Podcasts > Ep. 117 - Data infrastructure as a managed service
Ep. 117
Data infrastructure as a managed service
Tarush Aggarwal, CEO, 5X
Friday, March 04, 2022

In this episode, we discuss how traditional companies can turn their access to unique data sets into a competitive advantage. We also explore the challenges that companies face in setting up a data factory, from high risk strategic IT investments to the lack of technical talent.  

Our guest today is Tarush Aggarwal, CEO of 5X. 5X offers data reporting as a managed service on the latest technology platforms, so users can make data-driven decisions faster and adapt their architecture on demand.

IoT ONE is an IoT focused research and advisory firm. We provide research to enable you to grow in the digital age. Our services include market research, competitor information, customer research, market entry, partner scouting, and innovation programs. For more information, please visit iotone.com


Erik: Welcome to the Industrial IoT Spotlight, your number one spot for insight from industrial IoT thought leaders who are transforming businesses today with your host, Erik Walenza.

Welcome back to the Industrial IoT Spotlight podcast. I'm your host, Erik Walenza, CEO of IoT ONE, the consultancy that helps companies create value from data to accelerate growth. And our guest today is Tarush Aggarwal, CEO of 5x. 5x offers data reporting as a managed service on the latest technology platforms, so users can make data driven decisions faster and adapt their architecture on demand.

In this talk, we discussed how traditional companies can turn their access to unique data sets into a competitive advantage. We also explored the challenges that companies face in setting up a data factory from high risk strategic IT investments to the lack of technical talent.

If you find these conversations valuable, please leave us a comment and a five-star review. And if you'd like to share your company's story or recommend a speaker, please email us at team@IoTone.com. Finally, if you have an IoT research strategy or training initiative that you'd like to discuss, you can email me directly at erik.walenza@IoTone.com. Thank you. Tarush, thank you so much for joining us today.

Tarush: Hey, Erik, thanks so much for having me on the show. I'm really looking forward to chatting more and hopefully adding some value to your listeners.

Erik: And so maybe a good place for us to start would be before we talk too much about 5x to get into your background a little bit. When did you first touch the broader topic of how do you generate value from data?

Tarush: Yeah, just in terms of my background or in terms of?

Erik: Because just looking at your LinkedIn, it looks like this was maybe a decade ago. But really even an interesting early start to your career, I think you were playing around in a few different playgrounds?

Tarush: I've been trained as a software engineer, did my undergrad in America, left home at 17, and ran away from India. And I was super fortunate to get a job in Silicon Valley right out of college as a software engineer at Salesforce. Back in 2010-2011, we're just coming out of out of a recession, data really wasn't a thing. No one in Silicon Valley at that point had a data team. And it was fortunate to like figure out very early on that software engineering wasn't quite the right fit for me and was in some places the right place at the right time, and got to jumpstart a lot of the data initiatives at Salesforce and got to be one of the early data engineers over there. Since then, I've never really looked back. I've just been very niche-focused on the data landscape, and have just got to grow my skills along with the industry as it's become a more mainstream offering inside the entire development landscape.

Erik: I've got to imagine that Salesforce is one of the first SaaS companies to really understand the value of data. But when you arrived, was your sense that they were more let's refine the processes or that they already had a very strong feeling for the value of the data that they were?

Tarush: I think Salesforce was probably the first SaaS company, period; like, they kind of invented Software as a Service. By the very nature, they were thinking about problems back then before how the people in the industry really were. The way they function was, every customer was very independent. They use a multitenant architecture, but each customer was so segregated from the next customer.

And really what came very organically was this need to basically start to understand what are customers doing in a very, very anonymous way so that we can start to do things like benchmarking? How many leads does a real estate business at a million dollar revenue on average have that's super valuable for other real estate companies to know whether are they tracking up are they tracking down?

The way it was done was very ad hoc manual, not automated. And a lot of only data back in 2010-2011, the use cases we will focus on is understanding engagement for different customers through log data, being able to extract metrics from that and then putting it into a distributed framework to then be able to generate benchmarks and engagement metrics.

Erik: And then you moved from there to a company called Wyng?

Tarush: Wyng, yeah. I guess the highlight was after two years of being extremely technical, I decided to spend some time more at a high level, moved to New York and naturally started taking some of what we're learn at a very technical level at a company like Salesforce, which is probably ahead of the curve and start to apply it to a gender and mid-market business, and really focused on applying those skills to a much smaller company; rarely very often at a big company like Salesforce, very focused on a very, very small part of the business. And basically, my New York chapter with Wyng and most recently with WeWork was an opportunity of applying some of those principles to like hyper growing businesses.

Erik: And I've got to imagine that your experience at WeWork helped to formulate the philosophy around the business that you're running today, because WeWork is very much an end user with a lot of interesting data use cases, I imagine there was a lot of room for creativity there?

Tarush: Yeah. What started to happen is, saw this over and over again, and at companies like Salesforce and WeWork, we were fortunate enough to go 100% data teams. What I witness is that these use cases, how does a company use data for go-to market or understanding how customers are using the product, optimizing internal operations; these use cases are something which every company is now focused on. And not every company is going to have the sort of privilege of deploying an army of engineers to go fix it. So really, where 5x came from this idea of how do we make it very, very easy for the next wave of businesses to be able to take part in the same variety of infrastructure and platform and to be able to leverage data in the same way as companies as Salesforce and WeWork were able to do it.

Erik: So on the one hand, this is a very horizontal problem. Every company in the world can somehow find a data set that could be useful for their business. So if you're looking at 5x, who are you focused on you? Are you supporting SaaS companies? Are you supporting brick and mortar companies?

Tarush: As you kind of mentioned, it’s extremely horizontal problem. Every company in the world needs to figure out how to capitalize on the data. The high level use cases which any founders thinking about is what's my go-to market strategy? Where am I customers coming from? Now, if you’re a B2B business, you're looking at it from different channels. If you're a consumer business, you're looking at it in different channels. But fundamentally, you are approaching the same sort of larger problems. Now, it doesn't matter if you're in real estate, if you're in SaaS, if you're in manufacturing, again, it kind of changes where your data is coming from, and how you analyze it and what are your metrics.

But on a broad level, go-to market strategy is applicable for all businesses. What's our go-to market strategy focuses on is how do we get more business? How to get more customers? How are they using our product? Or how do we optimizes? How do we keep engagement up? How do we keep these customers? How do we prevent them from churning? Optimizing internal operations, whether it's manufacturing or finances or sales is all about how do decrease our bottom line burn? So at a broad level, how do we get more revenue, how do we decrease our burn, and how do we make sure we keep our revenue, is something every business in the world focuses on. And then as you get more granular, it changes per industry, it changes depending on the size of the company.

Erik: You guys are just today about 18 months, oh, there's a company roughly, right? So, you're probably not covering the full scope of the potential customers that you might have in the future. But who would you be focused on right now? What are the first problems that you think are the best fit?

Tarush: We're actually even younger than that because we pivoted maybe a year ago. So, in our current business model, we’re even younger. We're super lucky that today our largest customers are large public companies, Australia's largest liquor company. We're in talks with one of the largest burger manufacturers in the world. We have banks using us today. And smallest companies are pre-product: they just see the value of data and they know that when they do want to launch, they want to be extremely data driven.

So, for the majority, we're all across the board, all across different industries and SaaS, banking, manufacturing, real estate. What we're really focusing on is the mid-market tier where you have a business, it's pretty well established, they found success and now they're really interested in doubling down on a data strategy. They want to scale the business to a more enterprise business data that’s going to be a very, very large component.

And when you outgrow A, we hired a couple of analysts, and we're running some reports into more like the WeWork’s and Salesforce’s where we want to have thousands of employees or hundreds of employees across the company using data in a self-service way being able to be a step ahead and really building that sort of large scale engine in a way which is just a lot faster, predictable, cheaper than anything that's out there. That's really where we're extremely focused on. But just in terms of audience, we're really focused on helping companies who have already proven that they have a business and found some success take things to the next level.

Erik: Without getting too much into technical detail, maybe we can cover a little bit how you address this. Of course, there's a lot of platforms on the market that are selling technical solutions. My sense is that's not exactly what you are. So how would you define your business?

Tarush: So if you zoom out and you look at the space, there's two pieces to it, but one of them is the platform. But what's the infrastructure which we're going to use to basically get value from data? And the second part is always people. Like, who are the people who are the ones who are going to be operating this and integrate into the business? Any strategy is always a combination of product or platform plus people.

So let's talk about the data space or what you call the modern data stack today. If you look at that space, it's become super competitive. There was a company called Snowflake IPO last year, which is the biggest IPO in tech history. Snowflake is a storage layer inside the data space. It's your data warehouse layer where you store your information. If you look at a space in general, it's extremely fragmented; you have multiple layers in the data space. You have data collection, you have data ingestion, storage, modeling, reporting, AB testing, machine learning, decision making. Some of the new layers now or data catalog and metadata, reverse ETL.

So you have all of these different layers. You probably have a billion dollar company for each of these layers today. So, it's extremely fragmented. Like Amazon, and Google Cloud, they might have a few products, but the trend is today, people want to use the best in class product for a particular category.

Like just think about it in the consumer world, we want to use Zoom for video and we want to use Slack for messaging and we want to use Google Apps only for email. All of the offerings can do all. Google has got a product for messaging, and Microsoft has got a product. All of the platforms can do everything. But we still want to have the best in class provider for one small use case. And that's very, very similar inside the data space where we want to use Snowflake as a warehouse, but we want to use reporting with a tool like looker and like DVTs becoming the de facto modeling.

So, if you just want to get started, you got to go sign five enterprise contracts with these vendors, you need to go see how this stitch together and operate this and maintain this yourself. 5x is the first company which we provide the modern data stack as a service. So, we've gone ahead and have all of these back end deals with all of these companies. You can kind of pick and choose your stack, and you push a button, and you get the modern data stack on day one. You don't need to sign enterprise contracts. You basically get a monthly bill depending on how much you use. And you don't need to worry about how to deploy it and stitch it together. It's all done for you. So on day one, you've saved yourself that data platform team figuring out those enterprise contracts and you get the platform.

And the second piece, which we were talking about which is the talent piece, because we now know what the platform is and we know everything about the platform, today we interview 500-1,000 engineers per week, we get to hire the best 0.1%. And because we control the platform, we can pre-train these engineers on the platform. And I'll give you a marketplace, where you can not only get the platform but engineers who already know how to operate this platform and are trained in the modern data stack and get these engineers and embed them directly to your business.

So really, what this allows us to do is go into companies and deploy data strategies in the first 6-8 weeks, which typically takes companies 12 months plus with hiring their own engineers, figuring out the stack, stitching it together, getting started. So we're just able to do it so much faster, cheaper and higher quality than basically trying to start from scratch.

Erik: You’re basically building up a platform that might certainly leverage other technologies. But it sounds like you're basically almost a managed service provider around the tech stack.

Tarush: And in some ways, think of us as an Amazon Web Services, except the differences. We just go to the best vendors and add them onto the platform and figure out how they get stitched together. Like, a lot of companies have attempted to build all of this in-house. And what we're seeing as a general trend is people don't want to be tied into one platform, they want to use the best in class tools across different things.

And whereas on the other side, a typical consulting company, very different from us as well, because it doesn't matter even if you have a consulting company, which are experts on data, they still start from scratch for every customer, like set up everything from scratch. Whereas on 5x, enter credit card and you have a platform, in the first half an hour, you'll have the entire platform set up for you. So we've taken the entire stack and automated it such that this is a course that now which it's pretty widely accepted in the industry and it allows a lot of companies to bypass that long set up onboarding time period.

Erik: Every company that you're working with, especially the more mature ones, are going to have a lot of legacy. So they're coming in with a legacy set of infrastructure and platforms and SaaS and so forth. And so what is the engagement look like? Is it basically a consultation around where are you today where do you want to go, and then you help them to define the stacker?

Tarush: We're working with a public company in Australia, it’s Australia's largest liquor company. And if you think about legacy infrastructure, like looking at a very legacy industry, like alcohol retail in a place like Australia, not to say Australia's backwards in any way, but if you just look at the old school industries and places outside Silicon Valley, they tend to be a few years behind the curve.

So, being able to take a business like that, which has got 50 different data sources, all of which are extremely legacy, and be able to automatically ingest all of that into a modern warehouse like Snowflake, huge case study for us that if you can do for a business like that, chances are you can do it for any business. Again, a combination of the platform, some of the vendors we're using on ingestion are just the best in class vendors in the world, they integrate with hundreds of data sources out of the box.

But then also, the engineers who we embed into these businesses are extremely good at being able to take non-standard data sources and work with the platform and figure out the best ways to ingest this.

So to answer your question, at this point, it's less so about what are your use cases and how different your industry is. We just have the data now to show that it doesn't really matter what you're trying to do or what your use case is, being able to go there and take over a few different data sources from. Your average startup today's got 10-12 different sources of data, so your mid-market to enterprise companies. At WeWork, we had about 150-200 sources of data just given the fact that we were both physical and digital. So probably the higher end of the spectrum being able to take these different data sources and ingested in an automatic manner into a central storage or what you call the data warehouse. And that's really where the magic begins.

Because if you're able to do this in an automatic fashion and not manually have to deploy armies of people to ingest this data and you're able to do this in an automatic fairly at least once a day way, you now have all of your data sources in a central place. And that's when you can get really fast at being able to figure out what's actually happening in your business and how do you action some of your high level business that ever built.

Erik: Because of the nature of your business, you're selling to CIOs, is that right?

Tarush: Depends on the size of the company and the vertical. But in general, CMO, CFO, CIOs, or CTOs, and even CEOs that like on the smaller scale of mid-market, any executive who is unable to leverage data to really hit their business goals is a potential customer of us. And one of the values of us is if you go to a very technical business, if you go to Snowflake, for instance, and say, we have this problem, and they can give you part of the platform to go solve it. But you're still going to require engineers who need to figure out how this is going to work.

And one of the big problems is that very often, businesses don't speak data engineering and data engineers don't speak business. And there's a big disconnect over there, which is why a lot of these implementations costs hundreds of thousands of millions of dollars, and they take a year to go do. Because you need to figure out how to approach this problem from two people who don't speak the same language.

What 5x is solving is that guess what, data is not a technical thing, it's what every business needs to do. It's a very business-focused problem. In some ways, we're building the translator, making it very simple for businesses to go focus on the business value. And through what we've done with our platform and around standardizing so much of the sort of talent and how they work and how they're able to interface with businesses, we've kind of becoming that translation layer.

And one of our goals is how do we commoditize this and really make this mainstream? I'm not talking about the 10% of tech companies like WeWork and Salesforce, who are just going to naturally better deploy thousands of engineers. But how about the other 90% of businesses, which might have this together initially with a few analysts but really will struggle with taking this mainstream without spending millions of dollars?

Erik: So let's maybe take a little step back away from 5x and look at what your customers are doing before they work with you. What do you see a lot of companies doing wrong when it comes to data management today?

Tarush: It's all across the board, but happen to get into two or three different use cases. Let’s talk about this is Australian large liquor company we mentioned. We went over there and they're selling across multiple different channels, in different states in Australia, they have a direct to consumer business. Being able to just have visibility into what are they selling where and how are the performance of every single channel. So a use case around both finance and around marketing just to get very automated, they were basically having manual folks on the finance team build these dashboards or at the end of every month; or depending if any use case came from a sales rep or from someone in the marketing team, extremely manually done prone to error and extremely expensive in terms of people hours.

One of the first things we were able to do in the first few weeks was automate this completely. So now anyone can go into the business and depending on which state they're in seniority level, they can see their own data for their own store or region or district or channel, or as an executive, they can see it at a macro level, then really figure out what's the pulse of the business in a fully automatic manner.

So automating some of the internal finance and the sub go-to market stuff was something which a business like this was really able to start with. Flip over to the banking side. One of our customers is one of the fastest growing new banks in America, they're very focused on the customer side. How are the businesses using their product to make sure the business is super engaged? What are the features which have been used? What are the features which have been requested to early understanding what are the different types of businesses which use a product? What are the different types of segments inside these businesses? And how do they keep their eye on making sure engagement is high?

Because as long as engagement is high, churn is going to be low. But if you dropped the ball on how are they actually using your product, you are in big trouble. So a lot of them were focusing on actually the product and how to make sure that they're innovating around the product and going to be extremely competitive. So another tier of what these customers are doing with 5x.

And then we have other customers, which are more technical, which are like, hey, we have our own data scientists, and we know how to do a lot of this stuff. We don't care about owning the platform in the long term. And we know that ingesting data modeling and structuring and joining and cleaning it all of this is extremely cumbersome and requires a lot of expertise. Why don't you deploy your army of engineers and figure out those problems? We want to have clean data and once we have that, we have our advanced analysts and data scientists who know how to embed into the business and spread that information wisdom. We want you guys to own the core data engineering and the data platform stuff. And that makes sense to as businesses want to keep big parts of the secret sauce in-house, they can still use us for the core platform and the data engineering.

So vastly, depending on the size of the guy, then, obviously, smaller companies, which are pre-product, which is like, hey, this is not something we know how to do, can you just cooperate the entire thing? We'll use your platform, help us ingested data modeling structure, but also help us design how do we build our core data models? How do we set up tracking? What metrics should we actually collect? Can you use your institutional knowledge and go set this up and then we can keep editing small pieces, depending on what we want to do? So vastly varies over the board.

The problem we're really solving is, number one is the data platform, which is just something companies have to go do themselves, and the engineers end up spending 50% of the time more and setting up the stack and maintaining it. And is it the right stack? Did they pick the right vendors? They don't have the expertise.

And then the second thing is because of the standardization in the process and training these engineers and hiring extremely competitive engineers, we solve for a lot of inefficiencies, which happen, and can give you a very, very high quality, very standardized product, which allows you to move extremely quickly. So, if you just look at any of these use cases, we've gone in there and done a case study, the average numbers we look at is, hey, when we tried to do this, what you did is like 10 times faster. But what people are just blown away is like how quickly they're able to act on things because of so much of the standardization which goes into how we do it and the platform.

Erik: That totally resonates with me because a lot of the clients that we're working with, they're larger companies and so they often have pretty good understanding of what they want to do with data. But the question, how do you actually get the data? How do you get it cleaned and so forth? And I think often they also have competent technical teams, but those teams are just overstretched. And so you get into bottleneck and it's like, okay, we'll get back to you in three months. And by the way, that team sits in France and your team in China is trying to do something and they're trying to work at China's speed and just a complete dissonance in terms of actually executing on the strategy.

Tarush: I think that brings up this whole thing of centralized versus decentralized. And even if you have extremely competent centralized teams, just getting started getting that time of the day right and that's a massive problem. And we face something similar with 100 people on the data team sitting in America and we had a China team, which is developing five times faster. I love the fact that you use the concept, “China's speed”.

And I actually went to China and setup our own team in some way too much smaller. Like, we had five people on the entire China data team compared to 100 people globally and just empowering them enough to be able to execute at this speed. In some ways, what that meant was being able to set up our own tiny version of our platform inside the China space giving the China teams enough flexibility on that platform but still having ways for our global platform to talk to our China platform and sync on some of the core metrics, which might have still been own globally.

So you basically get into these hyper localized instances of the platform which large companies, you don't have to do all the time, whether it's at the department level, whereas there might be some core metrics across the company. But in general, as long as you adhere to those core metrics, your CMO or your CFO or your CPO, have the additional set of metrics on top of that, and they own kind of version of the platform on top of that. Or in a case of like regionalization, where you basically need to report on some global numbers, but for instance, how do you do billing?

America and China's vastly different, they need to tie back into psychometrics into what is their revenue, or what is their bond, or what is their span, what is all a few different things? But as long as you implement a system which standardized a few of these metrics and reports them back, it doesn't matter in some ways how the sausage is made, whether it's the regionalization of the department level use cases, it's a massive, massive problem for big companies.

Erik: So I think the cases that you've given us so far are enterprise related to finance, marketing sales, which makes complete sense because those are often systems whether the data might not be clean, but like there's relatively standardized high volumes of data. What about the IoT data landscape? So, if you look at things like AWS IoT or PTC ThingWorx, do you see any opportunity in the future to have a similar model where you can leverage these? Or is this too messy once you get into these applications?

Tarush: No, I think, IoT, at a point WeWork was a massive IoT game where every building, if you think about it was producing data, every chair is going to sensor in it, and you have like lighting and different things. So, we looked at each building as like a IoT robot producing this data. And the IoT landscape plays really well into this. You have an IoT, you have your real time systems and, you might have existing protocols they’re using or if you're traditionally in the OpenStack world, you might be using Kafka and stream processing, whatever it is.

The idea is that your IoT frameworks are producing data and at some point, you have a key value store which is updating, like based on aggregates, how many times a particular event happen, and that's updating what you call a key value store, and the key value store provides some reporting, hey, based on this IoT sensor, it went on and off 50 times in the last two hours, whatever it is. These update key value stores which should be updated inside the data warehouse, and then in place well into all of the other data platform stuff and how is this modeled, on top of that, how is it reported.

This data warehouse today works in this concept of micro batches, where you can have a batch update every 10 minutes, it doesn't have to be once a day job. So as you get into these use cases, where as long as you’re okay with a few minutes of latency and that few minutes could be two minutes to five minutes, if that's kind of what you want, then you can really build a lot of that complicated logic on the data platform itself. And even if you need to be streaming real time, then great, you can have your own IoT systems be extremely, extremely real time and then a copy of the data will be replicated into the warehouse, and based on any increment you want.

And you can still take advantage of these multiple layers stitch together and reporting, were kind of working out of the box and having all of the bells and whistles you get on top of the core data platform, which are extremely, extremely difficult to do with stream process, which I assume be difficult to do with Flink or other stream processing frameworks and to take advantage of both.

What's really happened in the last five years since snowflake and warehouse came out is that the data warehouse architecture and the way of doing things, so it's really one, it became mainstream. And batch processing with Spark or [inaudible 34:57] to do or even in some ways, stream processing with Kafka and Confluent and all of this stuff became more of the niche use cases, only if you really have those needs when you get into those.

So, what's happening now with even these companies, which have these needs around IoT and IoT is going to get more mainstream, but they're, in many, many cases, still take advantage of the warehouse when it comes to the core data stack on how you do reporting and experimentation and dashboarding and decision making and machine learning. We expect to see all of that to power on top of the warehouse.

Erik: So if somebody is working with 5x, they go to you, and they say we have a certain set of requirements, you have contracts already signed with Snowflake or other companies, so they're paying you directly. Is that correct? And then, so it's like a one service fee that includes access tool?

Tarush: I’ll tell you where we are and we know where we'll be in a few months. But the first thing is like the platform. So, imagine, in a few months being able to go to 5x.com, entering your credit card and the entire platform will be spun up for you: all those different vendors, you don't need to worry about any of that. And you’ll just build depending on how much you use. So if you use five credits in Snowflake, you get a bill for five credits in Snowflake. We actually don't charge anything for the platform. We just want everyone to use it. It's the same cost as if you went to Snowflake and [inaudible 36:34] DVD and stitches altogether, you will pay the same thing.

So there'll be no reason why a business, especially more early stage of mid-market business getting started from scratch to use the 5x platform on day one, you just get it all stitched together and you don't need to worry about enterprise contracts,. So that's the platform. In many cases, we also get some discounts from these vendors. We can pass back some of that to our customers. So in some cases, it might be even cheaper for depending on a few different pieces over there to use the 5x platform.

So, the self-service, sort of version of this, it doesn't exist today. There's still more work to be done around making fully self-service. And then on top of that we have these engineers, which we embed into the business. And at the moment, if you go to 5x, an engagement which is sort of join these two pieces where you'll have a monthly fee per engineer who is embedded into your business, and then on top of that, you'll get a bill depending on how much of the platform you use. So if you use X credits, you just get a bill for that, again, there's no margin on that on our side. In fact, in some cases, it might even be cheaper.

So at the moment, they both are joined because we're not at a point where it's fully self-service. But in a few months from now, if you just want the platform and have your own engineers, you'll be able to do that. But we expect a lot of the businesses to be able to add engineers from our side as well.

Erik: But this is somewhat a little bit like a red hat?

Tarush: We call this category, the SaaS marketplace. So we have a SaaS platform, which is we want to really spread this. And there’s a reason we don't touch any margin on this is we want this to be a no brainer about partnering with VC firms and incubators and all of these other companies being like when you're getting started, go use this platform because it's nothing proprietary to 5x. We've taken the best ways to stitch together the platform and we've gone and done this dirty work of negotiating all the enterprise contracts, and give it to you as a service. So everyone should go use this.

In some ways, once you have the core layer, once you have Snowflake in DVD and Five Trend, pick your own reporting tool like Tableau, MetaBase, Looker, Sigma, Preset, all of them will be compatible with it. And then you need to add a separate reverse ETL layer, great. You can just say plus, it's going to be like do you want to add sensors or high touch on your stack, here's what each of them is going to cost which on the one, you pick it. Great. It gets access to your data. It takes care of security compliance. It does it in the correct way and adds X number to bill, either it's in a fixed pricing or it's based on volume or it’s based on users or it's just your consumption scale. Whatever it is, it gets added to your bill and you're so good to go over there.

Erik: So you're just running on the…?

Tarush: And then there's a marketplace on top.

Erik: Yeah, you're on the resourcing, the teams around this?

Tarush: The marketplace. In general, the more business we push on to a vendor, we can start getting discounts from vendors too. But in general, our goal is to create the best platform out there and focus on monetization later. At some point, we'll have a more secure offering, like the business really wants to have audit compliant, GDPR, PII, all of these things inbuilt on day one, there's the way you do user access, and all of these things, we're building stuff on top of the core platforms and you'll be able to get a version of the 5x platform, they’ll called something like 5x Secure, you'll be able to sign up for that. And for that, there'll be some premium. That's how we eventually monetize the platform. But the core platform out of the box will be either at cost or cheaper than going directly to the vendors.

Erik: So we've looked a lot into marketplaces in the past for clients, and naturally, like most marketplaces are built on top of some platform. So it's PTC and the PTC marketplace and so forth, which makes sense. But then they're always proprietary, and they're always therefore limited. So I think this is a very interesting approach of having basically a SaaS plus actual human services best in class that's kind of curated and managed but without requiring long- term loyalty to any particular software package. That's an interesting approach.

Tarush: At some point, if there's any software package you don't like, you just press minus and it goes away, and you pick the next one, you press plus. And what's super cool is, as soon as you add a new vendor to your stack, because we know your stack, the certifier X engineer you get basically gets a notification saying, client XYZ or Erik just added this vendor, use the training module for this vendor and please finish it in X number of time. And now, your engineers already trained unlike the module you selected.

I think Starbucks is one of the greatest companies in the world, not because they have the best coffee, but they have the most consistent experience ever. Like I walked into a Starbucks in Shanghai, but it was super cool, Starbucks in the world totally for what's called the Starbucks Select. I'm not even a coffee drinker. But what I see is that this level of like standardization, which creates a delightful customer experience. No matter where in the world you are, right on Starbucks platform, which is the app plays really well with that in some ways very, very different example because we shimmy technical.

But we're investing a lot into training these engineers not only do we get to hire the top 0.1%, but the level of training and fall and follow up training that is get, we're hiring one person in the world, we're hiring data engineers. So, from our customer’s perspective, but also from the engineer’s perspective, what we're going to be able to do in terms of training and doubling down on these engineers’ careers, I don't see any sort of company or any consultancy in the modern data stack which will be able to provide the same level of sort of training around it.

Erik: Yeah, I mean, even just finding people, let alone keep it on by trained and managed well by itself, you're paying a headhunter minimum three months’ salary to bring somebody on board, if you can find the right person and how long do they last them.

Tarush: Exactly.

Erik: I know, you guys you said you just kind of pivoted into this direction about 12 months ago. But you now have venture backed, are you a bootstrapping this? Do you have any raise plan in the future that you're able to talk about?

Tarush: Yeah, sure. We are venture-backed. We raised around six months ago. We just crossed a million dollars of revenue. And we are looking to raise our next round in the next few months.

Erik: Anything we haven't touched on yet that's important for folks to understand about 5x?

Tarush: I'm so close to it very often, I go off on a tangent. But if there's anything we missed and you would love to learn more, feel free to sort of reach out and we love chatting. We’re super friendly. I'm happy to share more of the contact details with Erik on the podcast. But you can just reach out at like hello@5x.co

Erik: Well, Tarush, really a pleasure to talk to you today. And we'd love to have a follow up in two years or so and see where you are, sounds like this is going to be an interesting business to build.

Tarush: I would love that. Thank you so much for having me on the show. I really enjoyed talking to you. And I hope we were able to add some value for your listeners.

Erik: Thanks for tuning into another edition of the IoT spotlight podcast. If you find these conversations valuable, please leave us a comment and a five-star review. And if you'd like to share your company's story or recommend the speaker, please email us at team@IoTONE.com Finally, if you have an IoT research, strategy or training initiative that you would like to discuss, you can email me directly at erik.walenza@IoTone.com. Thank you.

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