Data Reporting as a Service with Tarush Aggarwal – Episode 58

Data Leadership Lessons
Data Leadership Lessons
Data Reporting as a Service with Tarush Aggarwal - Episode 58
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Tarush Aggarwal is our guest this week, and we have a fantastic conversation about democratizing data reporting, and how even non-data-focused businesses need to have deep reporting capabilities to be competitive. Regardless of your role or industry, this is an episode worth checking out!

Watch this episode on YouTube: https://youtu.be/Ehl4heGovas

Save 20% on your first order at the DATAVERSITY Training Center with promo code “AlgminDL” – https://training.dataversity.net/?utm_source=algmindl_res

Connect with the host, Anthony J. Algmin, on LinkedIn: https://www.linkedin.com/in/anthonyjalgmin

Become a Data Leader – https://algmin.com

About Tarush Aggarwal:

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, Tarush scaled the system to support 12k employees, as well as 100 data team members. Tarush was also the first data engineer at Salesforce in 2011.

5x – https://5x.company/

Episode Transcript:

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anthony: Welcome to the Data Leadership Lessons podcast. I’m your host, Anthony J. Algmin

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anthony: Data is everywhere in our businesses and it takes leadership to make the most of it.

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anthony: We bring you the people stories and lessons to help you become a data leader. Our

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anthony: Today on data leadership lessons, we welcome

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anthony: Tarush Aggarwal. Tarush is the founder and CEO of 5x, which offers data reporting

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anthony: as service so users can make data driven decisions faster, which are necessary to

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anthony: succeed previously as the head of previously, as the head of data at We work.

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anthony: succeed previously as the head of previously, as the head of data at We work.

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anthony: Teruch scaled the system to support twelve thousand employees as well as a hundred

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anthony: data team members. Tarush was also the first data engineer at Sales force in two

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anthony: data team members. Tarush was also the first data engineer at Sales force in two

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anthony: thousand and eleven to reach, as one of the world’s leading experts in leveraging

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anthony: data for exponential growth. Jewish, welcome to the show.

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tarush: thank you so much, Anthony roingsed to here. thanks

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anthony: and you’re coming to us from Costa Rica, and in our pre-show, I saw this amazing place

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anthony: that you’re at. I am in dreary Chicago land right now and it’s just abysmal, so

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anthony: you got to breathe the good energy of the wonderful weather to us. so like, um,

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anthony: like you do with all our first time guesss. Why’t you just take a couple minutes?

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anthony: Give us the story of your background. How

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anthony: like what you’ve done in your career has led you up to what you’re doing now at

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anthony: 5x and will kind of take it from there

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tarush: amazing yet. I again’t. thank so much for haing in the show. Really excited to chat.

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tarush: and uh, thank you for being patient. While the I know the preon boarding wasn’t as

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tarush: as easy as being in New York, so really appreciate that.

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tarush: Um, you know, I come from a. I come from a pretty technical background. growing up.

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tarush: My mom ran an e-commerce business. I was exposed, Um to software engineering pretty

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tarush: early on. She joks that I was you so technical because she was was teaching herself

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tarush: half of those things while she was pregnant with me. Um,

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tarush: so you know, sort of figure out very early on. Computer science was my area, Got to

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tarush: go to Connegie Mellon,

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tarush: After college, got a job at Sales for Dot com Silicon Valley, and super excited, and

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tarush: and I remember showing up to work on the first day and realizing very very quickly

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tarush: that software engineering was not my cup of tea. That focusing on a you know a small

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tarush: feature for an extended period of time is super super important, but just not

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tarush: something I was personally interested in, and more importantly in a software

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tarush: engineering. It had become an art form where the rules, um, of best practices and

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tarush: how to do it were already established,

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tarush: so it was very much learning the rules, being able to play inside this well crafted

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tarush: Um. art form. if you may, and you. I didn’t personally interest me and I, At that

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tarush: point this was two thousand eleven. Um. No one was really talking about data and I

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tarush: got to with a product manager. Focus on Um. You know building this framework which

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tarush: allowed sales for to extract metrics from log files. Sas, was used as for Uh, For

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tarush: products like benchmarking, used it to figure out engagement of different customers.

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tarush: How they’re using the sales for product. This later became the product and Lid,

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tarush: Esteam got hundreds of engineers on that team now and year before that. I moved on.

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tarush: Um. Most recently I,

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tarush: I got to join. We work again pretty early on with the data team Was of just a few

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tarush: people have skilled it. Up to a hundred plus people jump started data engineering

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tarush: data platform, Um, got to work on Um. Our out

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tarush: got to lead our China platform efforts when we doing a lot of cool stuff on machine

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tarush: learning and facial detection, and all of these other cool things which I

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tarush: ▁ultimately never got to see light of day just because of the uh of the direction of

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tarush: the Ip at that time. But I found myself at the beginning of covertt, interestingly

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tarush: enough, in Bali, Indonesia, which is extremely different from Silicon Valley in New

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tarush: York,

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tarush: And you know taking some time off from the valley

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tarush: helped me to

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tarush: in really understand what’s happening in terms of data. and that’s that. In know the

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tarush: next ten years

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tarush: every company is really go. A need to invest more in data capabilities. Things like

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tarush: how a customer using your product you go to market’ shargy, where different segments

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tarush: which a lifetime value. Um.

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tarush: these are the type of use cases companies are going to start focusing on in the next

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tarush: ten years. What’s happening today is that ten percent of the world or highly

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tarush: technical companies can really go do this. So the way Google and Facebook and Apple

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tarush: get value from data is very different from your typical series a company. And what’s

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tarush: happening is that the modern data stack is just becoming more and more more

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tarush: sophisticated, so all of a sudden, you know we require a few data engineers just to

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tarush: stitch together this stack, and all of a sudden we care about security in P, i, I

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tarush: and g, d, p, R, and all of these other things. So for every company which are just

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tarush: getting started for them to start from scratch, is no longer really feasible. so

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tarush: essentially started five ▁s, to help ninety percent of companies get value from data

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tarush: on the same way top tech companies can do it, Um we, with A with a much lower

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tarush: barrier to entry, And that’s reallyly what I got super excited about, and

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tarush: coincidentally that happen being in Baally, which is not a technical place at all,

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tarush: but yet a little bit of up about my background in fives.

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anthony: Yeah. Well, it’s interesting because you’ve seen perspectives from fast growing of

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anthony: Startu companies like we work, and then sales

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anthony: force, which is obviously pretty well established at this point. and you’ve seen

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anthony: how Silicon Valley operates and where there so much emphasis on that software

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anthony: engineering side and a lot of the processes and the tools that people are using are

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anthony: so far advanced. And then you go into the database and’ like what is this? This is

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anthony: dark ages. Comparatively,

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anthony: do you have hypothesis on why that is like. How did we end up in this spot? As far

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anthony: as you can tell,

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tarush: yeah, you know. I think

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tarush: that’s a great question. and if you look at the data space, it’s much newer than

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tarush: software engineering. Right this pretty much. I think Two thousand Eleven was the

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tarush: first the data engineers were recognize as a profession, and the Goodra market

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tarush: strategy data employed is very much replicated software engineering, where you build

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tarush: a technical product and use setary technical teams. And that’s typically, you know

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tarush: what we have done in this space so far. If you look at data, Vse, Software

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tarush: engineering, data is a lot broader.

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anthony: Mhm.

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tarush: a lot more. Every business beyond some size should should focus on how customers are

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tarush: using their product. What to do with the gold market strategy? So

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tarush: as an whole, data is a lot broader than software engineering. Yet they’ve copied the

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tarush: gold market strategy of software engineering companies? So it leads to

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tarush: this landscape where a lot of companies want to get value from data, but really

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tarush: don’t have the technical expertise of bandwith to go. Where do it? So we see a lot

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tarush: more companies playing at tools like Google and ▁lyrics, or maybe even something

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tarush: like a sort of tableau, which is a very low level and they don’t really have the

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tarush: right resources or expertise to bed the up level.

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anthony: Yeah, you know, and I think about it too. where like. We’ve had data for a long

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anthony: time, and and I think it’s a new area

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anthony: cause, data traditionally in the enterprise came from

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anthony: operations and operations. While very important, is kind of boring, but creates a

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anthony: lot of data and so a lot of the early data work was because we had to manage the

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anthony: data being spun off from operations. And so it was like we’ receiving, we’re being

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anthony: reactionary. We’re catching that data like Yeah, we create some operational reports

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anthony: and will do some stuff. But but the notion of analytics to drive improvements or

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anthony: drive new new innovations and opportunities in a business that was so secondary,

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anthony: and to the point where we didn’t even bother with it until like we’ pushing you

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anthony: well into the two thousands, at least right, and so outside of niche industries

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anthony: that we’re kind of predicated on that. I grew up in the financial industry where

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anthony: our traders were using advanced analytics. You know as early as anyone, because

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anthony: they could get an edge in the trading place. So it it actually directly tied to

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anthony: their business model. But many of the

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anthony: organizations out there were just like Hey, We got to get our sales information.

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anthony: We’ve got to populate accounting stuff. That’s about it like we know what we’re

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anthony: doing. We’re we’re going to look at operational metrics as as incremental

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anthony: improvement as opposed to innovative improvements. But where I’ve seen software

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anthony: engineering influence stateta analytics. Now we start to see. Hey, there’s really

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anthony: this convergence of those two areas and new and novel ways that everybody says

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anthony: today. Like Hey, these are table stakes. Any business, even the smallest of the

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anthony: small businesses need to have a Da to analytics capacity so that they can read the

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anthony: market so that they can understand where’s their opportunity? How do they find ways

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anthony: to grow their businesses at at literally any size? And so it sounds to be like what

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anthony: five ▁x is doing

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anthony: is making that capability available to literally any businesses out there? Am I

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anthony: reading that right? Is that what is that what you guys are doing?

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tarush: Yeah, yeah, that’ doing. But thinking of

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tarush: just on tangent, you know, I think you hit the nail in the head that every business

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tarush: now needs to do this. And really that happened for a few reasons. But if you look in

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tarush: the macro, you know, Five, ten years ago we were using much fewer platforms you were

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tarush: using. Gail, and that was calling, and everything,

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tarush: what’s happening today is that the average startup is using ten tow different data

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tarush: sources.

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tarush: All of a sudden, we like using ▁zoom for video and slack for messaging, something

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tarush: else,

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tarush: stripe and square for Ps and ▁ze T, and sales from much smaller use.

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tarush: What this means that the capabilities of each of these tools is becoming very limit

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tarush: because they have a much smaller s

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tarush: of data which they have access to. So

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tarush: this this inherent need to recentraliise data

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tarush: and answer holistic questions across it. So you, given that this is accelerated by

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tarush: these companies and products not being able to answer holistic questions? Five A.

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tarush: When we were only using Facebook for ads, Facebook had a pretty good idea on your

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tarush: marketing. Spendard could give you pretty sophisticated insights. But now for using

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tarush: five tools, you can’t compare Facebook candidates to Google and Itx apples to

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tarush: apples. So you need to injestt it into your own warehouse and make your own sense of

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tarush: it. So I think’s like the macro trend of what’s happening, and this is a pretty

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tarush: technical trend.

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anthony: So before we get into the the five ▁x, then causecause it it just. Reminded me when

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anthony: I was growing up, like all the old guys would say, You know history keeps repeating

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anthony: itself. You’re going to see these patterns come up over and over again, and now I’m

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anthony: one of the old guys. And so I’m like Da. this is exactly the same dynamic I’ve been

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anthony: talking about in business intelligence forever. And

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anthony: if you need data analytics from a particular

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anthony: one, singular source, that singular source, that that system is probably going to

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anthony: be fine at giving you the analytics in that system alone. But the moment you need

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anthony: to start seeing the analytics across two, three, five, twelve different systems,

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anthony: you’re going to need something that solves for the system, not just integrate each

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anthony: of the individual point solutions, because because they’re not designed to do that,

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anthony: they introspective, they’re not overlaying that analytics need. And that’s it’s the

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anthony: same problem that we had with basic reporting twenty

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anthony: thirty years ago. you know,

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tarush: spot on. and you’ right, sales force would love you to push metrics into sales force

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tarush: and have you you know reporting on top of it, But it’s not really built for that.

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tarush: It’s built more as you know, a relational data model

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tarush: and you, and it tries to you know, force stuff into it. Um, and that’s not. you

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tarush: know. You know that

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tarush: that. that’s not quite where the industry is going under other earth. That’s not a

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tarush: long term solution on any of the best practice of hard. Go do it

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anthony: Yeah, so so tell me about five acts. Where did five axs come from? in terms of its

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anthony: origin story? And what are you trying to to serve and differentiate yourself in the

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anthony: marketplace to do?

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tarush: absolutely. So you know five ▁x came with the realization that

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tarush: they are a small subset of companies which are getting exponentially more value from

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tarush: data than others and these companies are technical in background, But what they

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tarush: really have is they’re able to have these large platform teams which spin together

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tarush: the modern data stack, which sort stitch it together. So you know you have five

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tarush: layers in the modern data stag data collection, ingestion storage modeling,

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tarush: reporting

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tarush: each of them today have a billion orllar player. So if you’re a start up looking to

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tarush: get started, you have to go sign five enterprise contracts. Probably spend months

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tarush: trying to stitch together the stack on top of it. Now stakeholders want to consume

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tarush: it on different ways. You have B. I users wantnna Slic, and ice and Anys, when

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tarush: around, experimentation and Aml engineers who want run their models On Top of this,

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tarush: we now care about G, e, p, r and p. I, and security, and all of these different

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tarush: things. So it’s non trivial for companies to go build this from scratch every time,

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tarush: unless it’s really they inside their d n a right. So they end up either hacking it

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tarush: together, or you know, hiring an engineer, but spending more than half of their time

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tarush: building the stack and maintaining it, And that’s just not comparative anymore. And

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tarush: the the big companies have the luxury of having you are ten, twenty engineers. sort

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tarush: of doing this.

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tarush: So Fivevex came from the idea that you know how do we make it very, very, very

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tarush: simple for these ninety per cent of companies to really get the same level of value

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tarush: from data as some of the bigger companies. So you know, the first thing we do is

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tarush: think of us as that large platform team where you know we are.

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tarush: We are

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tarush: working with the best and class providers across those five layers of the stack we

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tarush: integrated in. You get security, P, I, I, g, p, R, compliance. Order those things

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tarush: for free, but more interestingly, Um, you know, as you have Nius cases, like pushing

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tarush: data back into application tools, what we call reverse E Ta or Hobb, we do data,

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tarush: lineage or observability, which is becoming a huge area or running machine learning

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tarush: models For all of these different vendors, You don’t need to figure out what’s the

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tarush: best tool out there. How do you integrate those tools? We do that for you, we figure

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tarush: out the billing. We work with all these vendors and have backck and billing, so you

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tarush: get you know one touch, one click integrations with the best in class tools. You get

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tarush: direct back and billing as a single platform,

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tarush: so in day one itself you are extremely productive. The platform is ready. It’s

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tarush: almost like you had your own large back from teams stitching it together. So that’s

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tarush: the first thing we do and we also have a service where you know. Currently we’re

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tarush: intervining about a hundred engineers a day fully automatically. Um, you know about

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tarush: eighty percent automatically in India, less than two per cent of it,

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tarush: less than two per cent of them make it through our interview process. And then

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tarush: because we control the stack, we can pretrain these engineers on the staff,

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tarush: and we give you these engineers to become an extension of your team. And you know

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tarush: the infrastructure plus the engineers. What this means is you really get everything

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tarush: you need to run a comprehensive data strategy and five ▁x companies end up using

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tarush: data to make decisions in the first one month of of starting an engagement. Whereas,

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tarush: if you try to go do this yourself, spend a few months to hire engineers. Sort figure

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tarush: out what stack you want to use. Sign those enterprise contracts.

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tarush: It really at least a year before you make decisions.

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anthony: It’s It’s an interesting model, because it. I think it starts to address some of

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anthony: the big challenges that I see in the marketplace right now with if you’re a start

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anthony: up, it can be very difficult. even just the the hiring process to get data

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anthony: engineers. You know you’re going to need them full time. You know you’re going to

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anthony: need a certain amount of capacity to be able to handle the information loads that

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anthony: you’re going to have, but it’s very difficult to create an attractive job posting,

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anthony: and to you know, attract a a data engineer, a capable one or an architect in into

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anthony: your small business. Unless you know you, you can convince them to chase the dream

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anthony: of you know, the I P. O, and the you know the Big pay day and what have you that

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anthony: sometimes works. But I think that people today, especially data engineers tend to

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anthony: gravitate towards more stable Um environments. And I think that if you can solve

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anthony: for data engineering consulting, which is effectively what you’re doing by creating

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anthony: that ability to recruit and hire talented people that can then be deployed to your

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anthony: clients, It it expands what your service offering is capable of doing. While

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anthony: recognizing that traditional project based consulting models aren’t really

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anthony: sufficient for the kind of constant load that a data engineering component would

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anthony: typically have would. Would you

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anthony: agree with what I said?

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tarush: yeah, Totally, you know. We don’t see ourselves as a typical consultant company Who

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tarush: comes and does you know a short term project?

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tarush: Typical consulting companies can start from scratch right every new engagement.

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tarush: whether are in houseteam or consultant, You really start from scratch and typical

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tarush: consultants come in short term. They might set up the warehouse. They might focus

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tarush: new for a few cases. We see ourselves as a long term replacement for Da engineering

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tarush: needs right,

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tarush: Typical fivex companies are looking at us long term.

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tarush: They have you know they want to get value from data they don’t know, Kind of hard to

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tarush: go. do this. Iss. Really time for that core data stack. How you collect a digested

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tarush: modelate, Structured reported. It’s really time for that part of the stack to be

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tarush: commodized, And when companies want a higher data hire us. They now want a higher

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tarush: data scientist and analysts who can focus on the high levels of the stack, which is

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tarush: a competitive advantage, which is where you can build Da, our products or build

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tarush: models and really use that as an advantage. They have no advantage in doing the core

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tarush: stuff. They just need to go do it. And what we’re showing now is we can just do it

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tarush: faster, cheaper and higher quality than you can do it in house or work with a

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tarush: consultant company. And you know we aren’t replacing data teams. We’re not. We’re

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tarush: not making data teams obsolete, which is having them focus on the higher levels of

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tarush: the stack, which is really where they want to be focusing on. right. like I, I sort

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tarush: of read the study that eighty percent of data scientists today spend time investing

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tarush: there and cleaning it and modeling it, And that’s not what they want to be doing.

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tarush: Um, And we can just do it right now. Um, you know, because of this platform we’ve

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tarush: built and stitch it together, and because we can hire really smart engineers in

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tarush: India, and then pretrain them on the best on the modern day best practices. Um, you

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tarush: know where we? we see ourselves as you know, fifty per cent platform, fifty percent

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tarush: services, and together, Uh, we, our goal is really hard to. We commodateize this

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tarush: layer and just do it as a service across any you know, sort of sies, a early stage

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tarush: company, but also you know about

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tarush: about twenty percent of our companies right now. Are you know? Fortune five hundred

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tarush: companies, Public companies, Uh, they’re non technical. So, think of you know the

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tarush: Mcdonald’s Burger Kings, or like the large liquor companies of the world, where they

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tarush: might have used excenenture in the past, but realize that Ecentra is not a long term

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tarush: solution towards owning your data stack.

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tarush: And now we can just do it as a service far more effectively and farm more cost

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tarush: effectively. Um.

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tarush: So for them,

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anthony: Yeah, well, and and I think in in having spent a fair amount of time in large

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anthony: organizations, you know, the the traditional consulting model is very expensive and

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anthony: isn’t particularly well suited to these set of challenges. And I think about you

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anthony: know, from that recruiting perspective even if you wanted to getting people to say

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anthony: hey, I’m I’m at Mcdonald’s and you know, great global organization, but not

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anthony: necessarily the first place, the top tech talent is thinking of going to work,

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anthony: Right they? there? it’s there’s. There’s a a added challenge when the places that

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anthony: people want to be tend to be the technology companies themselves, the Googles of

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anthony: Facebooks, the Silicon valley companies that are building out the technologies.

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anthony: That’s those are the people who are going to get the highest talent. But if I

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anthony: Mcdonalds, I don’t want to settle for second tier. And so I’ve got a gap there

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anthony: that’s going to be difficult to fill. The other

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anthony: thing that you werere talking about, Uh, really, uh, got me thinking is that you

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anthony: know, so my audience knows like I’m all about data leadership. Data leadership is

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anthony: my thing. That’s what I do. That’s what my book is on. That’s what the podcast is

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anthony: about. and I think about the. The fundamentals of data Leadership are really about

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anthony: having a business do something different as a result of the insights it gets from

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anthony: data. So data analytics technology doesn’t really matter. It’s all about improving

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anthony: business outcomes, So how am I driving an increase in revenue, a decrease in cost,

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anthony: better risk management. That’s really it. And when I think about what matters in

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anthony: that, it’s the decision and the action of the business.

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anthony: Everything precursor to that, the analys of the Da. the the data warehousing and

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anthony: the modelling. I have a question for your round Dayta modeling in a minute, but the

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anthony: uh. all of that heavy lifting on the data stuff

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anthony: isn’t to your point, like it’s not a competitive advantage. It’s table stakes. It’s

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anthony: stuff everybody has to do, and if you can solve for that most efficiently and most

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anthony: cost effectively, working with the top talent wherever you can source it, and your

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anthony: internal energies as an organization with those people who have to know your

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anthony: business from the inside. Because that’s where the business decisions get made. You

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anthony: can connect up this massive power of data analytics without trying to recreate it

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anthony: in every business that exists out there, and that’s I think. it’s an important

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anthony: lesson for those those students of data leadership say. what is the competitive

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anthony: advantage for me? If I’m a trading firm, Maybe the analytics and the stack because

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anthony: of the speed and latency competitive threat, Maybe that is part of your core

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anthony: business, But if you are a C, p G firm, you are not competing on most of that

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anthony: stack. You’re getting to that last one or two parts of that life cycle for

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anthony: it to actually matter for your business competitively. So what? what’s your

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anthony: reaction to that? Would you? Would you do agree with that or what what else

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anthony: would you say to it?

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tarush: no, I. I, one hundred percent agree with that. You know, the legwork is just

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tarush: something you have to do was the cost of admission towards eventually figuring out

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tarush: the higher levels of the stack where you have your competitive advantage, insights

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tarush: or strategy pieces. Making all of that stuff and data engineering and

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tarush: interestingding data was just the cost of admission. You know, what’s happening now

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tarush: is is just becoming so much more clear how effective this model is like. We just had

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tarush: one of our first few K studies which show that we were

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tarush: twenty to twenty five times cheaper than a consultant coming in and doing it, And we

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tarush: did more with one engineer in three months. Then they did with five engineers in six

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tarush: months. And it fundamentally comes from two different areas, and the the main one

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tarush: is, we don’t start from scratch Right on day one. You get the infrastructure area to

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tarush: go, and that takes companies about six months by itself. And what? that also means

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tarush: that companies have to maintain it, Whereas in the five ▁x model, you know, we have

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tarush: a large platform team doing this. So you know what’s a big focus for us in the next

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tarush: six months is day observability in Da age. But as soon as we figure what the right

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tarush: vendor is in the direction we want to go, all of our customers get it for free. And

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tarush: you know that is serious engineering manpower. If you try to go do Itrself,

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tarush: and The, and you know, the I think, the more interesting piece which you sort of

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tarush: touched on is top, Talland, historically has wanted to go work for Google and and to

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tarush: dear, sort of companies.

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tarush: And you know what we’re really seeing is that

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tarush: talent is now also a little bit fed up of being one of fifty data engineers in

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tarush: inphosis or an ecenture, or you know, even in a top tech company because your your

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tarush: relative marginal impact starts to dwindle

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tarush: what what really happens in the Fiex model Is that you know because of our platform,

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tarush: a relatively midlevel engineer is just capable of doing so much more, and

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tarush: we solve for that problem that you

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tarush: know most businesses today. If they hire a data higher, they don’t speak data, and

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tarush: the data hier doesn’t speak business, and they don’t really have a good way of

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tarush: working well together. and hence the company needs to get to a certain size. Really

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tarush: higher, data leadership. and only at that point are they able to really work with

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tarush: your midlevel engineer. With. finally, the data leadership acts like that translator

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tarush: in the five ▁x model. You know the process by which we work with the company makes

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tarush: it very clear of you know what we need from the company and what the engineer needs

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tarush: to do in order to add value. In some ways, You know the think of us as that

355
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tarush: translator between

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tarush: having hiring engineering talent and actually effectively communicating with the

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tarush: company on helping the company hit their business decisions. Because you know at the

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tarush: end of the day no one hires data for the sake of building a data team. It’s always a

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tarush: means to optimize the business,

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tarush: and what I’m extremely bullish on is this idea of you know, with the five ▁x

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tarush: platform with this process were built on helping engineers work with businesses.

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tarush: You know, a single data engineer is able to go into a company and really adds so

363
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tarush: much more value

364
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tarush: by being in a more diverse environment where they bring data skills to the table

365
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tarush: which the company really needs, and being in the same room as a decision maker in

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tarush: the Ceo Andchief Product Officer, instead of being one of fifty,

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tarush: being a fifty percent data team and a larger company where they are optimising one,

368
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tarush: ▁query, or like running you, one small part, one level of one small segment of a

369
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tarush: product, so

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tarush: I believe that with our. Model,

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tarush: A lot of data engineers are very quickly are very excited with what we are doing,

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tarush: and the idea being that their skill sets are so much more valued and appreciated in

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tarush: companies

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tarush: that are just at that right size.

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anthony: it’s It’s almost like you’re you’re able to create

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anthony: an environment or or an opportunity for those data engineers to have a a massive

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anthony: arsenal at their disposal like they’re They’re you’ creating. I don’t want to use.

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anthony: the term. Super Soldiers is not quite what I’m going for, but they, they’re able to

379
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anthony: come in, and I have this vision of like the Iron Man, uh costume, or something like

380
00:27:41,593 –> 00:27:45,997
anthony: the The Mechanical Suit Is that they’re able to do so much more than a single

381
00:27:46,247 –> 00:27:50,552
anthony: individual typically would be, because they have the surroundings of this platform

382
00:27:50,802 –> 00:27:55,040
anthony: that five ▁x has created so that they can do so much, but you still have to have

383
00:27:55,357 –> 00:27:59,594
anthony: that connection point to what’s unique in that business, and so your model is is to

384
00:27:59,678 –> 00:28:03,998
anthony: put them with that business and to. Have that that the best part of consulting is

385
00:28:04,165 –> 00:28:08,153
anthony: coming in and and personalizing for that organization what that organization

386
00:28:08,319 –> 00:28:12,640
anthony: particularly needs when I, when I was doing consulting, Um more frequently. I

387
00:28:12,807 –> 00:28:16,961
anthony: always said the patterns are easy. I’ve been doing this a long time. I can see the

388
00:28:16,961 –> 00:28:22,317
anthony: patterns immediately. What I home in on is what makes this company unique. What?

389
00:28:22,317 –> 00:28:25,920
anthony: What is the problem that this company has that is different than every other

390
00:28:26,087 –> 00:28:30,642
anthony: company I’ve seen? Because if I can get to that, we’ve got figured out like that Is

391
00:28:30,792 –> 00:28:33,278
anthony: the part of the solution you can solve any problem.

392
00:28:34,079 –> 00:28:39,367
anthony: You have to understand the problem at that level. And the only way you can do that

393
00:28:39,834 –> 00:28:45,607
anthony: is to recognize all of that platform. All of that power that five ▁x delivers to

394
00:28:45,673 –> 00:28:50,645
anthony: those data engineers is an important part, but not the entire solution. It’s that

395
00:28:50,795 –> 00:28:53,998
anthony: human who can really look at that uniqueness that

396
00:28:54,482 –> 00:28:58,403
anthony: becomes that critical link and five really helping that organation achieve

397
00:28:58,553 –> 00:29:00,088
anthony: everything possibly do.

398
00:29:00,088 –> 00:29:05,560
tarush: yet you know, our is very much in, you know, getting started as a data engineer when

399
00:29:05,560 –> 00:29:08,446
tarush: it was recognised as a profession and I remember Maxim.

400
00:29:10,999 –> 00:29:15,236
tarush: The the Guyt, a B and B wrote this super famous articlecause. it was called the Rise

401
00:29:15,320 –> 00:29:19,808
tarush: of the data engineer and that was such an inspiring. There was such an inspiring

402
00:29:20,041 –> 00:29:24,195
tarush: article which sort spoke about this new field which did engineering, and how every

403
00:29:24,362 –> 00:29:28,917
tarush: company is going really need it. and you know, in some ways like

404
00:29:30,769 –> 00:29:34,122
tarush: what we are building. If you look at five acts and the people were hiring right, we

405
00:29:34,289 –> 00:29:39,727
tarush: hire data engineers and our goal is really hard to commodize this and offered as a

406
00:29:39,878 –> 00:29:44,365
tarush: mass market service to help you know any business at that stage where they need

407
00:29:44,599 –> 00:29:51,005
tarush: this. So it’s you know, I really do resonate with your analogy of

408
00:29:52,674 –> 00:29:54,275
tarush: you know, solving all of those

409
00:29:55,560 –> 00:30:00,999
tarush: all of the challenges in data engineering today and really giving them superpows so

410
00:30:01,166 –> 00:30:05,403
tarush: that we can start to imped them across all of these businesses globally and give

411
00:30:05,553 –> 00:30:08,356
tarush: them the right tool to give the companies the right tos to work with these

412
00:30:08,439 –> 00:30:14,529
tarush: engineers, because the skill set they bring in terms of data hygiene, data quality,

413
00:30:14,762 –> 00:30:18,116
tarush: building or building the right foundations for you to

414
00:30:19,167 –> 00:30:20,919
tarush: leverage data for your,

415
00:30:22,353 –> 00:30:24,122
tarush: for your top of final metrics, and

416
00:30:25,323 –> 00:30:30,361
tarush: to of fun insights. Is extremely critical, and something which every business is

417
00:30:30,445 –> 00:30:34,516
tarush: going to need in the next ten years, and the businesses which are unable to do it

418
00:30:34,849 –> 00:30:39,087
tarush: just won’t be able to compete, Because when your competition is able to understand

419
00:30:39,404 –> 00:30:44,359
tarush: its market strategy, How customers are using your product, You’ unable to do it. We

420
00:30:44,442 –> 00:30:49,247
tarush: sort a sous in digital marketing ten years ago. with the rise, Google and companies

421
00:30:49,647 –> 00:30:54,285
tarush: that don’t do digital marketing today don’t exist anymore like

422
00:30:55,003 –> 00:30:59,557
tarush: we don’t hear them, because they gone. The same thing is happening in data ten years

423
00:30:59,724 –> 00:31:03,561
tarush: later, and history has show us what happened with companies that didn’t get into

424
00:31:03,645 –> 00:31:07,966
tarush: digital marketing. The companies that don’t get into data now just won’t exist in

425
00:31:07,966 –> 00:31:08,967
tarush: years from now.

426
00:31:09,517 –> 00:31:15,757
anthony: I completely agree and I think that the barriers to entry for data are so low. Like

427
00:31:16,157 –> 00:31:21,996
anthony: the. The amount that you can do with very limited investment is tremendous now. And

428
00:31:22,180 –> 00:31:26,634
anthony: and that’s raised that minimum kind of table stakes level of like. If you just want

429
00:31:26,801 –> 00:31:30,805
anthony: to compete. If you want to exist, there’s a certain amount that you that you just

430
00:31:30,955 –> 00:31:35,843
anthony: have to do. And and so I feel like I, I have to just be in full disclosure to the

431
00:31:35,994 –> 00:31:40,481
anthony: audience out there, In turns to you, is is I grew up as a data engineer. Like,

432
00:31:40,632 –> 00:31:45,687
anthony: probably more so than anything, I would consider the core of my career started as

433
00:31:45,920 –> 00:31:49,841
anthony: being a data engineer. And so this is all very close to my heart. and it’s great

434
00:31:50,074 –> 00:31:52,877
anthony: because I could like. twenty years ago. I could have told you I like date

435
00:31:53,044 –> 00:31:56,965
anthony: engineers. We need more respect, and so now we’re finally at this point where we

436
00:31:57,048 –> 00:32:03,288
anthony: can see in the marketplace where this value proposition has become understood and

437
00:32:03,288 –> 00:32:07,842
anthony: and realized, and that successful business is focused in this what could be

438
00:32:07,925 –> 00:32:12,313
anthony: considered a relatively narrow band of the entire value proposition of an

439
00:32:12,313 –> 00:32:13,314
anthony: organization.

440
00:32:14,082 –> 00:32:19,354
anthony: It, it is now clear that that’s an incredibly important part of that that value, so

441
00:32:19,604 –> 00:32:23,207
anthony: speaking, has one day to engineer or to another. I promsed that I had a question

442
00:32:23,358 –> 00:32:26,561
anthony: for yourndta modeling. And and this takes us in a little bit of a different

443
00:32:26,644 –> 00:32:29,280
anthony: direction, But I have to ask you anyway. be cause. there’s only so many guests that

444
00:32:29,364 –> 00:32:34,952
anthony: I can ask this question to. Is data modeling dead like have we? Is data modeing

445
00:32:35,286 –> 00:32:40,725
anthony: still relevant today? Because I think there’ a many organizations out there,

446
00:32:41,042 –> 00:32:45,279
anthony: especially when you think of things like Tbau or these other tools that try to blur

447
00:32:45,680 –> 00:32:49,767
anthony: what used to be very distinct lines between different parts of that data life

448
00:32:49,917 –> 00:32:55,123
anthony: cycle. There’s there’s an ability to do so much with the technology

449
00:32:56,474 –> 00:33:03,197
anthony: that some of the fundamentals of modeling data or creating um you abstractions from

450
00:33:03,448 –> 00:33:07,118
anthony: the data sources and ways that can connect to the business. I think some of that

451
00:33:07,368 –> 00:33:12,640
anthony: has certainly diminished in attention. Despite what we were just talking about with

452
00:33:12,724 –> 00:33:16,878
anthony: data engineering being recognized as super important. I see them moving in a little

453
00:33:16,878 –> 00:33:19,998
anthony: bit of a different direction, And I want to see what. What’s your take on the

454
00:33:20,081 –> 00:33:21,833
anthony: situation of data modeling today?

455
00:33:21,983 –> 00:33:22,984
tarush: Yeah, you

456
00:33:24,602 –> 00:33:27,638
tarush: think I think demolling is more important than ever. And

457
00:33:28,673 –> 00:33:32,927
tarush: what we’re seeing is with a lot of these tools like tableau, and these bi toools,

458
00:33:33,077 –> 00:33:36,597
tarush: which can directly connect your source systems and give you a level of insights.

459
00:33:37,248 –> 00:33:41,636
tarush: Those are great ster tools to get started when you don’t have these capabilities.

460
00:33:42,353 –> 00:33:47,642
tarush: But unless you, you know, if you don’t have that clean, distinct datamanding layer

461
00:33:47,809 –> 00:33:52,447
tarush: which is separate from your row data, lay, then what starts happening is your? for

462
00:33:52,597 –> 00:33:57,719
tarush: any new analysis or inside these tools, you are starting to have application logic

463
00:33:57,885 –> 00:34:02,757
tarush: inside of them, which means that Tablelo is now got some sort of application logic

464
00:34:02,924 –> 00:34:05,076
tarush: on top of your or data, which is what you report

465
00:34:05,393 –> 00:34:10,114
tarush: on. If you surface this data back to your customers, you have another copy of it.

466
00:34:10,281 –> 00:34:14,118
tarush: You start having a massive fan out problem, and every time you want to change one

467
00:34:14,202 –> 00:34:19,640
tarush: metric, you not have to change it in ten different places, And that is just software

468
00:34:19,707 –> 00:34:20,708
tarush: engineering. We have

469
00:34:22,760 –> 00:34:23,878
tarush: that this is data

470
00:34:25,079 –> 00:34:30,118
tarush: and that’s you, very, very significant. and it’s just like building a skyscraper.

471
00:34:30,368 –> 00:34:34,355
tarush: Right, You have to build. You have to dig up the earth and build a foundation. If

472
00:34:34,355 –> 00:34:38,593
tarush: you want to build a skyscraper. Now, if you’re serious about getting value from

473
00:34:38,760 –> 00:34:44,849
tarush: data, you, ignoring your datamanding, lay and directly and going, you know. going

474
00:34:45,083 –> 00:34:49,954
tarush: back to your row data directly allows you to build a few stories.

475
00:34:51,322 –> 00:34:54,675
tarush: You. You’re not really going to be able to goil build skyscraper. If you ignore your

476
00:34:54,675 –> 00:35:00,114
tarush: data Mar layer, So you know, in a world where we now want to go higher and higher

477
00:35:00,281 –> 00:35:05,553
tarush: and more you upstream, In terms of this pyramid, you know, Using Malo permits, needs

478
00:35:05,636 –> 00:35:12,527
tarush: for data data collection, ingestion storagening, reporting, um experimentation, Anl.

479
00:35:12,927 –> 00:35:17,081
tarush: If you choose to ignore your data modeling layer becomes really hard to eventually

480
00:35:17,248 –> 00:35:23,087
tarush: focus on data science and all of those areas, because you just have all of this debt

481
00:35:23,888 –> 00:35:27,008
tarush: in the middle of your pyramid, which becomes very expensive to maintain.

482
00:35:27,992 –> 00:35:31,279
anthony: I, I completely agree with you, and and I’m I was hoping that that was the

483
00:35:31,362 –> 00:35:35,283
anthony: direction that you werere going to go in. Um. And and I and I use the analogy I

484
00:35:35,366 –> 00:35:39,437
anthony: like the analogy of of creating that skyscraper. I think it’s it’s you know. The

485
00:35:39,687 –> 00:35:43,674
anthony: the classic analogy is is creating this house and it’s not about a house anymore.

486
00:35:43,841 –> 00:35:47,762
anthony: It’s about creating the skyscraper. It’s a it’s Um analogy. We’ve been using Um in

487
00:35:47,845 –> 00:35:51,032
anthony: my circles for a little while, and I, I think it works really well, so I. I’

488
00:35:51,516 –> 00:35:56,954
anthony: thrilled to hear you. Uh, talk about that. Um. I also would argue that I think that

489
00:35:57,121 –> 00:36:02,159
anthony: sometimes you can see syptoms of the lack of certain capabilities in other areas. I

490
00:36:02,243 –> 00:36:08,966
anthony: would argue that the lack of data modeling has led to a lot of challenges when it

491
00:36:09,033 –> 00:36:13,838
anthony: comes to master reference data in in organizations, because the lack of modeling on

492
00:36:13,838 –> 00:36:18,559
anthony: the front end creates what ends up being du. duplicate. copies and variant copies

493
00:36:18,726 –> 00:36:22,880
anthony: is of different sets of data that should be managed more collectively. And if you

494
00:36:22,964 –> 00:36:26,484
anthony: don’t have decent data modeing, you’re not going to have decent master data and

495
00:36:26,484 –> 00:36:28,152
anthony: reference state of management. And so you

496
00:36:28,402 –> 00:36:32,874
anthony: s startar to see these things trickle out into big problems. Because if if you

497
00:36:32,957 –> 00:36:37,445
anthony: don’t solve for that you, you’ve got challenges to reconcile data that needs to to

498
00:36:37,528 –> 00:36:38,796
anthony: work across different systems.

499
00:36:39,163 –> 00:36:44,518
tarush: hundred percent, I think you know. Um, I refer to that as having a single source of

500
00:36:44,602 –> 00:36:49,957
tarush: truth, and you know not having a, not having a single, a single maring layer, which

501
00:36:50,041 –> 00:36:54,595
tarush: powers then, B. I, machine learning, or of these other, All of these other layers

502
00:36:54,929 –> 00:36:59,233
tarush: leads to having multiple sources of truth, and very practically multiple sources of

503
00:36:59,317 –> 00:37:03,321
tarush: truth is probably the worst thing for companies, because what happens is that

504
00:37:03,554 –> 00:37:07,008
tarush: finance looks at a number and sales looks at a number, and these numbers don’t

505
00:37:07,241 –> 00:37:12,763
tarush: match. And for a data team, you know, you lose all opportunity at that point to

506
00:37:12,914 –> 00:37:17,568
tarush: explain that we were coping the data two different, D, two different data sources,

507
00:37:17,718 –> 00:37:20,288
tarush: and that finance went ahead and change some of those numbers on an Excell

508
00:37:20,438 –> 00:37:25,009
tarush: spreadsheet and sales the same. Hence those two numbers don’t match. You never get

509
00:37:25,159 –> 00:37:28,996
tarush: an opportunity to come out of that you, you know at that point instantly just

510
00:37:29,246 –> 00:37:33,634
tarush: becomes the numbers don’t matches. We don’t trust the data team and every department

511
00:37:33,884 –> 00:37:38,205
tarush: becomes their own gatekeeper, and those are tricky, tricky problems to reallyly come

512
00:37:38,356 –> 00:37:44,278
tarush: out of, and it all comes back to you know, having a consistent data modeling layer

513
00:37:44,679 –> 00:37:50,284
tarush: which is agreed on, and then the businesses you know, using that single source of

514
00:37:50,284 –> 00:37:54,038
tarush: truth, and if anyone wants to change it, not changing it directly, but actually

515
00:37:54,288 –> 00:37:58,843
tarush: changing it inside the single source of truth, you know. For people in the data

516
00:37:59,026 –> 00:38:00,027
tarush: space, this seems really,

517
00:38:00,995 –> 00:38:04,448
tarush: you know fundamental, and I love the fact that you sort about this up. but

518
00:38:05,633 –> 00:38:10,838
tarush: given what you know, modern tools allow us to bypass this and get to insight. you

519
00:38:11,005 –> 00:38:16,594
tarush: directly. Um. these symptoms of the surfaces sort of actually surface in these in

520
00:38:16,761 –> 00:38:22,600
tarush: these other ways, and at that point you know companies feel extremely helpless in in

521
00:38:22,767 –> 00:38:25,086
tarush: being able to execute their own dataies.

522
00:38:25,753 –> 00:38:31,192
anthony: I, you’re right and that that notion of data trust right. That comes back to when

523
00:38:31,275 –> 00:38:34,879
anthony: we talked about data leadership and how it’s about driving action. Well, if you

524
00:38:34,879 –> 00:38:37,765
anthony: don’t trust the date, if you don’t trust these analytics that you’re getting,

525
00:38:37,999 –> 00:38:42,803
anthony: you’re not going to take new action based on them, and then you’re going to kind of

526
00:38:43,287 –> 00:38:48,559
anthony: wither away the entirety of that argument that that you made earlier around. Hey,

527
00:38:48,793 –> 00:38:53,848
anthony: every organization needs this data analytic’s capability to drive these actions to

528
00:38:53,848 –> 00:38:57,518
anthony: drive these innovations. Well, if you don’t trust them if you have them, it’s just

529
00:38:57,601 –> 00:39:00,488
anthony: as bad as not having them at all. so this is literally.

530
00:39:01,605 –> 00:39:07,595
anthony: The lack of data modeling can be traced to businesses that will fail. I think is

531
00:39:07,678 –> 00:39:11,599
anthony: what we’ve just strung together. and and I think it’s that important. Uh, because

532
00:39:11,999 –> 00:39:18,239
anthony: that data trust is so incredibly essential to getting people to take action based

533
00:39:18,322 –> 00:39:23,594
anthony: on that, especially when that action that that the data suggests may run counter to

534
00:39:23,844 –> 00:39:25,846
anthony: their intuition. And that’s

535
00:39:25,846 –> 00:39:26,847
anthony: the whole point.

536
00:39:27,665 –> 00:39:28,666
tarush: you,

537
00:39:29,066 –> 00:39:30,067
tarush: I think

538
00:39:31,569 –> 00:39:35,005
tarush: this. you know. This is a great example of something which comes with experience

539
00:39:35,406 –> 00:39:40,194
tarush: right, Like something like how to set up a data modeling stack, And what? and what

540
00:39:40,361 –> 00:39:42,363
tarush: happens when this is not done correctly.

541
00:39:43,564 –> 00:39:48,686
tarush: What we’re seeing is that the data stack like these best practices are no longer

542
00:39:48,836 –> 00:39:52,523
tarush: just in modellling, but they’re also haeens. Like you know, five years ago,

543
00:39:52,673 –> 00:39:55,409
tarush: companies will building their own pipelines and injesting data themselves

544
00:39:55,726 –> 00:39:59,480
tarush: and today, if you do that, you is not going to be able to focus on the other area as

545
00:39:59,713 –> 00:40:04,602
tarush: using fully managed data pipeline. And ▁l T makes so much more sense. We see these

546
00:40:04,769 –> 00:40:08,839
tarush: kind of best practices across all of the layers, right or all the way from data

547
00:40:09,006 –> 00:40:13,077
tarush: collection. How you fire events in the front, and how you track that rece, an

548
00:40:13,160 –> 00:40:16,847
tarush: ingestion layer near the average Sa, up as a set of ten to twelve different data

549
00:40:16,997 –> 00:40:21,802
tarush: sources, mapping that stock into your store. How you store it and row in your role,

550
00:40:21,886 –> 00:40:26,524
tarush: Lay, or how you build this data modeling there? And then how does the data modeling

551
00:40:26,674 –> 00:40:31,162
tarush: layer feed reporting or machine learning? And what are the best practices there?

552
00:40:31,245 –> 00:40:35,966
tarush: Where you have application logic you know. Going back to the first thing I said is

553
00:40:36,033 –> 00:40:41,639
tarush: the modern data stack is becoming really complicated and you know these are super

554
00:40:41,889 –> 00:40:47,561
tarush: nuanced things which no longer makes sense for you to go to yourself, right. We saw

555
00:40:47,728 –> 00:40:52,766
tarush: that we marketing where you know five years ago. We realize that just posting a

556
00:40:52,766 –> 00:40:57,004
tarush: picture on Instgram is no longer marketing and that if you really want to do digital

557
00:40:57,154 –> 00:41:01,408
tarush: marketing, you know, bring in experts who already know how to do it and you know

558
00:41:01,725 –> 00:41:03,644
tarush: that same analogy is with

559
00:41:05,563 –> 00:41:09,884
tarush: it. It just doesn’t make sense in in our opinion, for for like companies to go

560
00:41:10,034 –> 00:41:16,841
tarush: focused on all of these areas. Um, this. it’s so nuanced and the r y offer is just

561
00:41:17,007 –> 00:41:21,162
tarush: having a a modeling layer which you then anyway, need to build on top of group when

562
00:41:21,245 –> 00:41:25,249
tarush: it comes to decision making, or or insights or or sort machine learning.

563
00:41:26,333 –> 00:41:27,334
anthony: Yeah,

564
00:41:28,085 –> 00:41:32,406
anthony: I think that’s excellent point. And so we’re running out of time. I want to ask you

565
00:41:32,473 –> 00:41:33,524
anthony: one more question. So

566
00:41:35,125 –> 00:41:36,160
anthony: to ▁ot, a

567
00:41:37,761 –> 00:41:42,716
anthony: statement I first heard in an agile conference, I think many years ago is that

568
00:41:43,767 –> 00:41:49,039
anthony: as as difficult and as complex and as massive as the Dta vis and complexity and

569
00:41:49,039 –> 00:41:50,474
anthony: everything that we’re dealing with is today,

570
00:41:51,525 –> 00:41:56,313
anthony: it’s never going to get easier than it is now. And so as we look towards the

571
00:41:56,397 –> 00:41:57,915
anthony: future, what do you see

572
00:41:59,200 –> 00:42:04,722
anthony: evolving and coming next in like the next five or ten years in this space? Where do

573
00:42:04,722 –> 00:42:05,723
anthony: you see it going?

574
00:42:06,123 –> 00:42:11,645
tarush: Yeah, sure, so, I think, in the last five years the understanding on what the modern

575
00:42:11,729 –> 00:42:15,633
tarush: data stack is right across these five layers which keep talking, Da collection,

576
00:42:15,799 –> 00:42:21,155
tarush: andgestion modeling, Uh data collection in storage, bonding reporting has become

577
00:42:21,322 –> 00:42:24,842
tarush: fairly well understood. Right, who are the best players over there? We now have five

578
00:42:25,009 –> 00:42:28,529
tarush: billion dollar players across each of these spaces. Um,

579
00:42:30,047 –> 00:42:35,002
tarush: what you know what I see happening of the next few years is is layer six to ten

580
00:42:35,569 –> 00:42:39,723
tarush: start to get more predefined right, And these areas like reversityity are taking

581
00:42:39,957 –> 00:42:43,160
tarush: these insights from the warehouse, pushing it back into your source systems, data

582
00:42:43,394 –> 00:42:47,881
tarush: observ ability, a sort of lineage where Uh, being able to at track these different

583
00:42:48,198 –> 00:42:53,404
tarush: data sources, Um, just to make sure that if a job fails how to, we rerun them, or

584
00:42:53,804 –> 00:42:59,159
tarush: just knowing where they come from having meaddta around them, areas like machine

585
00:42:59,393 –> 00:43:01,328
tarush: learning obviously is going to be a big one.

586
00:43:01,962 –> 00:43:06,684
tarush: I think you know the best practices of what are the billion dollar players across

587
00:43:06,834 –> 00:43:10,521
tarush: each of these stacks. And really, how do they fit into the modern day data stack

588
00:43:10,754 –> 00:43:15,643
tarush: become? you know, extremely relevant in what we’re seeing in Uh Maturk. State of

589
00:43:15,643 –> 00:43:19,713
tarush: data. Twenty twenty, one, Which is something I really enjoy reading. Also help

590
00:43:20,047 –> 00:43:25,402
tarush: validate is that companies today want the flexibility. they don’t want to be locked

591
00:43:25,636 –> 00:43:30,441
tarush: into one platform. So the Amazons or the Google, you know ecosystem is not that

592
00:43:30,441 –> 00:43:31,442
tarush: compelling companies

593
00:43:31,475 –> 00:43:34,762
tarush: wa to use Snowflake for storage, and they want to use D B T. and they want to use

594
00:43:34,928 –> 00:43:40,434
tarush: Fivetrand, So you know there’s going to be a lot of innovation in these microlayers,

595
00:43:40,601 –> 00:43:45,005
tarush: not microas, But you know a single layer instead of platformizing, four or five

596
00:43:45,155 –> 00:43:50,277
tarush: layers at a time, And I think you know the. I think the next few layers and the and

597
00:43:50,361 –> 00:43:55,399
tarush: the and the vendors across those layers becomes more and more and more standardized.

598
00:43:55,482 –> 00:43:59,887
tarush: And you know we start. You know this starts to emerge. Uh, a sort of winner across

599
00:44:00,037 –> 00:44:04,758
tarush: some of these different layers. So as layers you know six to ten, and you know at

600
00:44:04,842 –> 00:44:08,529
tarush: this point it’s no longer hierarcharal. You know, after the Da of malding layer it

601
00:44:08,595 –> 00:44:12,116
tarush: all kind of becomes impalling like you do, reporting Imp pandem with machine

602
00:44:12,282 –> 00:44:18,205
tarush: learning and panel with reverse C. ▁l, so not really six to ten in terms of the

603
00:44:18,355 –> 00:44:22,042
tarush: hierarchy ructure, but the next Pa layers

604
00:44:23,077 –> 00:44:28,766
tarush: or of use cases sort of start to emerges trends, And I think one of those layers,

605
00:44:29,483 –> 00:44:33,637
tarush: Um, is what Five Ex is focused on is really assembling the data Sta.

606
00:44:34,838 –> 00:44:38,208
tarush: This is going to be. This is going to become another layer. as the da’ Sta becomes

607
00:44:38,359 –> 00:44:42,679
tarush: so much more complicated. And you realise we need we, ten different vendors, or

608
00:44:42,913 –> 00:44:46,517
tarush: they? You know we, We strongly believe this is going to be this room of a company

609
00:44:46,917 –> 00:44:51,088
tarush: like us, which standardizes and gives it you as a single offering. That’s one area

610
00:44:51,238 –> 00:44:55,159
tarush: which obviously extremely bullish on, but there going to be more and more layers

611
00:44:55,242 –> 00:44:58,679
tarush: over there. And for the layers which have now been established, we’re going to start

612
00:44:58,679 –> 00:44:59,680
tarush: to find.

613
00:45:01,565 –> 00:45:05,803
tarush: We’re starting to find winners and sort of factor champions of those layers, which

614
00:45:06,036 –> 00:45:10,040
tarush: of Emerg, and start to sort of marketal

615
00:45:11,241 –> 00:45:12,276
tarush: those in those areas.

616
00:45:12,960 –> 00:45:17,364
anthony: You know every now and then as a podcast host you, you. thank yourself for asking

617
00:45:17,514 –> 00:45:21,518
anthony: the question that you as And that was one of those times that was awesome. Like I,

618
00:45:21,518 –> 00:45:26,407
anthony: I really like the way you characterize some of these additional layers, and I think

619
00:45:26,473 –> 00:45:31,762
anthony: you’re right. I think you’re absolutely spot on with where this likely heads,

620
00:45:31,995 –> 00:45:35,599
anthony: because we’ve seen patterns of maturity and I was thinking like the accordion

621
00:45:35,766 –> 00:45:40,237
anthony: expands and contracts. We see these repeating cycles, but I totally agree. I think

622
00:45:40,320 –> 00:45:42,406
anthony: that you’re going to see more clearer

623
00:45:43,340 –> 00:45:44,341
anthony: stages of

624
00:45:45,275 –> 00:45:49,997
anthony: that evolution of data and and subsequent to those five stages that exist today. I

625
00:45:49,997 –> 00:45:54,234
anthony: think that’ I think that’s brilliant. I think great great insights to close the

626
00:45:54,234 –> 00:45:57,755
anthony: show with, and and we are definitely out of time now. So, to thank you so much for

627
00:45:57,838 –> 00:46:00,808
anthony: being on the show with us, I really appreciate it. This has been amazing.

628
00:46:01,008 –> 00:46:04,178
tarush: Thank you so much. Thank you. A Have mean. it was so fun. It was so fun chatting

629
00:46:04,178 –> 00:46:05,179
tarush: with Yestday, morning.

630
00:46:05,679 –> 00:46:08,632
anthony: absolutely. and thank you all for joining us today. You’ll find more information

631
00:46:08,966 –> 00:46:12,886
anthony: and links in the show notes. Dive deeper with my book at DataLeadershipBook.com

632
00:46:13,036 –> 00:46:17,357
anthony: Com and use promo code “ALGMINDL” at the DATAVERSITY Online Training Center for twenty

633
00:46:17,524 –> 00:46:20,878
anthony: percent off your first purchase. And if you enjoy our show and would love your own,

634
00:46:21,044 –> 00:46:24,565
anthony: but don’t know where to start, visit Algmin.com to learn how we make having

635
00:46:24,715 –> 00:46:28,635
anthony: your own video podcast as easy as joining a call and sending an email. Stay safe

636
00:46:28,886 –> 00:46:31,355
anthony: during these unusual times and go make an impact

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