:17

Yes. So you know, so there's two pieces outside. That's right. First of them, the first piece is the platform which is all of these different tools needed in order to get to insights. The way that works is you know, you pay as you go. So you know at the end of the month, if you've used tool, you know, if you've consumed the sort of 100 credits of a tool, you gotta bill off 100 credits, right. So it's the same thing. There's no, instead of signing an enterprise contract, you just pay as you go. So you know this sort of best way to think about it is for an early stage business, you know, your monthly bill, if you don't have any absurd amount of data, should be a few 100 bucks a month just depending on how much you used. So that's the first piece of it. And the second piece is the access to talent piece. Now, you know in America or even in parts of Australia. Talent is really expensive by the a sort of full time data engineer costs over $150,000 if you have to really get to analytics or insights, you know, you're sort of looking at two engineers six months to one years, you know, the average entry point into doing data today is pretty close to half a million dollars. Whereas in 5x, you know, you can do this in weeks at a much much lower cost. So you know our talent what we call these engineering boards which is access to a sort of group of engineers starts at about $5000-6000 a month, which means you know, you can implement an end to end data strategy in, you know, in sort of 50 to 100 grand, which is, you know, 56 times cheaper than sort of getting started from scratch. And it's like at least 10 times faster than if you had to go do it yourself.

Jeff Bullas

00:31:18 - 00:31:26

So what's your, I suppose, sweet spot as a target company?

Tarush Aggarwal

00:31:27 - 00:32:14

So you know, today our smallest companies are companies that are pre-product, so they haven't even launched a product, they have no data but they know that when they do launch they are very, very interested in it. And our largest customers are banks, which are like public companies, right. So we have, so we have clients all across the board. You know, I would say that was sweet spot is really, you know, your mid-market sort of companies that, you know, are growing very quickly the want to move and scale extremely quickly and they know that one of the core always they're gonna be able to do that is through, is by having the right sort of data strategies.

Jeff Bullas

00:32:15 - 00:32:16

Right, okay.

Tarush Aggarwal

00:32:17 - 00:32:33

So you know, put another way, it's no longer companies, it's not companies who haven't figured out 0-1. But when you have the product market fit and now when you're looking to accelerate growth, that's when 5x is extremely relevant.

Jeff Bullas

00:32:33 - 00:32:48

In other words, you're not a startup, you're more a someone who has already worked out what their customer is, already getting traction now just make more sense of the data that your customers are giving you.

Tarush Aggarwal

00:32:49 - 00:33:45

In some ways like all of these tactics, right. Like, you know, like even like ads, right, a lot of early stage companies are sort of focusing on ads and the thing about ads, ads is like putting, it's like putting gasoline on a fire, you know, if you do that, it's gonna, it's gonna accelerate but you need to have a fire first, right. And in the same way we did it like, you know, all of these things are extremely relevant once, you know, that you have product market fit that you know you are that you have an actual business and you have figured it out once you kind of have that and you use, you know, sort of and you're able to use data and analytics to accelerate your growth. That's when things become sort of really powerful. So let’s have a look at it.

Jeff Bullas

00:33:46 - 00:34:08

So what's the future for 5x you’re thinking? Nine months in, how many customers do you have? You don't have to tell me that. But where do you see yourselves going from here? Is it do more enterprise style data? How's it look for you?

Tarush Aggarwal

00:34:09 - 00:35:45

So, you know, we are very, very interested in becoming the single platform across the modern data stack, right. So we know that the modern data stack movement has become mainstream. Every company is going to have, you know, a sort of version of the stack. In the next few years, we want to be that single platform across the modern data stack which allows you to, you know, implement this inside your business. So you know in the next few months anyone will be able to go to 5x, enter the old credit card and you have this sort of modern data stack up and running your own version of it with one monthly bill.

So you know, we’ll become 100% self-service which means that, you know, we will be able to have hundreds of thousands of sort of businesses use us at a monthly level. And you know in terms of where we want to go as you kind of said, you know, we in some ways want to be the Google Analytics of the modern data stack and this sort of reason we say Google Analytics is that, you know, in the old world, if you had a website you would probably have google analytics. It sort of helps you figure out where your customers are coming from, how much time they're spending, you know, which features are they using? Which features are they not using in the new world where you have multiple different data sources. Google Analytics is not, you know, as I started relevant as it once used to be. We want to be the sort of next Google Analytics built for the modern data stack.

Jeff Bullas

00:35:46 - 00:36:00

Okay so let me ask you a question about that then is providing analytics, are you helping people actually make sense of the analytics that actually can make more money?

Tarush Aggarwal

00:36:01 - 00:36:52

Yeah, absolutely. So you know no one is doing data for the sake of doing data and actually getting analytics right, it's always as a means to optimize the business. Now you know today this sort of sad truth is that even if a company has this half a million dollars and goes and signs all these different layers of the stack and how is the higher the sort of data engineers chances are that these companies don't speak data engineering and these data engineers don't speak company. So there's a translation gap over there now, you know how we solve this is we have this layer on top of just the engineers and we call this our project management layer and think of these people as you know early employees of companies like Uber and WeWork and Salesforce and Spotify where they've really been at the early teams, they've seen these companies grow, they know how to talk to executives, they know how to translate generic business requirements into insights and recommendations. And you know when you, you know, when you decide that if you need help with this sort of talent piece of it, you get access to these people and the way it works is you have these one week sprints where, you know, WeWork in one week sprints yours what we did, just what we will do, here's what we need from you and these people are sort of there to, you know, in some ways act like that translator, be able to take your generic requirements and convert them into actionable insights which the business can then implement.

Jeff Bullas

00:37:39 - 00:37:49

Because at the end of the day, the entrepreneur wants to have actual insights that help them actually get more leads.

Tarush Aggarwal

00:37:49 - 00:38:14

All companies care about is sort of three main things. Number one is, how do we get more customers? Number two is, how do we keep these customers engaged? So how do we decrease shunt? And number three is, how do we optimize the business or how do we decrease the cost? There's the, you know, the three broad areas which sort of companies sort of care about and those three areas are sort of what we start with.

Jeff Bullas

00:38:14 - 00:38:20

So, 5x uses data to meet all those three areas.

Tarush Aggarwal

00:38:21 - 00:38:22

Yes.

Jeff Bullas

00:38:22 - 00:38:27

Cool, that sounds like something worth doing. So just to better wrap up, what's one or two top tips you would like to share? Actually I want to ask you something else, frankly, we talked, initial discussions and what we shared was almost every company is becoming a data company now.

Tarush Aggarwal

00:38:52 - 00:38:52

Yeah.

Jeff Bullas

00:38:54 - 00:39:37

Because you can sell data going, okay I have a database of 10,000 customers. They are all 25 - 30 years old or these are my top three demographics. So before we get to sharing your top tips, how can a company become a data company in the modern world, we used to talk about being a social media company, which is actually less important now, to become a data company and collect the right data and use that data to grow our business.

Tarush Aggarwal

00:39:38 - 00:43:01

Yeah, I mean, so the sort of reality of it is, you know, this does not happen overnight, right? Like you want, you can't decide I want to do this and sort of tomorrow you're going to be a data company. It takes a few iterations, right? And you know, if five years ago, very few companies were doing digital marketing, right. Like maybe, like 10 years ago, no one had a website and Google came along and told you that if you don't have a website, you won't exist and you're some from now and like today, that's so true. If you don't have a website, if you don't care about your online branding, you probably don't exist. Now in the next five or 10 years, used cases like where are your customers coming from, where’s your different segments of users? What's your average lifetime value? How do you personalize your app for your different segments? How do you use and licks just to sort of figure out who your best sales reps are. How do you use this to go figure out supply chain? These are the types of used cases. If you don't get very good in the next five years, you just won't be able to compete because your competition will be doing this. So you know the way we look at it is and sort of going back to answer your question, what's the biggest tip? The biggest tip is go get started because it's, it's not gonna happen overnight and the whole mindset of, you know, a sort of founder thinking, just sitting on top of a sort of gold mine of data and they're just waiting to hire one data scientists to go mine this data and sort of companies amazing insights, that's not how it really works. Like you know, you can't, you know, you might be sitting on an amazing plot, an amazing amazing crowd of land, but you are a few years away from like activating and like building a skyscraper and you know, if you don't get started right now, you're just gonna be left behind because you know, we can see this, we just have enough data to sort of see this right now that, you know, there are enough companies where sort of data might not be caught to their business and I'm talking about like real estate companies or like education companies only, cryptocurrencies or like, you know, you name it like that, you know, we are sort of engaging with all of them and you know, they're extremely interested in not playing a sort of two months game, but they're interested in playing what happens in the next six months and what can you do, which, you know, allows us to execute our competition.

So, you know, I think the number one kind of tip is, is go get started because if you don't, that's, you know,

an extinction level event coming up. And I think number two is this idea of like, I'm going to go hire a sort of analysts and go build some like a dashboard and that's kind of enough. That's like, you know, using that analogy that I want to do marketing and it's enough to go, you know, build an Instagram account and make some posts, right. This sort of reality of it is like that time when you can do marketing with a few Instagram post is sort of long gone. And the same analogy that you can go hire a sort of analysts and build some dashboards and that's going to make your data driven, it's probably is also long gone,

Jeff Bullas

00:43:02 - 00:43:04

In other words, play the long game and then use a service like 5x to do it for you rather than just wasting money on just one freelancer or data engineer that's going to cost a lot of money, which and they won't have the expertise either across the board. So you've got this expertise across the board, right, that allows you to provide a whole range of insights that will help companies grow and scale.

Tarush Aggarwal

00:43:37 - 00:43:59

Yeah, I mean you don't have to use 5x but you know if you're interested in sort of chatting, we would love to chat. But I think just getting started in data is if there's anything you're probably gonna take away from this, it would be like go get started and hiring an analyst is like not the right way of getting started.

Jeff Bullas

00:44:00 - 00:44:20

So let's get to the number then. I totally agree with you. Let's get started. Yeah. And I've just launched a new product and we're collecting data but it's a bit messy at the moment. So how do you get started? What should you be doing? What’s the fundamentals of starting to collect data?

Tarush Aggarwal

00:44:21 - 00:46:36

Yeah. So you know, I would look at it like phase one and phase two, right. In the first phase, it's really how do we get to this concept of self service analytics such that you know, anyone in the company which has got a reasonable question can answer this themselves in a few clicks. So what kind of needs to happen for? So it's sort of self service analytics, number one, you know, as I said only we have like 10 to 12 different data sources. How do we in an automated manner injustice data from the standard 12 different data sources into a central warehouse? How do we then model its structure and clean it and build a clean business layer which is structured in a way to answer business questions. And then how do we have some sort of BI tools on top of it which allows us to like slice, dice, answer these questions in a self service manner, right. Kind of that's really like phase one. Right. So initially your core metrics around what percentage of important questions do I have, which I can answer myself without waiting on like one week for, you know, an analyst or someone on the data team to answer that phase one. Now when you have that, now you know you sort of get to like play around with more advanced capabilities. How do I take this data and kind of make decisions on it. Can you know, can we have sort of intelligence to actually tell us which cities to focus on and sort of what price point should we go after and which campaigns to avoid. Can we take this data and push it back into other tools? Like push it back into our sort of marketing automation tools or take some of these intestines, sort of push it back inside Salesforce or or sort of can we use some of these insights which we're getting to build customer dashboard so that our customers get better insights into how they're using our product. Actually get sort of recommendations on top of that. So you know, there are like 5, 10 different, you know, areas which you can kind of focus on but it really starts off with, you know, the first kind of phase is self service analytics built inside an automated manner and from there things get really interesting.

Jeff Bullas

00:46:37 - 00:46:45

And this is something to, a lot of companies are struggling with is how do I build, how do I use analytics in a very clever and efficient way?

Tarush Aggarwal

00:46:47 - 00:47:16

Yep. I mean I think, you know, what I kind of just sort of gave you is a very kind of actionable way of doing it right. It's sort of focus on what your ultimate end goal is and for phase one, your end goal is self service analytics which is, you know, pretty actionable insight instead of, you know, talking about, hey do I need this one tool or you know, do we need a warehouse or ingestion tool? The goal is you know, something pretty actionable. And I think self service analytics or sort of self service reporting is your first actionable phase.

Jeff Bullas

00:47:17 - 00:47:18

And the second one again? The second phase.

Tarush Aggarwal

00:47:22 - 00:48:29

A lot of things get more, it's a challenge, right. So then it's kind of the business where you know, it's something like, you know, you know, be able to push intelligence into other different tools.

The chance we can push these intelligence into sort of other tools, other areas like managing your sort of lineage and what's gonna happen is you're gonna have hundreds and hundreds and hundreds of sort of data sets and you know, are you kind of able to figure out which ones are the ones, which are important, which ones are joined from others? What if one dog fails, what's the upstream and downstream dependencies? You know, increasingly increasingly more and more stuff around AI and sort of ML which you know, are you able to predict, you know inside and sort of recommendations ahead of time? So you know, the downstream dependent on all these sort of, you know, once you have the core analytics, what you can start doing after that, you know, that's just becoming broader and broader every day with all of these new categories coming.

Jeff Bullas

00:48:30 - 00:48:35

So what you're really saying is start with the right foundation fundamentals?

Tarush Aggarwal

00:48:36 - 00:48:36

Yeah.

Jeff Bullas

00:48:37 - 00:48:46

Which is what you guys provide as a service and then play the long game and then use that to actually grow and scale a company.

Tarush Aggarwal

00:48:47 - 00:48:48

Absolutely.

Jeff Bullas

00:48:49 - 00:49:29

Cool. Tarush, it’s been an absolute, fabulous experience to hear your insights. A lot of what we've talked about today is going to blow most people's minds and will slightly confuse them maybe. Because a lot of companies are still trying to escape analog and trying to get into digital. And people are still struggling with social media, you know, 13, 14 years later. Thank you very much for sharing your knowledge. It's great to see you launching 5x and I look forward to catching up with you maybe in Bali one day.

Tarush Aggarwal

00:49:30 - 00:49:35

I would love that, Jeff. Thank you so much for having me on the show and hopefully we were able to add some value to your listeners.

Jeff Bullas

00:49:36 - 00:49:40

I'm sure we will. And how do they find you in Tarush?

Tarush Aggarwal

00:49:41 - 00:49:57

Our website is 5x.co. Again, that's 5x.co. You can reach out to us at [email protected] You can reach out directly to me at, at [email protected]

Jeff Bullas

00:49:58 - 00:50:02

Thanks Tarush and it's been an absolute pleasure.

Tarush Aggarwal

00:50:04 - 00:50:06

Thank you so much. Thanks for having me on the show.