Data Strategy, Data Monetization, and More with Vin Vashishta, Founder and Technical Advisor at V Squared

Data-Driven Podcast

Vin Vashishta talks about his experience working in the data industry for over 20 years. Vin has helped organizations make data-driven decisions and achieve their business goals through effective data management and analysis. Vin spoke about data strategy, data monetization, and the future of data.

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Data monetization’s an interesting concept because data is a unique asset. So that’s the piece that businesses haven’t come to terms with. They still look at it as a digital asset. And it doesn’t work that way from an evaluation standpoint. Data’s never tapped. If you think about the metaphor, metaphors, the new oil, right.

First things first, get technology out of the driver’s seat. I keep hearing even large companies, you know, best in class hyperscalers, you’ll have somebody from them saying, you know, technology should be the driving strategy. It’s absolutely not. No. I’m on the technology side. If anyone should be an advocate for technology driving strategy, it should be me. But it doesn’t work.


Dave Mariani: Hi everyone and welcome to the AtScale Data-Driven podcast. Today, I have a special guest. It’s Vin Vashishta. And VIN is the founder and technical strategy advisor at V Squared. Vin, welcome to the podcast.

Vin Vashishta: Thanks for having me. Appreciate it.

Dave Mariani: Thank you for joining us. So, I always like to start out our data-driven podcast with, with the same question vi, which is, I get the opportunity to talk with some really amazing data leaders and it’s always interesting to hear about the background and how you got to where you are today. So, can you tell us a little bit about your journey to, and, and how you got to be a data guy

Vin Vashishta: Yeah. I started out wanting to be a data guy and ended up not being able to, cuz I was in that first wave when I went to college. We had the first wave in the nineties where everyone thought AI was coming. And so I went to school, got a research grant from Microsoft to teach us some data science and machine learning. And then it fizzled. Didn’t make sense because the technology wasn’t there. The interest wasn’t there, there wasn’t enough data there. It just all of the little pieces that we needed. So I went into traditional software engineering, played pretty much every role in the software development lifecycle I built and led cross-functional teams. And then about 2010 kind of came back around. Mm-hmm. started seeing the potential of data, bi low level analytics in 2012 started vs squared, finally got into data science, almost 20 years after first intended.

Dave Mariani: Wow.

Vin Vashishta: . Yeah. It was a long and ugly trip.

Dave Mariani: So, so we, so yeah. Were you a little bit reticent about sort of dipping your toe back into the water after the first time around

Vin Vashishta: Not really. I was, you know, it’s interesting. I had a lot of use cases around customer behavioral analytics. Mm-hmm. from marketing, because I’d done a project in 2010 and 2011 where we had taken a ton of data from the casino gaming industry, player tracking. And we had found some very interesting things in those data sets about how customers moved around casinos, why they chose the machines that they chose. There was a lot of analytics that had never been done before. And so I saw the potential and started pitching companies in 2012. That’s how I landed my first clients. It didn’t end up being in marketing, but it was supply chain because those people in manufacturing and supply chain, they were used to being data driven. So the message made sense to them. Got some early clients, finally got into the marketing side and the behavioral side.

Vin Vashishta: Mm-hmm. figured out about 20 14, 20 15, I couldn’t do any of the cool stuff unless I got sea level buy-in unless I started talking to people in the business, figuring out what they actually needed. Learned some from some very, very smart scientists, people that had done rigorous modeling outside of the data science world, how to build for higher reliability use cases. And that’s where my arc sort of landed me is that about seven, eight years after initially playing around with data strategy, AI strategy, data, product strategy, applied machine learning research, now I’m full-time. I’ve taken my feet off of the development world in the model engineering world, and I’m full-time technical strategy data and AI specifically. It’s been a journey. It’s been fun though.

Dave Mariani: Yeah. You know, it’s, take us back to, you know, the days of the early days when you sort of dipped your toe back in, especially on the casino, or on the gaming side, casino gaming side because, I think we all, we all, we all started, started to recognize, you know, you, you know, if you, if you do go and, and gamble, mm-hmm. , they definitely encourage you to register. Right. use your card. Yes. Yep. Your loyalty card. and it’s the same thing with grocery stores mm-hmm. . So obviously that data’s pretty valuable. so, you know, how, what was that like And so how did you, and, and what kinds of things, because obviously it became very popular, but, so how did you get from raw data to, to actually actionable insights were, you know, these companies were changing their business. Tell us a little bit more about that.

Vin Vashishta: Well, player tracking old, the player tracking cards have been around since the early two thousands. And even before that it was, I mean, there were old school cardboard cards that they kept behind the desk and the cage where they would track their biggest players in their whales. So this has been around forever. The data has been just sitting there. No one used any of it, none of it at all. All that we’re doing was tracking play to figure out comps mm-hmm. and how many, you know, how much comps they should be giving to each one of the players. How were they playing? What were they spending? We’re doing some analysis on how slot machines individually performed, but that’s it. And it’s, so, you know, a decade later, we’re still in the same boat where companies have a lot of data and there’s value locked inside of it.

Vin Vashishta: Yeah. But they’re focused on a very small number of use cases instead of really exploring it and getting more value out of it. Casino gaming’s heavily regulated and it’s siloed. So one casino has its own data and it’s not sharing that data with anyone for any reason. I mean, that player tracking data was gold. And the system that we implemented brought in data from across casinos, which is the first time that had ever been done. Wow. And so we were able to follow players on our company slot machines around Las Vegas and around the US eventually we got in internationally. And so we were able to track the same players where they would play at home or in casino properties near home when they would go on vacation to Vegas, or if they would take, you know, some of those types of gaming trips that some people do.

Vin Vashishta: We were able to track their behaviors and it was novel because we had an, and that’s what kind of got me into understanding the value of a novel dataset. Mm-hmm. versus the dataset that anyone could have access to because everyone had access to their data on their property. There was nothing new there that they were learning, but we had access to something that was absolutely novel, and that’s where the business value came from. That’s where all of the use cases started to trickle in. Casino owners hated it at first, and then they said, wait, can we get access to that data? And all of a sudden they just a whole lot more because we’re providing ’em insights about their players that they couldn’t get anywhere else. And that’s, even today, that’s the true value of unlocking data, is getting through either figuring out how to build a platform that gathers data that nobody else has access to, or doing the experiments that create novel data sets. And now you have something that no one else can replicate. So that’s the, I mean, that’s the foot in the door. And it’s still true with almost every company today. It’s not going straight to ai. It’s really starting with novel data sets and then building forward from there, eventually getting to more complex models.

Dave Mariani: I I love this concept of a novel data set. So when I was at Klout, you know, we used to, Klout was a company that would create a klout score, which would basically be your social media score. Oh

Vin Vashishta: Yeah. I used to have one of those. Yeah.

Dave Mariani: Yeah. So what was your, what was your score been you

Vin Vashishta: Remember Top that I ever got to was like 87 or something like that. I was, whoa,

Dave Mariani: Dude,

Vin Vashishta: That’s, yeah. I was up there for

Dave Mariani: A while. Yeah. That’s celebrity status. I was like a 46 or something, and I was the, I was the, the VP of engineering there. but, I could tell you that, the novel data step there was that we would collect social media data across the social media sites. And so the novel data set is that we could actually see you on Twitter and, on Facebook and, and, Instagram for example. Mm-hmm. and that was very powerful and very valuable. And then you could see that today with like, I know there’s companies out there that are selling data, like for foot traffic in, in different, in different businesses. and I know that, especially during the pandemic, a lot of companies used that foot traffic data to judge demand so they could like, you know, they could, they could get ahead of the supply chains, supply chain nightmares that they were experiencing. Yep. So, I love that. And you talk about them in your, a lot of your writing, you talk about data monetization. You talk about, you know, how important it is for companies to be able to value the data that they have. So it seems like that’s the, that creating novel data sets is a key there. But can you talk a little bit more about what you mean by data, a data monetization strategy and what that’s what that means to people out there who should know what that means.

Vin Vashishta: Everyone should know what I’m talking about. It. Should they all read my blog

Dave Mariani: Teach me. Teach me. I don’t

Vin Vashishta: Yeah. No, I, they used to have an 87 klout score. Everyone should know me.

Dave Mariani: I know that’s a, like I said, Justin Bieber was, Justin Bieber was a hundred. So you weren’t too far away from Justin Bieber that then,

Vin Vashishta: I can’t remember what year that was. I think it was 2020 15 or

Dave Mariani: Previous. it was, it, it would’ve, it would’ve been 2012, which is, I started AtScale in 2013. So that was, that, that was,

Vin Vashishta: So I was after your time. That was after you left. Yeah.

Dave Mariani: Okay.

Vin Vashishta: Data monetization is an interesting concept because data is a unique asset. Mm-hmm. . So that’s the piece that businesses haven’t come to terms with. They still look at it as a digital asset. And it doesn’t, doesn’t work that way from evaluation standpoint. Data’s never tapped. If you think about the metaphor, metaphors, the new oil, right. But I mean, you use a barrel oil, it’s gone. Mm-hmm. , you use a terabyte of data, it’s still there. Mm-hmm. , you can build as many models, you can deploy as many data products as you can come up with use cases for. And the business has capabilities of developing, deploying, and supporting. Mm-hmm. . So that’s the interesting piece about data and data monetization. When you start looking at what’s the most valuable data that the business has, companies start to figure out very quickly in that initial audit, most of the data they have is unusable.

Vin Vashishta: Mm-hmm. , they’re paying a ton of money to gather it, clean it, store it, and they don’t get any value out of it. Mm-hmm. , the amount of data that a business needs is significant. It’s massive. But the way that businesses gather data today is unintentionally undirected. And it needs to, people need to look at it as an asset before the business begins to say, okay, this is worth investing in. Because our investment strategies at businesses are, we’re going to build data science teams, we’re going to have data scientists come in, and if we get the talent and we buy some software, we’re good to go. And technology does not immediately become valuable on its own. Mm-hmm. , it has to be connected to business value in some way, shape, or form. And that’s really the use cases. What are we gonna use this for What kind of business value should we be creating with the data

Vin Vashishta: What data is the most valuable to be gathering from a customer workflow standpoint, from an operational standpoint, from a decision support standpoint, what can we use to support improved productivity, reduce costs, and begin to deliver value to customers in new ways Use data, leverage it really for growth and data alone, just by itself. High quality data sets are extremely valuable from a decision support standpoint. Basic automation, even delivering value to customers, customers will pay for inference. If you look at products like Copilot mm-hmm. That’s a massive dataset that GitHub and Microsoft had. They had access to it. And combined with, you know, they partnered up and were able to very quickly deliver a product to market that people are now paying for. They put it into beta, it was so popular, now they’re charging people for it, and it’s a revenue generator. And so those are the types of progressions where you identify business goals.

Vin Vashishta: First, it’s the business saying, this is what we should be doing. Here are the opportunities we want to pursue. And then looking at technical strategists and data strategists and saying, okay, so what do we have right now Or what data do we have easy access to that can support these opportunities in these use cases Now we’re connecting business value to data sets where, okay, your data strategists can look at it and say, these data sets will support these use cases. We can use basic reporting. In some cases we can use basic descriptive analytics. And if necessary, if we have the higher reliability requirements necessary to really justify machine learning experimentation, that applied research cycle, then we go in that direction. But the majority of use cases, I would say maybe even 90% of use cases can be handled by basic descriptive models or simplistic reporting. They could almost be handled self-service. So the value of data is, it’s very easy to unlock if you have the strategy component and if you understand how to position it as an asset.

Dave Mariani: So, so Vin, how do, how do companies even get started with this Because I hear you on the use case cuz if it’s a use case, if it’s a use case, there’s, it’s a, it’s a probably be, it’s a business use case and it’s gonna be attached to some kind of value. But, you know, how do you herd the cats? I mean, companies are big, there’s a lot of competing priorities. people are already doing stuff with data they don’t even know they have, they haven’t quantified it. They don’t even know what they have. So what are some of the sort of the steps that these companies can take to, to get to where you’re, where they’re, they are use case driven and they can, you know, tie their investment to a return

Vin Vashishta: Yeah. First things first, get technology out of the driver’s seat. I keep hearing even large companies, you know, best in class hyperscalers, you’ll have somebody from them saying, you know, technology should be driving the strategy. It’s absolutely not. No. Mm-hmm. , and I’m on the technology side. If anyone should be an advocate for technology driving strategy, it should be me. But it doesn’t work. What you end up with is more technology, not necessarily more value. I mean, the purpose of technology should be to serve value, but if you look at the nature of technology, it serves itself. It’s self perpetuating. And that will happen in the organization if we don’t have someone at the strategy level who is driving the technology. So traditional strategy planning is changing because we’re introducing data, we’re introducing a better understanding of customers, the marketplace competitor’s opportunity discovery is changing because that can be data driven as well.

Vin Vashishta: And so if the company has the right culture, if the company is willing to use data and make decisions, make changes based on data, then strategy planning can be significantly more effective. So it starts with culture. You need a data strategist because if you don’t have someone with the technology background and the strategy background, that opportunity discovery gets really, really murky. Yes, you need leaders, you know, data leadership who are able to lead strategy and connect the strategy that’s built to execution. Building it out, really taking it to taking it to customers. You need the product management and the project management layers. There’s so many overlooked roles where we hire a data scientist, a data engineer, machine learning engineer, somebody for ML ops, we buy software. But that’s only a very small part of the picture. It’s much more of an enterprise wide effort. And the

Dave Mariani: Business driver, let’s, let me, let me just quickly cause this, this, questioning on that, because organizationally, right Mm-hmm. , you talk about culture and, you know, culture is, you know, really does, has to come from the top really, you know, in terms of, your, your leadership team and the like, but you know, so what does, you know, so tell me about organizationally. Where does that data strategist live Where do these roles live Because, you know, is it in the business? Is it centralized? Is it, is it at the management level? Is it a CDO? Is it an Op? How’s it work operationally?

Vin Vashishta: From an operational standpoint, what you need is you to start off with everything scattered. You know, we have to meet businesses where they are. We kind of have to admit that infrastructure’s all over the place. They probably have some data resources. There may be some of them focused on one team. You may have analysts scattered throughout the business. You may have engineering resources all over the place. You may, may even have some in it. Step one is just doing an assessment, doing that basic audit. What data do you have Where does it live? What data has got value to it Build out a data monetization catalog. Begin to create a strategy. But the first strategy you have to create is the continuous transformation strategy. Because the business isn’t just going to do this one time transformation. It’s going, it’s surfing waves now of technology. And so each one of these technology waves, when we adopt it as a business, we have to make changes to the business model, the operating model.

Vin Vashishta: And we need a plan for that. And it can’t be just, what are we doing this year It’s, what are we doing in two years What do we anticipate even though, you know, five years, nothing’s certain, but what do we anticipate we’re going towards What do we think will be important in three years and five years And so with that plan, now you can move forward. Now you can sort, because you’ve admitted the whole business is changing. Mm-hmm. The customer expectations are changing. And so that’s where you started, like you said, it starts at the top. Mm-hmm. . What’s also critical, and it’s interesting, you see these early adopters. Disney’s a great example. They went back to Bob Iger recently. Why mm-hmm. because from an operational standpoint, the danger of technology transformation is that the business’s culture gets lost. So it’s not just a culture of data, it’s also tying it back to where the business is.

Vin Vashishta: Great. What is the purpose and vision that has gotten the business to that point? So C-level leaders have this balancing act, and so you need somebody who’s at the CDO or CDA O level mm-hmm. , who owns the vision for data. You need a technical strategist who also lives in that data organization. And so now you’re hearing this. Okay, so it sounded like a centralized organization. Mm-hmm. , it has to be, that’s part of the transformation strategy is to say mm-hmm. , we’ve got all of these resources all over the business. Part of the transformation is we need to centralize them. Why? Because we have a low level of data maturity. And if we want to accelerate that progression, if we want to get to a higher level of maturity faster, we need everybody that’s doing data in one place, figuring out how to do it quickly, efficiently, get the right people in the room together to figure out the technical side of this challenge.

Vin Vashishta: But we’re also figuring out the business side of this challenge. We talk about the AI last mile problem, and everyone’s focused on, okay, we’re gonna build a center of excellence and they’ll produce value. Well mm-hmm. , they’ll produce great technology mm-hmm. , but there needs to be that connection back. So part of the AI last mile problem is actually a first mile problem. Mm-hmm. and getting that strategy in place and creating a connection. Continuous transformation does this between the data team and the rest of the business. Because the data team does things often for technology reasons. The rest of the business doesn’t care, care, like centralizing data into one place. The rest of the business couldn’t care less about that until they realize their initiative’s gonna get done this year instead of next year. Because we are more efficient as a data team with data in one place, being able to do things one time instead of five times, then all of a sudden it’s value centric.

Vin Vashishta: Oh, okay. I get it. So it is, you know, you’re totally right. It’s top down, starts with strategy. Strategy drives technology, you have a centralized organization. Mm-hmm. , the reason why you do all of this is built and distributed to the rest of the organization through data strategy and AI strategy. There’s literacy training that needs to happen. People need to be data literate and model literate because they’re gonna express requirements in ways that they’ve never done before. Mm-hmm. It isn’t how does this need to function? It’s how reliably does this need to work in order to meet my business needs? Because models don’t have, you know, hardcoded functionality, they have levels of confidence and reliability, and that’s a different kind of requirement. And I mean, you’re hearing how big this gets. It’s, this is a massive undertaking.

Dave Mariani: Yeah. And what I’m hearing you saying is that, like, having a C-level, a c-level executive owns data and analytics is key for this to even happen because the strategy needs to be set there. Mm-hmm. obviously that, that role is important to work with the rest of the business to figure out what those use cases are, that they’re gonna define that strategy and then define the investments. but I, I, I I don’t know that, so the question I have for you is then when it comes to, it seems like self-service is very important. Data literacy drives self service. So you’re not saying that central data organization is, is, is doing as analytics for the business, but really, is it, is it an enabler and setting the strategy for how you do analytics for the business So what’s, what, what say you on that What, what’s, what, what part of that is, which, which side is of the equation Do you fall on that

Vin Vashishta: It’s an arc because the reason why we have distributed resources in the first place is because we want data talent to be close to the business units that they serve.

Dave Mariani: Right.

Vin Vashishta: The early drivers are very simple use cases. They don’t take a lot of work to do. So those are initially gonna be handled by the data team, but the goal is to eventually hand it back over. Mm-hmm. , that’s the purpose of putting all of these best practices in place. Because when they’re not trying to hand over a self-service tool it is just not going to work as a self-service tool by itself with terrible data, no processes in place, no governance, security, I mean, just, it becomes a nightmare. And mm-hmm. The scary thing is users can adopt a self-service tool and trust this data, which ends up being low quality, leads ’em in the wrong direction, and they lose trust for data, data science, and analytics in general. They’ve had some bad experiences. They get a bad taste in their mouth and they don’t want to try it again. Mm-hmm. . So it’s important for the best practices to be in place, but if the data team is handling all of the analytics, all of the reporting, they’re not doing the highest value generating activities that they could be doing. Like I said, 10% of use cases really require data science, machine learning. And if the data team is stuck doing the 90% mm-hmm. , number one, it’s inefficient. Number two, you can’t, it, there’s no cost justification for it. And a lot of companies are running into scale,

Dave Mariani: Scale about, yeah.

Vin Vashishta: You can’t scale. I mean, try scaling a data science team to handle all of the businesses data needs. It’s impossible. It costs more because those initiatives don’t have the ROI to justify high-end resources. And so it’s this concept of best practices that can then start sending those tools, the self tools out to each one of the organizations so that they can serve their needs if they understand what they need better than anyone else does. Right. And an organization can serve themselves with one or two analysts supporting them. Mm-hmm. , and that’s part of the data and model literacy training is they are, they have someone who’s doing oversight for them, who’s kind of checking their work, who’s watching what they build and what they do, and saying, oh, okay, so I, in this case, what you actually needed to do was, and so now one way business knowledge is flowing into the data team because you have these people who are team facing, who are supporting them every day, who are seeing their needs, their applications, and the knowledge is going the other way. You’re getting data literacy training, you know, obviously there’s that initial class or seminar that you’ll send people to for data literacy and then for model literacy. But this is sort of that every week: reinforcement, continuing education, working with somebody who’s an expert and slowly they’re ready to take on more and more. And so your data science team takes their hands off of the things that they shouldn’t be working on. Mm-hmm. suddenly the ROI on that center of excellence, that data organization shoots up because they’re just doing the highest returning projects.

Dave Mariani: So vin, it sounds like you’re describing a hub and spoke style where the hub is the central data team. They’re responsible for, you know, for that strategy for, you know, for enabling the business through, you know, helping drive data, data literacy, and self-service. and the spokes are the business who are, you know, who are hopefully being enabled to do a lot of this work themselves. Now that’s sort of like in contrast with the strict definition of data mesh, which is decentralization, which…they don’t, data mesh doesn’t talk about a center of excellence or that central data team. Seems kind of crazy to me. So it’s, so what do you think about this whole talk about data mesh Do you think it’s, do you think it’s overblown Do you think it’s too decentralized or do you think that, that we should be calling it at a hub and spoke So can you talk a little bit more about that organizational style and, and the kinds of things people are talking about in that realm

Vin Vashishta: Well, data mesh is, so they’re assuming the business is where they want them to be. Mm-hmm. , they’re assuming the business is, you know, stage four maturity on its way to stage five. That’s not where businesses are. Yeah. It’s an arc. It can’t totally, you can’t just take the business, pick ’em up and turn ’em into meta or turn ’em into Google or turn ’em Amazon. That doesn’t work. Mm-hmm. , that’s not where most businesses are. Most businesses are at that level zero, level one maturity. Mm-hmm. and they’re beginning their journey. So yeah, I would love to just put everybody at the end state , but that’s not how this works.

Dave Mariani: I, I really, I really like That’s a, that’s a great answer. because it’s, because it’s not an either or, like you said, it’s an arc. It’s a, it’s where you are on the maturity curve and you’re saying that, you know, maybe if you get to a four or five a data mesh might work, but, but most companies are at zero or a one and you need to have somebody driving the bus.

Vin Vashishta: and data mesh can work at every phase, but you’re not implementing it. And I think we do this with Scrum and Agile too, where we say it’s all or nothing. And no, you begin the journey of maturity by picking what works now and moving your way into adopting something that’s common sense. You are going to adapt to the business model. There isn’t a single technology solution that will 100% fit the best business model. The one that will give you those competitive advantages that allow you to be more efficient, more productive, and deliver value in a way that your competitors can’t. There isn’t. If there was a one size fits all solution for that, everyone would’ve already adopted it. This would be simple. If there was a rule book and all you had to do was follow the rule book A and you’d win, then everyone would be, that’s the complexity of this is it requires a common sense approach.

Vin Vashishta: We can’t just tell the business, Hey, you’re AI first. They’re not, most businesses are never going to be AI innovators. They’re going to use it for competitive advantage. They’re not going to become AI first. They’re going to be AI smart. And I wish we were more realistic, in the technology world instead of rigid. We can’t hold the business to an unrealistic standard, but we can can help them walk down the path, kinda take that arc and get there eventually. Because that’s what happens with the center of excellence. Eventually it breaks up that phase three when we begin to, you know, we’ve got data science working when the infrastructure is in place, high reliability, we have the talent, we have processes documented, we have standardized workflows and tools. And when things are humming, then you decentralize mm-hmm. You actually break the team back up and do what we know was right.

Vin Vashishta: But we had to accelerate transformation by creating a center of excellence. Then you distribute the team so that resources are in the product teams that they support or the functional teams that they support. Cuz the closer you are to the need, the better solutions you’re going to create. But if you start that way and try to make it work from that direction, transformation’s way too expensive. It moves too slowly. Mm-hmm. knowledge doesn’t get across the organizations. You kinda get these data silos, you have worse practices in place and you don’t get the innovation side either. Because when you’re in these functional teams, they’re very tactical. They’re focused on what’s happening now, what are the deliverables Next week or next month, when you do the center of excellence, you begin to build a culture of innovation. Mm-hmm. And so even frontline organizations understand that the majority of their initiatives, you know, 80% are going to be looking at three to six months. They better be delivering in that 90 to a hundred day 180 day threshold. But there is a future. The company also must protect. And so innovation’s a critical part. You know, you can’t save your way into growth. And so you do have to spend intelligently on efficient innovation. And, and that’s the, you know, that’s the arc. Companies that want to be innovative with AI tomorrow don’t work that way. It’s a progression.

Dave Mariani: I think that sucks. That’s super good advice for everybody. I could tell you that I’m of a like mind there. I do think a center of excellence is really key, because it is where you can, you can define and drive that strategy and, and really drive the culture of, self-service and a data-driven, culture in your company. You’ve gotta start somewhere and it needs to be, at a seat at the table with, you know, with the other C levels cuz it’s, data is, is as important to, and it’s important to every function in the company. So, it should have a seat at the table. So, I I love that Vin. I couldn’t agree with you more and I think it’s really great advice. It’s pragmatic advice for the listeners out there. So, you, you, you’ve articulated it really well. So, we’re, we’re wrapping up here on time. I’ve, I’ve, I’ve taken too much of your time, but I always love to ask Vin a little bit about a prediction for the future so you can go anywhere with this, when it comes to data analytics, data science. So what, what do you think, what do you think that the, data leaders out there should be thinking about in the three to five year timeframe, of things that are gonna gonna really disrupt or change their lives potentially

Vin Vashishta: I think you have to watch out for what I call the, the thano snap, the great business die off that’s coming. And this is really relevant three to five years. I think it’s more like three years where you’re going to see half of businesses either become zombies or go away altogether. And data is a competitive advantage. Machine learning is a competitive advantage. If you can get to the point where you’re using causal methods to determine what KPIs you should be focusing on. You learn faster, you iterate faster, you fix problems with strategy, operations, products so much faster. Mm-hmm. , you can’t keep up with that. Companies that get there first are going to have such a competitive advantage and the window to catch up will be narrower than it’s ever been. We feel like this is the fastest we’ve ever moved. This is the slowest business will ever move again. And so I think if leaders are looking out for something, it needs to be, are you moving fast enough? Are you creating value? Not just creating technology and watching for these emerging trends that are disruptive. You’re going to see companies like Google disrupted by machine learning. It’s not just going to be, you know, what we consider legacy business. It’s everyone who’s vulnerable to how fast all of this will move forward and how well some companies are going to execute versus others.

Dave Mariani: Yeah. We saw sort of a, maybe a preview of, of some of that disruption during the pandemic. couldn’t agree with you more that, you know, it’s, data is a differentiator and it’s becoming more and more important. So that’s great advice, for everybody out there. Vin, you’re awesome. I love your strategic thinking. I love how you were so articulate and I learned a lot and I’m, I hope everybody else has learned a lot on the podcast. So thanks a lot for joining us.

Vin Vashishta: Thanks for the opportunity. I appreciate it.

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