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Monetizing Data & AI with Vin Vashishta, Founder & AI Advisor at V Squared, & Tiffany Perkins-Munn, MD & Head of Data & Analytics at JPMC

Richie, Vin, and Tiffany explore the challenges of monetizing data and AI projects, the importance of aligning technical and business objectives to keep outputs focused on core business goals, how to assess your organization's data and AI maturity, why long-term vision and strategy matter, and much more.
May 2024

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Vin Vashishta

Vin Vashishta is the author of ‘From Data to Profit’ (Wiley), the playbook for monetizing data and AI. He built V-Squared from client 1 to one of the oldest data and AI consulting firms. For the last eight years, he has been recognized as a data and AI thought leader. Vin is a LinkedIn Top Voice and Gartner Ambassador. His background spans over 25 years in strategy, leadership, software engineering, and applied machine learning.

Photo of Tiffany Perkins-Munn
Tiffany Perkins-Munn

Dr. Tiffany Perkins-Munn is on a mission to bring research, analytics, and data science to life. She earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology, statistics, and their real-world applications. As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.

Photo of Richie Cotton
Richie Cotton

Richie helps individuals and organizations get better at using data and AI. He's been a data scientist since before it was called data science, and has written two books and created many DataCamp courses on the subject. He is a host of the DataFramed podcast, and runs DataCamp's webinar program.

Key Quotes

Not getting too far ahead of ourselves is very important. Don't focus on the technology, focus on the outcome. Because a lot of times you'll realize really simple approaches that don't take long to deliver can deliver a ton of value to the business. Technical complexity does not always translate into higher value.

We talk about innovation, but then everybody's afraid to fail. And then if you fail, you spend so much time trying to create spin. So it doesn't sound like you really failed, you know, instead of just failing forward and moving on. And so in that regard, people are never incentivized to like be original, be creative, try new things, move on. So the incentive is not only a financial incentive, which I think is important, like incentive, incentivizing people around the way revenue is created and what their goals are. But it's also incentive around really being innovative and creative and thoughtful in how you execute a transformative process. Right. And so when that doesn't happen, you end up doing things that are, you know, you can do, you can make sure that you have like the process in place for data collection and integration or data quality management and all of that. But if you don't have this foundational idea of culture around incentive, then you really are, it's a little bit like treading water. You end up treading a lot of water, repeating processes and never, maybe moving incrementally, but you're not leapfrogging like you could.

Key Takeaways


Effective monetization of AI and data hinges on a strategy that aligns technological and business goals, ensuring every step from data collection to AI implementation adds value.


The synergy between technical individual contributors, strategists, and product managers is crucial for translating AI capabilities into business outcomes and driving monetization.


Adopting frameworks that evaluate data and AI maturity, alongside fostering a culture open to change and data-driven decision-making, is key to fast-tracking monetization efforts.

Links From The Show


Richie Cotton: Welcome to DataFramed. This is Richie. While every organization wants to get better at using data and AI, improving your capabilities in these areas costs money. That means that at some point, someone in management is going to start asking questions about what the return on investment is. How the company might go about monetizing those efforts.

Today, we're going to combine some of my favorite topics, data, AI, and making money. In addition to talking about monetization use cases and techniques, we're going to cover assessing your data and AI maturity level, deciding on a technical strategy, and how company culture affects your ability to harness the power of data and AI.

I have two guests for your listening pleasure. Vin Vashishta runs the vSquare data and AI consultancy. And he has 25 years of experience in strategy, leadership, software engineering, and applied machine learning. He's been named a LinkedIn top voice and a Gartner ambassador. He's the author of From Data to Profit and the instructor of the Datacamp course, Monetizing Artificial Intelligence.

Tiffany Perkins Munn is the head of data and analytics for the CDAO organization at JPMorgan Chase. She spends her days unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. She has over two decades of experience working in data after moving to the field following a PhD in social personality psychology.

Tiffany was recently a top 10 finalist in the Merrill Lync... See more

h Global Markets Innovation Program. That means we've got two experts with quite different backgrounds, so let's see if they agree on how to make money from data and AI.

Hi there, Tiffany and Vin. Great to have you both on the show.

Tiffany Perkins-Munn: Hi there. Thank you for having me. Yep,

Richie Cotton: We're here to talk about making money out of data and AI. So, first of all, why do businesses find it hard to do that? what's the problem, here? Tiffany, do you want to go first?

Tiffany Perkins-Munn: I would say that there are a number of reasons that businesses find it hard to do this, but mainly it's because it's a long term commitment that requires like ongoing optimization and a willingness to adapt to like changing circumstances. So monetizing data, even before we get to sort of monetizing AI, right?

Can they can both be challenging though, for businesses due to several factors that include like technical factors, organizational factors, strategic challenges, businesses need a holistic approach that involves addressing those technical issues, building a data driven culture, investing in talent, ensuring compliance with regulations, developing clear strategies for monetization.

So all of that means that it's not a short term. near term sort of objective or initiative. It's a longer term play and a lot of businesses really just don't have the patience for that longer term type of optimization.

Richie Cotton: Definitely brings home, I mean, working at a startup, it's like anything more than two quarters in the future, that's just like science fiction, far future. So, yeah, long, long term strategy. It's tricky stuff. Alright, Vin, do you have anything to add to that?

Vin Vishashta: Yeah, those are all great points. I think the big problem that we have and being from the technology side, people kind of think what I'm about to say is a little strange, but we get wrapped up in the technology and we don't focus on the business enough. We have to start with the business. What are the business's objectives?

What is core strategy today? What are the main goals? What matters? What do we need to move the needle on that we're not having success with right now? And that's where we start with data analytics, machine learning, deep learning, and then eventually AI. It's also a progression. Tiffany, you kind of nailed it.

It isn't a thing that happens tomorrow. But we have to monetize incrementally. If we're doing data, we should be monetizing data. We can't go straight to AI. There's no way to jump over, you know, sort of the, well, we don't need data for AI. Well, no, you really do. And it's even more important when you get to that bigger and more complex model, but we get wrapped up in that technology conversation.

And we should just start with what is the business having trouble with right now? Where can we help? What can we do today? And what are we building for? In the near future, and also long term, when you keep all of those in mind, you typically have much more success monetization because it's a holistic view, just like Tiffany said.

Tiffany Perkins-Munn: Yeah, that's spot on. Spot on.

Richie Cotton: Yeah. So as a data person, it's like, Oh, I just want to play with some data and then maybe we'll figure out if there's a business use case first to go with it. But yeah, that's the wrong way around to think about it. You need to start with the business problem and then see how can I make use of data to serve that?

All right. So, Vin, I know you're a big fan of technical strategy in order to make use of it. So can you just explain a bit about what is technical strategy?

Vin Vishashta: Well, it's a framework for decision making, and businesses haven't had that before. They don't feel like they can ask questions like, why are we using technology in the first place? So that's where technical strategy comes in and explains why the business uses technology to create and deliver value to customers.

And we also have to look at technology as multiple. There isn't just one or the technology, but from a C level leadership standpoint, they don't want to get dragged into, you know, whether it's digital or cloud or data or AI or something else, they want to look at technology as a lever. But they can pull to get a competitive advantage, to grow, to maintain margins, to do something a business cares about.

And that's what technical strategy does. Creates that framework for decision making across the firm about why we use data, why we use AI, why we use digital and understanding the unique strengths of each one of the technologies so that we can make informed decisions in an aligned way. We often make sort of these disconnected decisions and disconnected initiatives.

And when you align, you can move forward so much faster.

Richie Cotton: I like that. Just being able to. Decisions consistently rather than one at a time seems like it's going to save a lot of effort and reduce inconsistency there. In order to get better at working with data and AI, you need to understand where you are at the moment. So I'm curious to how businesses can assess their current level of maturity around data and AI.

Tiffany, do you have any advice on this?

Tiffany Perkins-Munn: Yeah, I think there are several evaluations that are required to kind of figure out where you are. I'm in the maturity of your data initiatives, but starts with like data maturity. So using a model that will help you like categorize the maturity level from ad hoc practices to fully optimized processes, And then you have data governance. You want to evaluate how well is the data defined? How do we document it? How do we govern it? Where are there areas of opportunity to improve the quality, the integrity, the security, right? And then you ask, is the organization's data strategy and vision Because I think this is related to what Vin was saying about technology strategy.

Everybody's in data. They know they have it. It's around. They're capturing it. But what is the strategy? Is there a clear vision? Is it well communicated for the business use? and how does it aligned to the overall business goals and objectives of the organization, And then last but not least, also related to what Vin said, is the IT stack, Does it support the firm's ability to collect and store and process and analyze all this data efficiently? We have to consider whether the technology supports the, like, the scalability and the flexibility that's needed for future growth. So, I think those are four areas to start while there are other areas that they would also be thinking through, you know, there are people in process areas that I think matter for data maturity as well, but those four related specifically to data and technology are very crucial and important.

Richie Cotton: Okay, so it's really, it's a lot to think about. It's not just about, oh, I've just bought some new tech. And uh, we're going to be there. Okay,

Tiffany Perkins-Munn: that happens, you know, they are like, Oh, we're going to bring in this CDP, like, but how does it connect to legacy systems and what is it really supposed to do?

Richie Cotton: Yeah, I'm sure that happens far too often. Let's just buy a solution to our problems. Alright. Let's try and make this concrete. So, Bin, do you have any examples of what a high data maturity business looks like.

Vin Vishashta: If you can consistently monetize up front, which means estimate how much return you're going to get for an initiative. If you can consistently do that up front, you have frameworks and processes to help you understand, before you even start, what the expected return is. That's a huge sign of maturity.

And then it is. Do you have steps to go from? We found an opportunity. We've estimated the opportunity size. It's aligned with the business to the data team has enough information to build this thing. They understand what it is. Is there that translation layer between? Here's the value that we expect. And here's the solution we're going to deliver.

And here's how they connect to each other. And then finally, can you get that into production? Can you support it? Can you continuously improve it if you don't have those layers in place, you can't consider yourself a mature organization when it comes to data and there's so much to support that. I mean, Tiffany called out a lot of the core elements that you need because your data is everything.

If you don't have data, you don't have models. If you don't have data, you don't have any sort of source of insight. If you can't connect your data to your business context or customer context, models can't learn from it. So there's so much, from a technical standpoint that goes into it. But maturity isn't just about the technology.

It's also about backing up one step and solving some of the strategic debt that the business has. We talk about technical debt, but it also has strategic debt and that's cultural debt. And so mature organizations have attacked the problem holistically so they can create value. With technology, not just create technology with technology.

Tiffany Perkins-Munn: I happen to be in a highly regulated industry. So I just wanted to double click on what Vin said, because all of that is like completely accurate. And the other thing that I would add is that firms have to have robust data security measures in place. Like encryption, access controls, regular security audits we're always working with compliance around data protection regulations to make sure they're consistently maintained.

And you have to have those in place early on, especially if you talk about monetization. So let's think about how we get those in place for our sort of traditional analytics machine learning that we will do when you decide you want to monetize. Now you really need to think about any additional risks that you might be introducing into the system and making sure that you are compliant from a regulatory perspective.

Richie Cotton: That's just incredibly important because if you start ignoring what you're supposed to be doing on from a compliance point of view, I'm sure you're going to get fined out of existence pretty quickly.

Tiffany Perkins-Munn: It's not that people ignore it. I think it's that As AI becomes more prevalent and generative AI, for example, gets more embedded and incorporated into processes, new opportunities for risk arise. And I think that sometimes we jump to the, let's use this really cool, new, shiny thing for something exciting, and we accidentally overlook, oh, but it has introduced these new areas of risk that we need to have processes in place to deal with.

Richie Cotton: So maybe moving back from the start. So we're giving examples of like what the good case is when you do have that sort of a high maturity and you can put things in production and it works and you're complying with all of your regulations. What about the start? Like, where do companies typically begin?

Like what's, what's an example of like a low maturity business and how do you move away from that?

Tiffany Perkins-Munn: Is the question, how do companies get started in transforming their data capabilities?

Richie Cotton: I think so. Yes. That's a much better way of phrasing it.

Vin Vishashta: Describe the worst train wreck you've ever seen and name names. I don't think we can do that

Tiffany Perkins-Munn: So Vin actually Opened up with this, which I thought was a great point. the very first thing you have to do is articulate your business objectives, right? Articulate the business objectives that the data transformation aims to support. Really understanding how did they? How does the data contribute to achieving specific goals?

Such as, you know, improving operational efficiency or enhancing customer experience or driving I hope you innovation or generating additional revenue streams because we're talking about monetization here, And really the goal is to, Make sure people are in place who can oversee those data initiatives or someone who has the strategic direction to ensure that those data efforts align with overall business objectives.

I think that's one thing you can do in addition to the assessment that we talked about in addition to building the strategy that been talked about. But also this data driven culture. we also have the tendency to jump into new initiatives. We, the culture's over here, we're over here thinking about new things, and then we're over here doing it, and the culture's still over there.

So it's like bringing people along to really foster a culture where data's valued, it's utilized across the organization, we have communicated up and down, left and right the importance of data driven decision making and, and, and we've provided training. Thank you. right? Training is really key so that people understand what they need to do in order to enhance data literacy among the employees.

So I think those are some of the steps that firms need to take.

Richie Cotton: These all seem to be important things. There's quite a lot there, so you've got to set your goals and your strategy and make sure people have training and they've got, I guess, the right attitude, the right culture. So a lot going on there. Are there maybe some quick wins you can get? Like, what's a simple good first goal there?

Vin, do you want to take this?

Vin Vishashta: after you've done some kind of opportunity discovery, either top down, bottom up, taking all of the opportunities you have and finding out just really looking at them and saying, okay, which one of these can we do something in the next six weeks with what we have, not like some crazy, we're going to put all this stuff in place first, but.

What can we do with what we have today? And that revolves around what data do we have right now? What infrastructure do we have and what talent do we have? If you break it down that way, it's a whole lot easier. I mean, because everyone sort of calls out, Hey, I had a quick win here. Hey, I had a quick win here.

But every business has a different opportunity set and every business has different available data, different talent, different infrastructures. We have to acknowledge that they all start from a different place. And it's very important to begin with. What do you have and what can you deliver? And talk about delivering quarterly.

Yes, we're building out the long term vision. Yes, we're building out a lot of infrastructure. We're going to do security data governance. We're going to get to the point where we're doing AI. But what can you do right now? And not getting too far ahead of ourselves is very important. Don't focus on the technology, focus on the outcome, because a lot of times you'll realize really simple approaches that don't take long to deliver can deliver a ton of value to the business.

So technical complexity does not always translate into higher value. So it's really important. Find those places where The business is needing help. The business has problems that it feels. Customers have pain points that they feel and they're articulating. Look at those, figure out which ones we can do something for quickly, six weeks.

And then incremental, you know, iterate around that. Iterate around that one first start. And about six to eight months in, you realize, wow, we've delivered a lot. It wasn't as hard and as painful as anything we've done in the past. We're building towards something bigger because we're putting those incremental pieces in place while we're delivering.

And that's really the quick win. You think about quick win, quick win, but we have to also string those quick wins into something bigger. Into something cohesive that connects together that implements around a vision around a road map. So you're not just delivering disconnected features. That's the danger with quick wins is you deliver a ton of quick wins.

There's no connectivity between them, and then there's no next thing we've delivered a couple of quick wins. Okay, now what? And if without that cohesive vision, it's very difficult to get some place where the business is more mature, where infrastructure is more mature, where better best practices are put in place, it, we have to have that cohesion

Richie Cotton: That seems very true. So, I think if you, If you're at the start and you're really in a bad place, you're going to find some low hanging fruit to fix, but maybe that's not going be sustainable to turn into some kind of long term better vision. Tiffany, do you have anything to add to that?

Tiffany Perkins-Munn: Well, I think that Vin hit it on the head. I would just add that I have been in lots of situations where something new comes along. A firm decides that they are going to go all in on this new technology, this new solution, this new plan, and they spend a lot of time building out the final target state.

Like, this is what it's going to do, this is what it's going to deliver, this is how great it's going to be, and then everything gets lost there. Because then, You know, a year down the road while they're in development, the business comes back and says, but wait, I thought we were going to be able to do all these great things and we really want to know what can we do now?

Like right now, given our current capabilities, what can we actually implement, execute, do right this second? And I think it gets lost in translation sometimes because of the excitement. So really. Laying those steps out, those quick wins, those very key steps that you need to take, where you need to bring the business along with you, explain to them what's happening, but also explain to them, this is a transformation, and as a result, some of the things that you're used to doing will now slow down.

Like, they have to understand the implications of this new transformative work and understand what's happening now, what's going to be happening in the immediate term, and really that this longer term solution that we're all aiming for, talking about, planning for is three to five years in the future, And how do we plan for that progress? And that, you know, that happens. People often get there before they're really clear on what we can do now, and so a lot of programs die. Because of that,

Richie Cotton: This is interesting the fact that there are these short term projects, but like a lot of the big changes happen over that longer three to five year time span. Like just changing infrastructure, changing culture does take a long time. Chief data officers have a notoriously short tenure because of this, like, you know, they start these long programs and then the business is like, well, where's the money?

And then they get cycled up. I was wondering whether you have any advice on how to reconcile the fact that these things do need to be done long term, and yet there is that demand for immediate return from the business. Like, is this just a communication thing, or is there something else?

Vin Vishashta: much bigger. So the point of the data and AI strategy is to Help facilitate decision making to inform decision making. It has to be actionable. And if you don't have one of those strategies, you don't understand the connection between what you're doing today and where you're going. And so there's always this demand for, we need generative AI.

We need it tomorrow. And it's, there's no connection to what we're doing today. There's also no iterative value delivery that's coming from the data team. And so your CDO is kind of set up for failure. They're being asked on one side to deliver technology and on the other side, immediate solutions. That don't align with the complexity of the technology they're asked to deploy.

So the reason you need a data and AI strategy is because everybody has to be on the same page. When they're not, the CDO is put into a position where they're forcing transformation in parts of the business they don't control, they don't own. And so the data and AI strategy is kind of like backup for CDO.

Because it is that framework for decision making that helps other organizations figure out exactly what Tiffany was talking about. You have work to do, but no one goes to those organizations and really articulates what that work is and doesn't give them that framework where they're empowered to do it themselves, where they can make those decisions, where they have the domain expertise to do it correctly, but also that framework for when do they need to talk to other people.

What do they do if they don't have enough information to do what they need to do? What if they don't know, they don't understand, where do you go? And that's the point of the strategy. Without that, your CDO is really, they have maybe 30, 40 percent of what they need in order to get the job done. And there's no budget justification for things like infrastructure, for things like security.

If you don't know why that connects to initiatives that you're doing, if you don't know how that connects to revenue and cost savings, then it is just this thing that sounds like a cost center, even though it's a critical success factor for making that monetization jump. So, CDOs fail because they don't have that backup.

They don't have that connection to value and revenue, and with that, they just don't get the coalition they need to be successful.

Richie Cotton: Okay, so you really need to have that document that says this is what we're doing and this is why we're doing it before you get started on your big infrastructure projects, your big security projects, things like that. Okay. So, I'd like to talk a little bit about cultures. This seems to be a common thing that you do need to change your data culture.

Tiffany, what does that mean, changing your data culture?

Tiffany Perkins-Munn: it means making sure people are clear, have a clear understanding of what we're trying to do in the business, where data plays into that, how you need to build sort of data literacy around how data drives the business of the organization. And also to just double click on something we were talking about earlier, the financial component.

I think, in data strategy, we're often talking about how data is going to drive our intel, you know, it's going to make us smarter, it's going to make us more connected to our customers. And, There is rarely, though sometimes, but very rarely is that the CDO and the CFO talking together.

That's usually the CDO sort of laying out the strategy and then at some point going to the CFO to say, Oh, can you help me make the financial connection between what we're trying to accomplish and the return on investment or revenue generation or even where there are costs. Cost savings where we've you know, increased operational efficiency, for example, and really that needs to happen in a more collaborative way so that the data strategy is really being informed by financial implications along the way, not just the longer term Big Bang financial input or outcomes.

But here's Strategically, how we're thinking about monetization of data or AI or analytics, et cetera along the lifespan of the development of a more mature process.

Richie Cotton: Okay, so yeah, you really need your executives to actually speak together and work together to begin with, rather than just having chief data officer come up with some grand data strategy and then trying to get buy in later on.

Tiffany Perkins-Munn: For sure. that's one group that needs to be involved if you're talking about monetization from the beginning, right? Finance. I

Richie Cotton: That does sound like a very good idea, have the financial people involved in that. Nice. Vin do you have any other examples of cultural changes you need to make?

Vin Vishashta: One of them is incentivization. You want people to use data, right? The most important data that we have proves us wrong. If all of our data tells us, you're right, you're right, you're right, Well, then it's not very valuable. It doesn't have any sort of return. But we also don't have an incentivization structure for changing decisions.

If you make a decision and you change it, that scene is negative. And Andy Duke has a great book called Quit, which provides a framework for decision making when you have data. Because you're going to get incrementally better data. You make decisions based on best available data, but you also have to be able to use data to make decisions.

To change your mind and be incentivized in that direction instead of penalized for having made the wrong decision, you need to be incentivized for continuously improving. And these are the types of small pieces of culture change that have to be in place. Otherwise, there's no way for people to use data.

We see this in so many different companies where it's a cover up or we use data to support what we've already decided to do. You're discouraged from using data to reveal challenges, because if you do reveal a problem, you know, it's called a problem, and that's something you're punished for. So we need to change the culture around how we operate the business, and that's a collaborative component.

It isn't something, I mean, exactly what Tiffany was saying, we cannot have the data organization dictate to the rest of the business, this is what you should do. The rest of the business has to incentivize a lot of these new behaviors. And there has to be alignment, otherwise it's just us telling people what to do, but it doesn't match the reality of what they're being asked to do.

Richie Cotton: Okay. I like the idea of using data to give up on a bad idea because otherwise, yeah, you,

Vin Vishashta: What's

Richie Cotton: to carry on doing the stupid thing, right? Excellent. Okay. So, we talked a bit about cultural changes. I'd also like to talk about process changes. So are there any processes that typically need to change in order to take advantage of data?

Vin Vishashta: sure. From a process standpoint, you're looking at a company transforming. And so you have to answer the question, what are you transforming to and what do we put in place in order to support that transformation? And this is something Tiffany's already brought up. There's work, there's additional effort.

And so there needs to be processes in place for the business to recognize we have effort involved with transformation and ours don't just magically appear if we don't incentivize again back to the incentivization structure, if it's not part of goals. Then no one's going to do it. Why? Because eventually it'll just fall off because there's something else that you are incentivized to do.

So when you have to make the tough decisions, what falls off? It's those things that don't align with your goals or whatever you bonus with. Same thing with C level leaders. If they don't see an impact on revenue, if they don't see the impact on cost savings, if there isn't some sort of accountability on the data team to deliver, if there aren't real numbers, tangible outcomes, then what is going to suffer every single time we have to make tough budgeting decisions?

Well, it's the data team. Why? Because they're not connected with any sort of financial results. And so that's the process that I think if we had to say, where do you start? What's process one? It's understanding that until we set clear expectations for. Real business impact, tangible, put some numbers against it for revenue and cost savings.

And then based on those numbers, we can create a budget and we can also give everyone else in the business. Accountability for transformation. Why? Because we know what that will return to the business if we do it. And so when you have that connectivity, that's process one is closing that loop and understanding just across the business, we're doing this because, and there's real numbers behind it.

Richie Cotton: So in this case, you just need to make sure that there is some connection to how you're using data and. The impact on the business, like from a, in terms of concrete financial numbers.

Vin Vishashta: Yes. Real accountability.

Tiffany Perkins-Munn: Vin has hit on is like the number one issue that plagues like change management and innovation, right? We talk about innovation, but then everybody's afraid to fail. And then if you fail, you spend so much time trying to create spin. So it doesn't sound like you really failed, you know, instead of just failing forward and moving on.

so in that regard, people are never incentivized to like, be original, be creative, try new things, move on, so the incentive is not only a financial incentive, which I think is important, like incentive, incentivizing people around the way revenue is. creative and what their goals are. But it's also incentive around really being innovative and creative and thoughtful in how you execute a transformative process, And so when that doesn't happen, you end up doing things that are, you know, you can do, you can make sure that you have like, the process in place for data collection and integration or data quality management and all of that. But if you don't have this foundational idea of culture around incentive, then you really are, it's a little bit like treading water.

You end up treading a lot of water repeating processes and never, maybe moving incrementally, but you're not leapfrogging like you could. If you really rallied around this idea of how you modify your incentive culture to accommodate the new data or AI process.

Richie Cotton: Do you have any more ideas around that on like what makes a good incentive to encourage people to actually take heat of data?

Tiffany Perkins-Munn: You mean outside of money?

Vin Vishashta: Cash works.

Tiffany Perkins-Munn: But I think obviously that's the obvious answer but there are ways that I think employees really understanding what employees needs and wants are And how they connect with their roles and what makes them happy and how they feel that they are successful in their jobs even giving them new opportunities to work on sort of stretch projects that are outside of their daily job that may lead to something innovative, allowing them to work on, to go into war rooms or, you know, different people call them different things, but little working groups that are really tasked with innovation.

I think people like to feel like they are. Contributing in a meaningful way to not only the business as usual, because they want to make sure that, they're doing that, but also to like the future. And then the way that you do that is you help people to be connected to these projects, how Their day job connects to this particular project and what can they contribute and how they can add and you get people sort of collaboratively working together There's a psychology principle that basically says if two people have two opposing goals You get them working together in a collaborative sort of line of sight goal that is unified, and you will get maximum productivity.

They will come away with even a better idea than they had as separate entities. So I think there's a lot of that that needs to happen in the culture as an incentive to help people understand that they can lift their heads out of their sort of siloed way of thinking day to day and move into this more holistic way of thinking about how their individual contribution can support the broader business.

I think when new people come into a company, they're like, Oh, I came from here and we were doing this and this is exciting. But there are people who have been enrolled for such a long time and they've done it a certain way. And so incentivizing them to actually do it. they have the expertise, the institutional knowledge, the depth, incentivizing them to think, come up out of that box and think more holistically across the organization could be a real win for a firm.

Richie Cotton: Yeah, certainly having that mix of people with experience and they've tried things that have gone wrong and okay, we don't want to do that, but also incentivizing them to actually occasionally try something new. So I guess what the idea is mixing people with experience and people are new and then they can sort of feed off each other.

Is that what you're suggesting?

Tiffany Perkins-Munn: Yeah, that's one idea. I mean, just as how do you, you know, move beyond when you're thinking about employees who could be most helpful to your exercise, your AI, exercise. How do you help people move beyond outside of money? Obviously, how do you help people get into a mind frame where they want to be collaborative and be innovative and want to really try to drive change in the organization?

Richie Cotton: So I think it's time we talked about some real examples of how you go about making money just to make it really concrete.

Tiffany Perkins-Munn: Um,

Richie Cotton: Vin, do you want to tell me, like, what are the most common ways that you're actually going to make money from data or AI?

Vin Vishashta: Well, the first one with data is figuring out who wants access, and that's probably the hardest thing for companies to wrap their heads around because they're used to traditional products. We know who the customer is, but with data, it's a unique asset. You can monetize it multiple times. So what we really have to do is change the way that we think about data as a product.

It isn't just going to be used once. This is something that can be monetized multiple times. And so the way that I break it down when I'm working with C level leaders is just to say, look, what generated this data and who else cares about that thing? If you're looking at a process where you're helping customers, Who else would want access to those customers?

And what sort of intent could you support? If you look at Instacart, great example, they provide ads at the point of intent to buy a particular item. So they want, external partners want access to data that helps them serve ads at the point of intent. So this is critical. If you look at it and add marketplaces or any other sort of marketplace concept or platform construct, how can you bring platform partners onto the platform with data?

Because then you're not just monetizing them with the data. You can monetize them in multiple ways on the platform. So not only are you monetizing data multiple times, you're also monetizing that same partner. Multiple times and you can improve the artifacts that you deliver. So you go from delivering data that supports something that they care about to improving that data and delivering a model artifact.

So now you're providing not just obvious insights from your platform, but true insights where it is new information, new domain knowledge that you're bringing to your partners. That's sort of the framework for thinking. If you look at it as how do we bring partners in and how do we monetize what we used to not be able to monetize in the past without building out the same thing that the partners have?

How do we use data? To get them on the platform and get them engaged and that works with partners. But it also works with new types of customers. How can we use data to bring new people? And so it's really just connected back to what is it that generated the data? What was the workflow, the process, the system, whatever it is, and who else cares about that?

Who else is interested in that? And that will point you in the right direction for monetization every single time. And it goes beyond just, you know, looking at customer success or looking at experience or looking at cost savings around finance or different types of really common workflows. It pulls you back and you start saying, Oh, it's not just about efficiency.

It's not just about incremental. It's about completely new revenue streams. And that's the highest value. If you start there, you're That's where you get the attention of the rest of the business. Now you have C level leaders bought in because you're showing cash, growth, things that are hard to get right now.

Richie Cotton: That's interesting because I think a lot of businesses start with, oh, well, we can use, we can automate something, we can save some money here. But maybe that's not the right place to start. Tiffany, do you have any opinions on this? Like, what examples have you seen of using data to make some money?

Tiffany Perkins-Munn: I think the most obvious one is like selling or licensing data sets to external parties, right? A company that collects and analyzes market trends might sell aggregated and anonymized data to other businesses. for market research purposes. So I'm thinking of the data that we in financial services have actually participated in.

There are subscription based services where you charge a subscription for access to premium or exclusive data. Obviously targeted advertising and marketing. That's a big deal, like using data to deliver, targeted advertising or marketing campaigns and charging the advertisers for access to a specific audience.

Data driven innovation where you're using data to innovate around new products, like using data and information that you learned from consumers about maybe a new product that doesn't exist. Like, I remember I was talking to someone at Amazon once and he was saying that, They learn a lot by the search that customers use when they log into Amazon.

They often learn that there are products that people are asking for, in mass, that aren't available. And so it gives them a lot of ideas that just simple, you know, keyword search in Amazon gives them a lot of ideas about how they could, could create new products and services that they could subsequently monetize based on demand from consumers.

And I think there's really easy, that's a an unexpected but easy way of driving, revenue from data. And there are more complicated ways to do it as well, but I think the goal is, think about from the easiest, like, how could we package this already existing data? In a way that is meaningful to share with others that might also find it useful to more complicated.

How do we use technology to wrangle or manage this data in a way that might get us to a new solution that could also be a candidate for monetization.

Richie Cotton: That's interesting, the idea that you can just package up your data and then that's like a thing that's saleable in itself, even before you start thinking about, well, how could I use it internally to make more money somehow.

Tiffany Perkins-Munn: There are a lot of firms like across financial services that'll do things like they'll go around to all of the financial services firms capturing some bit of anonymized data and then they will package that together because all the firms care about what the other firms have, but we're not in a situation where we can share data across the board.

firms like that. So they'll package it and sell it. So they have optimized on the fact that there is little, you know, of that kind of selling and negotiation between the firms, for example.

Richie Cotton: Okay. And I think in both cases, you've talked a lot about custom data here. So is customer data generally the most valuable kind of data that organizations have?

Tiffany Perkins-Munn: Consumer organizations for sure because they care about what the consumers are doing, what they're thinking, where they're doing it, which channel matters to them, what things make them feel saturated, what they can do to get more business, how they can get them to be multi product, things like that.

So. Every decision that the consumer is making, whether it's on us, meaning it's a product or service we have, or it's off us, they're doing it somewhere else, is information that's interesting to any business that is really consumer focused.

Richie Cotton: So yeah, if you're a consumer business, that's, the place to start then is, data on your customers. All right. So earlier on Ben, you were saying that can't just jump straight into monetizing AI. There's a lot that has happened before that. Which is slightly disappointing because, you know, that's the hot thing if one wants to make money out of AI.

Uh, So, I guess what, what, what's the minimum amount you need to do before you can start monetizing AI?

Vin Vishashta: I think it's important to realize you don't have to build the AI yourself in order to monetize it. There are tons of third party vendors. If you think about Copilot. You can go to Microsoft, 30 bucks a month per person, you are monetizing generative AI, you are, and you're doing it in a way where you're getting the gains today, SAP has tools, IBM has tools, you know, so I'm not pitching Microsoft, but I'm just saying across the board, you can go to different vendors and immediately get tools that are secure, that meet the needs of the business right now, and it's deployed.

All you have to do is train your users. Microsoft to leverage that tool in their workflow and you're making money with AI. So I think it's really important to explain you don't have to build everything yourself. Sometimes buying is the smartest thing to do because it's so cheap. When you think about trying to build these tools for yourself internally, yes, you're working your way there, but you can buy today for low cost.

A lot of these are just incremental upsells. where you are now monetizing AI. And if the productivity gain is there, who cares who builds it? As long as it's not a core part of your business, you don't always need to own it. When it comes to technology, if it's a core part of the business, it's important for you to own it or be on a path to develop and own it yourself.

But you don't have to start there. And in some cases, you're never going to own it because it isn't core business. So there's very important decision, buy versus build. The other component of this is your data has to be built for model consumers. And C level leaders don't understand that there's a difference.

If you build for BI, there's a person on the other end who has heuristics, they're smart, they have domain expertise, they know what to do with the data. Models don't. So we need to gather it in a new way. And as soon as you make that switch, it's really not that expensive to do. You're just gathering more information, customer context, business context, context about whatever it is that created the data.

So that you can now pass that into a model and it has an easier time of learning smaller models, cheaper. It's really important to just shift that focus and it isn't hard. You don't have to buy a ton of infrastructure for this. It's just gather data in a new way intentionally for model consumers, and now you are accelerating.

That's like a fast track. To get to machine learning and to get to AI. So it's those types of things where yes, you have to change, but the change doesn't have to be expensive or long term. You can do some things pretty quickly.

Richie Cotton: I can certainly see how most companies aren't going to be able to build, like, their own version of Copilot for less than 30 a month.

Vin Vishashta: Yeah, that might be a little more expensive. I, you know, I've heard, I've heard, I've heard some numbers thrown around.

Richie Cotton: Yeah. So that last point though is very interesting that you need to collect, I guess the idea is that you collect a lot of metadata to go alongside your data, and that's gonna make sure that your models are gonna perform better.

Vin Vishashta: Yep. And we have to be specific about the type of metadata. We really have to be intentional about it because metadata is often thought of in sort of this fuzzy context for governance. But no, this is going to make your business run more efficiently because models are cheaper if we gather data, you know, and so it's really important to connect governance quality and that sort of those concepts with what's the business impact?

Where's the R. O. I. For this? And if you do that in every conversation, you're much more successful. I

Richie Cotton: on how you get, what's, what's the fast path to monetizing ai? Okay,

Tiffany Perkins-Munn: a fast path, I'm sorry to say, and I hate to be a Debbie Downer, but I am in a regulated industry. And I feel like firms need to undergo a series of, like, strategic, technical, and ethical considerations to ensure a successful and sustainable approach to monetization, right? Like, they need to assess and mitigate technical risks, like model accuracy, which Vin was talking about, and reliability, ethical considerations, bias, fairness.

Like, That is such a big part of the conversation that we need to have up front as we think through how we start to monetize. And I just don't want that to get lost in like the excitement of monetizing AI.

Richie Cotton: that's fair. You should probably go through all the sort of ethical procedures first, make sure you're doing things right, rather than just diving straight in there. All right. So beyond that, are there any common mistakes that you've seen businesses make when they're trying to get into monetizing AI?

Tiffany Perkins-Munn: Mistakes with monetizing AI. I mean, I think it's a lot of what we've already talked about here. Like, they put the cart before the horse they start, trying to get into, like, the monetization of the AI without clearly understanding the business strategy. You have technology building something that the business hasn't quite asked for because the business can't articulate their needs yet around AI because it's new for them, And so, but people are excited, so they are outbuilding. And then subsequently there's this miscommunication and, and then this big project and it gets lost in translation. So I think a lot of it has to do with, The connection between the technical strategy, the business objectives, the goals, the outcomes, and how you lay out that roadmap of development so that everyone's on the same page.

Because when you skip any of those critical steps, Then something gets lost and then ultimately you end up either with a solution that you didn't ask for or a process that dies on the vine or people doing work without any clear indication of how it all fits together, which ultimately makes the outcome less optimal, right?

and less likely to be really successful, especially if you're trying to get to a point where you can then generate additional revenue or monetize around the activity.

Richie Cotton: So what I'm hearing is that you need to like plan things properly, which is always very disappointing. You can't just dive in.

Tiffany Perkins-Munn: I just, you're eager, right? You're like, Oh,

Vin Vishashta: think the other thing to bring up though, is that strategy doesn't have to be heavyweight. It doesn't have to slow things down. It does not have to stop you from doing things that are obviously aligned with business outcomes. You can do things today, but it's the alignment. I think that's what we understate is the power of aligning decision making and empowering people.

At every part of the business to make decisions about this, not just inside of the data team, not just at the sea level, but giving everyone the ability to make decisions at their level about how the business uses data and I to deliver value to customers. So it's important that alignment. It prevents a lot of these challenges where you have 15 disparate systems, you have, everyone going in different directions, you have to walk back, it's really important to start with alignment, even if it's a really lightweight framework, that's good enough to at least get everyone moving in the right direction.

Richie Cotton: Okay. So if you're trying to align all these different teams, you mentioned like you got the C suite, you got the data team, and then you got all these other teams that need to be on board with the plan. So whether it's like customer teams or product teams, that seems tricky. Are you going to have to form a committee to, to get all this?

Or is it, what, what's the deal here? How do you get that alignment?

Vin Vishashta: Well, the C suite should be involved in building the data and AI strategy, but there has to be someone who owns it. And we often mistake that role for somebody who's already in the business. And oftentimes, people don't have the capability to do that. We need someone who's dedicated to doing data and AI strategy.

We need dedicated data and AI product managers. We need people who do that. Do new jobs. We talk about this is a last mile problem is the technology is the focus, but it's a first mile problem too. So alignment is once you build that strategy, publish it. And if it's actionable, it should support everyone in making those decisions.

It should support every part of the business, not just the data team, not just the C suite, but at any level, you should be able to pick up that document and figure out. How am I going to make decisions about this? Or if I don't have enough information to make a good decision, who do I go to? What's step one when I don't know what to do?

And if it's not actionable, you see this a lot with strategy. The reason why a lot of strategy is considered heavyweight and really bulk or fluff is because it isn't actionable. You can't do anything with it. So it has to be, I mean, if

Tiffany Perkins-Munn: sounds

Vin Vishashta: do anything, yeah, sounds great, that book's going to go on a shelf.

I mean, it has to be actionable. People have to be able to make decisions off of it. You have to be able to do something with it.

Richie Cotton: Yeah, certainly having a strategy that's actionable to seem uh, an incredibly useful thing. Alright, so, you talked about the idea that there's some new roles there, things like AI strategist, AI product manager. So to talk a bit about, like, what skills are needed for these roles. So, maybe Tiffany, do you want to go this time?

Like, what do you think are the most important skills different roles need in order to take advantage of AI?

Tiffany Perkins-Munn: Yeah, I think there's, so when I, when I hire for, you know, these roles, I'm often asking for people who are like data wranglers, or storytellers, or, you know, just like trying to think outside the box around, yes, they're the technical people. Yeah. capabilities that are needed, model builders, people who are writing in different codes, Python, et cetera.

But there, we just need people who are thinking strategically and who can manipulate and wrangle data to pull out insights to help understand what's happening in the data, Because I think Vin mentioned this earlier, like there are so many efforts that we're doing in a firm that don't require AI.

and so you're constantly thinking through what is the business objective, what is the analytical exercise that I need to undergo to execute this objective, and if there's a higher order to the objective that actually gets you to a place where you might need machine learning or deep reinforcement modeling or generative AI, then you want to really understand, like, What are the skills that I need to actually execute on those types of roles?

But the strategic part of it is the piece that I find missing a lot, So, people writing in generative AI now, there's a role for a prompt engineer, like people who can actually write the prompt in a way that helps the system not to hallucinate, for example. But there are so many roles that I think generative AI, machine learning, AI in general will open up that I worry about it less.

I think that some of the more easier sort of low task kind of exercises might get automated, but I don't worry about as long as people are upskilling and learning as the tools come out, they're actually upskilling and learning what is this tool and not only the execution of the technical tool, but the What is the business strategy that I am using this tool for?

Right? So I know lots of technologies. I'm not a technologist. I know it because I want to know how do I use this technology to drive a business strategy. So a lot of those taking the opportunity to learn not only the technical aspect of a new solution, a new product, a new technique or a new technology, but also the business strategy for the application of that.

to your type of business problems. And that could be something like how do you write the prompt for generative AI so that the solution doesn't hallucinate? How do you build the strategies for technical and data, et cetera, in a way that firms haven't thought about it historically? So I think there are lots of opportunities for that.

Richie Cotton: That just seems so important that employees are continuously upskilling to make use of these new technologies and that they understand both how to use the technology and why they're actually bothering to learn it as well. Yes, that, that seems like you're going to have much higher engagement that way.

Vin, do you have anything to add on what skills people need in the age of AI?

Vin Vishashta: Yeah, I mean, looking at your AI strategist role and your data strategist, or your data product manager, AI product manager, the AI product manager is probably the easiest to define because they're going to start at opportunity discovery. They take that opportunity, break it down into something that the data team can build.

So they're the translation layer. They translate an opportunity into technology and technology, vice versa, back into value. How will this solution align? They do things like manage the research life cycle because it's very difficult to keep research, which can be unpredictable, connected back to business value.

So you're looking for people that live in both the technology world and The product world and understand product strategy, monetization, they know how to use data to understand customers and help the business find those new opportunities they manage really estimation, understanding what Opportunity size exists, and they can make that translation.

How could we create a product that would help us take advantage of that? Whether it's an internal opportunity or external. When you're talking about a data and AI strategist, it's that step before. What do we need to put in place in order for the data team to be successful? In order for the CDO to be successful?

In order for the AI product manager to be successful? But also, how about the entire business? Because this is an enterprise wide transformation. Transcription What do we have to put in place in order for everyone to know what they're doing and to be successful? How do we facilitate opportunity discovery?

How do we make the connections between C level advocates and sponsors and your data team? How do we put all of that incentivization that we talked about? How do we get that cash showing up? How do I make it so that no one has to justify infrastructure? That should be done up front. How do I make it so that you don't have to explain why data governance and data quality and data security are valuable?

Well, you know, that shouldn't have to happen up front. So you're looking for people who can make those connections and explain technology in terms of business returns, and that's how you end up translating what the business needs into a data and AI strategy that's actionable. And so those are really, you know, Tiffany called out a lot of really important data teams, sort of skills transitions and maturity, those new career paths.

But on the other side of it, we also need connectors to the business. And we can't ask very technical people to take on roles that they're just not prepared for. And sometimes they don't want. We really need to give them the ability to focus on what they enjoy doing and take those pieces of the work that we often put on them when they're not prepared and they don't want to, on to people who have the right capabilities to implement.

Richie Cotton: Yeah, I think there can be a lot of data people in the audience who are like, Oh, yeah, it'd be really nice to have someone else talk about the business side of things.

Vin Vishashta: Wouldn't it?

Richie Cotton: Yeah, but I guess it's personal opinion as well. Like there's some people who are very comfortable about both sides of things and translating, but it's a different skill set.

Okay. So what are you most excited about in the world of data and AI at the moment? I like

Tiffany Perkins-Munn: I am most excited about sort of explainable ai. I think, you know, like how do we enhance the interpretability of AI models? Like it's crucial for building trust. I think it is a critical element in this whole storytelling component that we've been talking about, Because explainable ai.

Really aims to make all of those models, machine learning models, AI models, more transparent, more understandable, accountable, and especially in critical applications, like in places like health care and finance. And I think the closer we bring people to really understanding how AI works and make it a less Scary thing.

The more easily we'll be able to integrate it into business practices in a more seamless way, the quicker we can get there. And I think that's the most exciting thing about, and I think generative AI has kind of made it tangible for everyone because they can just type in something and voila out, spits an answer.

Though it might be wrong if they don't have the right prompt engineer, but, you know, outspits an answer. I think that has helped people to really understand how easy it is and how it really fits into their lives, their day to day lives. And we need more of that, really, for AI to take hold in the way that we, and for it to be, for us to be thinking about it, not as like, oh, those are the people over there that do AI, but really understanding the integration of AI into our daily lives.

Richie Cotton: point that just having more explainable AI is going to increase trust in how it's used as well. And I can imagine if the regulators are looking at your models, then this is going to be incredibly beneficial as well. Excellent. Bin, what are you most excited about?

Vin Vishashta: Well, to piggyback off of some of the things Tiffany said Moving from data engineering and data management to knowledge engineering and knowledge management, because that's one of the big things that we need in order to get to explainability, looking at causal methods, especially for planning use cases.

You get the transparency with structural causal models, and it's explainable by nature. It's easy to visualize. It's easy to collaborate with domain experts on. So I'm super, super excited about bringing that into planning use cases and understanding sort of the complexity that people are really bad at understanding using data and more reliable models.

To help them understand these more complex systems and longer workflows, longer chains of events that might span from one side of the enterprise to the other. Robotics is really exciting right now. I think chat GPT moment coming for robotics here over the next year where we see where customers people normal outside of the enterprise.

get to put their hands on what a robot can do. What does robotics really change for us? And I think seeing that, you know, watching it happen, that's really the big thing is when we can make it usable by a larger number of people and they can see the impact and they can figure it out for themselves. I think we have that coming for robotics here pretty quickly.

And from an IoT standpoint, looking forward to that data, more data than we've ever had access to you. before and in places we've never had access to it before. That's going to be enormous for us, but it's also kind of expensive. So hopefully the costs come down in the next couple of years to make it feasible.

Cause right now it's, Ooh, that's a little too expensive.

Richie Cotton: Okay, yeah, all very exciting things. And yeah, the robot thing is particularly interesting because I guess AI has been very much a software thing for the last few years. Well, maybe a few decades. And so, yeah, having physical hardware, that's going to be an interesting shift.

Tiffany Perkins-Munn: But also, Ben's point, edge computing is like, I mean, we can actually, do these automations from the source. It's going to be amazing.

Richie Cotton: But too many exciting things happening. That's brilliant. All right, so just to wrap up then do you have any final advice for how businesses can get better using data and AI?

Vin Vishashta: Just start with the business. Don't worry about the technology. There are smart people who can handle the technology. Don't worry about it. Just talk about what your problems are. Like you always have. And get used to using technology as a lever for advantage. Look at it as, how do I put the right people in place, so technology becomes a lever that I can just pull.

And it delivers the cost savings that I need. It delivers the efficiency. It delivers the growth that I need. It becomes an input that creates growth for the business. Don't worry about the technology side of it, put the pieces in place. So it becomes that lever. And anytime you're struggling, you can just pull it.

Anytime you run into a challenge, you can just pull it and you know, it will deliver.

Richie Cotton: All right. Don't worry about the technology. That's a a nice idea. Tiffany what's your final advice?

Tiffany Perkins-Munn: Yeah, I would say develop a clear business strategy with your return on investment built into that strategy from inception. Don't try to connect it at the end. Build it out from inception. And then, obviously, consider privacy, compliance, and ethics. First and foremost.

Richie Cotton: All right. That does seem very important. And it seems like we got two wins for like thinking very hard about business strategy here. So, that's excellent. Thank you both of you for your time. It's been brilliant.

Tiffany Perkins-Munn: Thank you.

Vin Vishashta: having me. Yeah. Thank you.



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