CDO Matters Podcast: Data as a Product

Jonathan Reeve, BetaNXT's CPO, explores the evolving partnership between Chief Product Officers and Chief Data Officers. Their conversation dives into how data can serve as a strategic product, the importance of post-launch customer feedback, and the shift from using AI for individual productivity to driving process efficiency across the business. They also explore the challenge of scaling products while meeting client demands, and why influence and storytelling are essential skills for product and data leaders. It’s a must-listen for anyone looking to drive innovation through smarter data and AI strategy!

Malcolm Hawker

Good morning. Good afternoon. Good evening. Good whatever time it is wherever you are in this amazing planet of ours. I’m Malcolm Hawker, and I’m the host of the CDO Matters podcast. Thanks for joining today.

I’m thrilled to be joined by Jonathan Reeve, is somebody kind of actually like me, product and chief product and chief data officer. I have been a chief product officer in a previous life. My passion my first passion in business was product management, because some of you may know that. And then I made a transition into into data, and I’m really thrilled to talk about Jonathan about this kind of the commingling of these worlds, what it means to actually be a chief data officer when your product is actually data. We’re gonna talk about data products. We probably won’t talk about it in the way that you may be a company to as a as a data analyst professional, but that’s exactly the point. We’re gonna talk about building great products that customers want to use.

Jonathan, welcome to the podcast. Thanks for coming.

Jonathan Reeve

Alright, Malcolm. Thanks for having me on. It’s a pleasure to be here and talking about my two favorite things, like product and data coming together.

Malcolm

Well and maybe even number three, in our prep call, you had mentioned that you have a passion for Lego. And I was just looking back at some of my notes. That was my favorite thing as a kid growing up. And it’s so weird because I know so many people in our in our profession where that’s actually true as well, that Lego was one of their favorite things as a kid. It was certainly my favorite choice. I don’t know what that says about us.

Jonathan

Well, I think that’s you know, when you’re a kid, you and I maybe now online, there’s less of it, but you’re taking these seamlessly square blocks and then turning it into a crane or a car or something something cool. And at the end of it you just it you’re transforming these pieces of plastic into something cool. And that’s to some extent, that that that thrill of building something new is kinda what we’re doing with data.

Malcolm

It’s exactly, I would argue, what we are doing with data. So I love the metaphor there. So you are the chief data officer at BetaNXT financial services firm. Quickly tell us what you guys do, and what does a day in the life of Jonathan Reeve look like?

Jonathan

Yeah. I’m actually the Chief Product Officer at BetaNXT, and I’m responsible for ideation in terms of what are the products that we should build. BetaNXT is a accumulation of a couple of companies, Beta, Mediant, Maxit, and, actually, we just acquired a company called Delta Data. And so, again, like the Lego blocks, each of those companies had a bunch of data assets and looking to see how to stitch those together to solve new solve problems for our clients.

Malcolm

So how deep do you go into what I would euphemistically refer to as the supply chain? Right? When you are when you are maybe curating data or building data, like, how deep do you go in your role into kind of your supply chain and saying, well, I need this. I need this. I don’t need this. Or here are the requirements I need to define to do these things. What I’m trying to differentiate what you would do potentially on a daily basis versus what a CDO who is focused on the data and analytics side of the world would do a draw distinction there.

Jonathan

Yeah. So, I mean, the data regardless of the data type, you want to have and I’m gonna use my language here. You want to have the definitive source. So it’s important that when people are looking at data, that they understand that it’s accurate and it is the definitive source.

And so whether it’s a piece of reference data in terms of identifiers or descriptions or it’s a corporate action or in our in the case of Beta, we’re actually presenting account names and account values. Because the underlying databases of Beta are the definitive source for that, we we we can guarantee high accuracy on the data where we are the definitive source. The value comes in is when we can commingle that with other data, market data, corporate actions, and make and get that from the definitive source. And now we’re creating curated blends of data that our clients can count on for reliability because we haven’t taken it from second or third or ancillary sources.

We’re trying to get that that definitive source. So to answer your question, where where BetaNXT is the definitive source, that’s quite easy. Where we’re working with other third parties or integrating other content, then we have to make sure we get that level of accuracy. And then, of course, the art of the deal is all about linking. Unless you link the data in a way that’s meaningless, doesn’t create the value that the that the clients need.

Malcolm

So tell me more about linking. Because to to me, in my world, when I hear linking, though, I think matching. Right? What so definitive source to me would be system of record. I think definitive source, we can all generally, that’s a the conception Yeah. Of a lie to that. That’s not too far from my beloved world of MDM where we say, okay. This the gold master, or this the system of record or the the record of source or source source record. But when you say linking, what does that mean? Linking your data to the third party data or linking your data to client data or both?

Jonathan

Yeah. Both. So when you when when you have data, and, obviously, data is growing exponentially, and we should touch on that, the value comes when you can not only navigate from one dataset to the other, but where you can draw conclusions and hopefully conclusions that other people have not made. If you’re if you’re let’s say, if you’re looking for alpha or you’re trying to manage risk or you’re trying to present a consolidated view of your account to a client, you can create views that previously because these datasets were not connected, people have these aha moments about I did not know that. And we’ve created value because we are linking the data, and we’re doing it in a definitive way. So I’m not questioning whether this data is truly relevant. So if I’ve got an account, I understand the securities in that account. I understand the value of those securities. I understand what the last trade of was of that securities. I understand if it had a corporate action and when those dividends were paid and when they will appear in my account. So all of those are datasets as you know, in their databases of record.

But until you link them together to create value because you’re satisfying some curiosity or doing some piece of research until you do that, you don’t create the desired customer experience. And we live we live in a Google type world where you expect to have the information at your fingertips, and so linking is the way that we do that. So you can travel you can literally travel from the account to the security, to the value, to the corporate action, to the did it was it involved in m and a action? What’s the latest news on that company. If you want to be able to create all those connections, you have to link the data.

Malcolm

Okay. Well, that sounds an awful lot like matching to me, but we don’t need to get hung up on semantics as much as we love as data people to get hung up on semantics, and we cert and we certainly do. But there was something interesting in what you said there, which was this idea that you’re going to create value be by finding these aha moments for for your clients and finding these insights that you didn’t otherwise know were there. And in my experience, that’s when the magic happens.

Right? Like, creating a a net new sales dashboard. Okay. That that’s that’s great. Or sales for last quarter dashboard. Okay. That’s fine too. But the real the real value and the real is using data to expose things that your customers didn’t otherwise know about. Now I assumed that that process is reasonably bounded, meaning you’re not just out there trying to throw fifteen million pieces of spaghetti to the fridge. You’ve got a reasonably good idea what your clients are gonna need. So tell me, and I’m putting words in your mouth, but tell me what that requirements defining process look like looks like. Like, tell me, do you have, like, a big team of product managers that are going out and to to clients and say, hey. What’s gonna make your life easier, and how do we try to go about finding those moments? What does that process look like?

Jonathan

Yeah. So, I mean, my job as a better product person, I have to be in front of customers. And I often say, like, fifty percent of my time is with customers. So I spend a lot of time with our sales team, and I go to a lot of client meetings. And the reason I do that is first of all, they they they want to have product updates. I’m showing them a road map about all the great things that we’re building.

But more importantly, I’m getting feedback from the customers about what challenges they have and what they’re trying to do. So as an example. Right? So if I’ve if they’re if they’re looking for additional insights and they’re looking to connect, let’s call it my data, which is account information and balances, and they have a CRM implementation, and they want to they want to create better insights to their financial advisers so they can have better tools available to them, we can potentially create a utility where they’ve now got we connect their account information to their CRM information, and now I can do new and interesting things.

So if Charlie Brown is my client and I’ve connected it to the CRM instance and now I have information that Charlie Brown’s daughter is gonna graduate college and she’s gonna you know, she’s got a new job, I can now use that information to alert my adviser to make a call, say, congratulations on the graduation. You know, do you want me to help her with her new employment or sort of set up an account for her or anything? But the point is you can that’s an insight that you may not have if you were just looking at here’s my balance and here’s Charlie Brown Brown’s balance. I can do I can create, like, insights that that financial adviser may not have had.

Malcolm

What what role does so in the process you just described, I do want to come back around on product management. But what role does in the ideation process of coming up with these products? Right? There’s there’s the need definition. want to drill on that a little bit more. But there’s also necessarily kind of going back to my product management roots. There should be some idea of an ROI analysis. What role does that play in kind of the product ideation process? Do you do you have people who are, like, sitting down with clients and asking, hey. What would this be worth to you, what would you be willing to pay for it?

Jonathan

Yeah. Absolutely. Because the worst thing you can do as of any product manager is build things because we can. So go back to the Lego blocks. We can build so almost anything but the question is, will it add value, and will a customer pay for it? Right? And I don’t know if anyone’s taken sort of there’s a course on this by a company called Pragmatic as an example, but this Pragmatic product management, which is what they refer to sort of outside in product management. And what that means is we’re getting insights and feedback from our customers about what problems they’re looking to solve. We’re not doing inside out product management, which is, gee whiz, I have a really cool tool over here and it’s really cool software that someone sold me, and I’ve got some data over here. And this interesting, but it’s, like, internally interesting. You know, not not necessarily, is it a problem a client is trying to solve, and will they pay for it? And if we do enough work upfront and we have good relationship with customers and we can say that does this does this solve a problem, and would you pay for it? Then then there’s the of an idea, And now we can build a prototype, and we can go back and said, okay. Here’s an alpha version. Is it what you expected? But, ultimately, you have to build stuff that people will pay for because we’re at the end of day, we’re a commercial enterprise. Right?

Malcolm

Yeah. So is it correct to assume that people are managing those interactions? Are are product managers on your team?

Jonathan

Yes. Yes. Very much so. And so we have one one of the one of the sort of operating cadences we have at BetaNXT is this project to product evolution. Right? And so, typically, the way a a client will say, I want I wish your system would do this for me, or I wish I could have the following feature. And we can either do that as a one off project for that customer that says, okay. You know, we’ll we’ll add this knob or dial for you, or we can say, hey. That’s an interesting idea from client one. Do you think client two and three would like it? And if they if we can repurpose it, which is where we want to head in that evolution from project to product, we can take that idea that we got from client one and rather do it rather than do it as a project, turn it on as a feature or a capability or an add on or whatever you want to call it. And now we can roll that out to clients two, three, four, and so on. And that’s this philosophy of build once and sell many. It’s just obviously a much more effective way to to run a company.

Malcolm

So data people take note. Sorry, Jonathan. Quick aside. What Jonathan just expressed was that scalability from a product perspective is a function of incremental demand. Scalability is not necessarily a function of being able to efficiently do something multiple times, although that’s certainly what it means. I stress on this point because in our world and data and analytics, we talk about data products, and we talk about doing data products as a way to be infinitely scalable. But if nobody wants what you’re building and nobody can get value from what you’re building, then the scalability is completely irrelevant. You have built a scalable infrastructure that is literally a bridge to nowhere.

Be before you dive into assuming that you that your endgame is scalability, the endgame should ultimately be figuring out, uh-huh. This product is needed multiple times, and there’s plenty of demand for it. And the person doing that is the product manager. Now now, Jonathan, you mentioned you mentioned clients coming to you and say, "I’d like a new knob or or a dial or a button or or or something." You know, having been a product manager and having led product managers, I’m ultimately wary of when somebody comes to me with an answer. Right? And and often customers will come to us and say, I need the knob. But I’ve learned over the years to say, okay. Wait a minute. What do you think the knob is gonna do? Why do you need the knob? Tell us a little bit about the process that you that folks in your team use to really actually drill down on on the core requirement here. And what is it about product managers that kind of makes them uniquely qualified to do what I just described?

Jonathan

Yeah. Well, this the classic I’m sure everyone’s heard this quote. Right? If Henry Ford listened to his customers, he would have built faster horses. Right? And that’s where you have to take you have to take a need and dissect it and understand what they’re truly trying to do. And is there a way to pick it apart and understand the end result the client is trying to get to rather than it may not actually be a dial that they’re creating. They want to create this end result. Like, what are you gonna do with that dial? You know, ask a lot of questions. And, ultimately, the answer might be different than than just building a dial.

The other thing I think that’s potentially useful when building product is to think about things and componentize the solution into smaller parts. And the reason you want to componentize and this sort of my rather build up micro components rather than sort of one mega system. Because if you build micro components, then when just and then you stitch them together. So you build micro component a, b, c to to deliver solution d.

Tomorrow, when you’ve got a different problem, you may be able to use a and b and this new component f to to solve a completely different solute problem. So it’s much better to build these components rather than what you’ve called monolithic architecture. Right?

Malcolm

So that does align to what a lot of data and analytics people call a data product. So data products to me exist on a bit of a spectrum, and there’s many people in our world that believe that there’s this kind of this left side, a shift left, which is kind of this lowest atomic unit, which kind of aligns to what you were just which which is you were just describing, which is kind of some modularity there. And that’s an extremely incredibly valuable approach. And if you’re gonna be building products, you you need to have a very you know, a highly efficient, what I would argue with supply chain.

But it does start, to your point, with that customer need. And if you’re not doing it to solve a specific problem, well, then you you’re you’re you’re you know, how are you gonna stay in business in the long term? You mentioned yourself, your commercial business. So, yeah, I love this stuff.

What about post sale? Right? What about kind of managing the life cycle of a product? When do you know something has run its course and may no longer or should no longer be supported? is that you know, how do you think about kind of the overall product life cycle in your world?

Jonathan

Yeah. So, like, typically, what I see is, obviously you’re launching you you launch a product, and you’ve got this one one dot zero of the product. And then as soon as you I’m going through this today as we speak. So launching one dot zero product. And, typically, the clients, sort of the first adopters of that product will come back and say if it only did this and it only did this. Right? And so once you get those requests, though, that’s where you ask yourself that project to product question. Okay. The first client wants me to create add on feature one and add on feature two. Should I do that, or or is that something just bespoke to this client? Hopefully, it’s something that it it’s it’s scales. And now we get and we do that once or twice, and we may do it for for a couple of years.

Ultimately, in the adoption life cycle, obviously, everyone’s seen the curve adoption of the adoption life cycle. Typically, something happens. Either the customers move on, and they they start to want different solutions. Technology moves on, and now what was really cool four or five years ago or efficient or valuable, the technology and as a data as a chief data officer or a product officer, you have to stay abreast of the current technologies because there are things there are technologies out there today that didn’t exist five years ago. And if you don’t think about using them, you’re you cannot build sort of world class products.

So, typically, you’ll know you’ve when things are a little dated, the clients are moving on, there’s a new technology that allows you to do it better, or to some extent, the the demand is starting to tail off. And then and then you need to essentially go into end of life mode, think about what the next generation of that product will be. And the hard part is, especially when there’s revenue on that first product, is to force the migration to the next generation. That’s that’s very hard.

Your CFO will probably ultimately force you to do it because then, otherwise, you’ve got the support costs of two platforms. But if you’re if you’re cutting edge enough, you’ll move you’ll move on to the new technology, but you’ve got to, at some point, issue the end of life letter and say, hey. This this end of life, and we’ve we’re gonna move to this next generation. And and as you know, especially with data platforms, it’s not like a log on. Generally, it involves a data migration, a switch, writing to new systems of record, and that requires a project on the customer site. And that’s you know, those are generally long processes, sometimes multiyear.

Malcolm

Congrats on your launch. Hopefully, everything goes smoothly. Been there. Right? Like, kind of fingers crossed everything goes smoothly. So senior VPs of data and analytics, CDOs, if you’re out there and you’ve got hundreds of dashboards that aren’t being used, get rid of them. Right? It it it doesn’t help by having a whole bunch of dashboards sit around that nobody’s using. And if you follow some of the things that Jonathan was just talking about, right, if you if you are razor focused on customer needs, if you’re razor focused on solving specific problems, in theory, you wouldn’t have built the dashboards that nobody really wants or needs anyway in theory. Right? Like, if you are fully aligned to what the customers are needing, well, then they’re gonna be using the product and gives you built a product that is in full demand.

So just kinda circling back, Jonathan, what I heard my first line of questions were how do you differ from, like, a CDO who was doing in data and analytics internally? I didn’t necessarily hear a whole lot of different. I heard data management because you were about housing the data, maintaining the data, matching data. Right? That’s that’s an internally that’s a you know, that’s very much a data management function. I heard data integration, which is also a data management function. I heard managing data quality. What you spoke to was making sure that you are creating curated, trusted, accurate insights that that your customers trust. That’s inherently a data quality process.

So I don’t necessarily see a huge difference here between what you would be doing as a chief product offer officer where your product is data and where a chief data officer is managing things internally. What what do you what do what do you think? Do do you think you’re you know, how would learn?

Jonathan

I think you’re exactly right. Like, the what BetaNXT is aiming to do, the promise of BetaNXT is delivering on connected wealth. Right? We’re we’re providing connected wealth solutions. What does that mean? How do you provide connected wealth? It all comes back to the data.

The the data is the glue that allows us to serve our customers. It allows us to provide insights. It allows us to create reporting. So to some extent, what I’m saying, data is the product. Right? Now you may present it in a GUI. You may present it in an API. You may present it in a a cloud share, but the product is the data. So you’re absolutely right. I’m a CDPO. Well, like, sounds like something from Star Wars.

Malcolm

It would certainly does, which would also align well to all most of our audience as well. We there’s a lot of us. So let’s you mentioned about new technology being a driver of product life cycle changes and maybe the need to archive specific products. Let’s talk about our our favorite topic number one these days in the world of data, which is AI.

What are you seeing out there for your clients? You work in a highly regulated industry. What are you seeing in terms of demand for for AI based solutions? And then what are you also seeing in terms of, let’s just say, the willingness to operationalize within some of these financial services companies? Because I’m seeing I’m seeing a whole bunch of stuff out there. I’d love to hear your perspective on what you’re doing what you’re seeing.

Jonathan

No. So, obviously, you can’t engage in any data conversation these days or go to a conference and not talk about AI. It’s Yeah. It’s it’s everywhere. But as you know, at the end of the day, the AI and every everyone knows this. The AI is driven by the data. And what separates your AI right? Because you you you as an institution have a bunch of proprietary data, whether it’s your security master, whether it’s your account information, whether it’s your patents, whatever your proprietary data is, what you want to do with AI is unleash AI on your internal data. And that I mean, they that’s probably obvious, but unleashing AI on public data, anyone can do that. It’s your internal data that will allow you to create new products, allow you to be more efficient, drive better insights, manage your customers better. So you, as the AI owner within your organization, want to want to drive a create new insights with AI on your internal data.

So by definition, for that to be effective, your internal data has to be useful. It has to be, I’ll call it linked. You can call it matched because and it has to sort of all be available and available to that AI engine. If you don’t make it all if you don’t make it available and make it sort of schematically useful, then and you unleash the AI without that, you’re going to get false positives, bad results. And you’ll hear this from many many sort of AI technologists sort of looking at that they’ll spend fifty, eighty percent of their time wrangling, I love that term, wrangling the data versus unleashing the AI. And so that’s why it’s important this a bit motherhood and apple pie. You’ve gotta get that data if you have that data foundation solid, then your AI machine will move much faster.

Malcolm

Well, how many of your clients do you know, that you’re talking to? You’re spending a lot of time in the field. How many of your clients feel like they are ready or not ready, and what are they doing about it? Are are your clients just kind of tiptoeing into POCs or some of your clients going head you know, full on into adoption of some of this stuff? What are you what are you seeing from your client’s perspective?

Jonathan

So I think there’s there’s I think I think we are in the early innings here. So there’s still a lot of experimentation. I see I sort of see and then this applies to us and our clients. They’re looking to either they’re playing with AI in two areas. One is to drive operational efficient efficiency. They’ve got some operation function, and they want AI to say, okay. You know, this operations team, time this happens, they click this button, and they click this button. And if I could harness all of the motions that this operation team’s doing using AI, I could automate that and make that that sys that process much more faster.

So that’s sort of on the operational side. And then I think on the the other side is the insights. Right? So how I want to create I want to create new insights. So I want to scour and mine this data, and I want to learn something that I didn’t know before or my customers didn’t know before or or which I can use to create product and generate revenue. So that’s the that’s the two areas both where we’re playing and I see our customers playing is operational efficiency and additional insights.

What we’re doing, and I’m not sure if this makes sense to everyone, but we’re sort of starting with the operational side. Like, let’s use AI internally to make our processes more efficient, and then what we learn there, then apply that to external products.

Malcolm

Well, that’s according to Gartner, you would be generally one step ahead of where most organizations are. So according to Gartner, where most organizations are right now is individual productivity on the desktop, Right? Being individually whatever I’m doing, and I’m using just some chatbot to make my you know, whatever I do, whether I’m in marketing or HR or software engineer. And then Gartner would say kind of the next necessary evolution is to work is to use AI to make business processes more efficient, and not a lot of companies are there.

It sounds like you may actually be a little bit ahead of the curve in terms of helping, you know or leveraging AI to help those internal processes work better. So there you go. You get the two thumbs up.

Let’s let’s circle back to to just kind of the discipline of product management and the intersection of product management and what I would just loosely call data management. If I’m a CDO and I’m hearing this conversation and I’m hearing me, Malcolm, consistently thrown on in my LinkedIn feed about the importance of integrating data or or integrating product management as a discipline into your data function. As a product person, what are the kind of the two or three things that you would recommend to a CDO to do within their organizations to become more product management driven in their daily operations?

Jonathan

Yeah. So I think we we live in a world and to I’ll use some some famous platitudes. Like, data really is the new oil. Right? And if you are the CDO in your organization, to some extent, you hold the keys to the kingdom. And whether you use that sort of knowledge of how data works within your company to drive efficiencies or to drive product I think you have a lot of ability to influence the direction of your firm because at the end of the day, the company, no matter what the function is, whether it’s HR or marketing or your product development or your engineering or your R&D, they’re all using data.

And if you are building out your data capabilities, your skills, you’re managing it better, then, ultimately, you’ve got the power to influence innovation at your company, product direction, where investment goes, and so on and so forth. This a stat I used at a recently recent conference. The word you started this podcast with: the term planet. The planet has created more data in the last two years than in its entire history.

So in other words, like, the exponential growth of data is so fast that we’ve created more in the last two years than we have since the beginning of humankind. And so you can imagine what that means for the next five years. And so if you’re sort of thinking about how to manage data for your firm you’ve got the ability, I think, to influence all sorts of things. And you should be sort of taking that awareness of your data management practices, your data management inventory, and get sort of get use that to get involved in the direction of the firm, I think.

Malcolm

So you used the word influence multiple times there, and I love that because as a product person, I would argue that influence is a big part of what we do. You could kinda call it sales, potentially. You could call it product marketing if you want.

But what role, either from an individual product manager perspective or maybe it is part of your role, maybe it’s part of everybody’s role, what role does kind of product advocacy play for you? And how does that actually kind of kind of transcribe in terms of or how does that actually get operationalized from the perspective of your interactions with everybody else in the C-suite or your internal customers? You know, data people may call that, like, storytelling.

I just think it’s just product marketing. But tell me a little bit more about how if there are ways to formalize some of the influence channels that you were just talking about.

Jonathan

I think so first of all, as my one of my favorite bosses said, we are all in sales. So it doesn’t matter if you’re a technologist, a data steward, whatever. We’re all we’re all in sales to some extent because you’ve gotta get your ideas across. The other thing I think that’s really important for any for in the data world is the ability to simplify the story. We have we have to tell the Crayola version of the story because at the end of the day, data is intricate and complicated, and there’s reference data. And you’re gonna use words like ontology, and executives are gonna roll their eyes back.

And so while while the if you’re in the day if you are a data geek and you and you spend your time with data governance and ontologies and metadata and all that stuff, it’s very, very obvious what we’re talking about, and those words are useful. When you’re at the C-suite and you’re talking about building using data to drive a strategy, you have to simplify the message and make it so whether you’re using analogies or you’re using stick figures, you gotta figure out a way to simplify the message such that you can explain the art of the possible, the potential of what we could do if we leverage the data, but you gotta tell it in such a way that someone who’s not steeped in it every single day could actually understand.

Malcolm

I love it. And speak the language of your customers. Nobody wants to hear the techno babble. Maybe some technical people here and there, but for the most part, this the C-suite and senior leadership wants to hear about results. Right? More revenue, lower cost, less risk. I think that’s a perfect way to end the conversation.

What’s a good way for anybody listening to this to to connect with you? Are you active on LinkedIn?

Jonathan

I’m very active on LinkedIn. I do represent BetaNXT in a number of conferences. In fact, we were just at an award dinner last night in the city, New York. And so, yeah, you can reach out to me on LinkedIn, and I’ll absolutely reply to you.

Malcolm

Fantastic. Jonathan Reeve, thank you so much for your time. For our listeners, for our subscribers, well, for our listeners, if you haven’t already subscribed to the CDO Manners podcast, I’d be absolutely positively thrilled if you did that.

Quick sales plug. My gosh. I didn’t I don’t have a copy of my book sitting at my desk. I should. My book by the time you’re watching this podcast, which will likely be released in the fall, my book will have been out a couple of months. Don’t forget to check out the data hero playbook available from Wiley. Go to Amazon dot com. Search data hero and Hawker. It’ll come in number one. I own the search term data hero. So be thrilled if you checked out my book. Don’t forget to subscribe.

Thanks so much for being a part of this community. Jonathan, thank you so much for your time. We will see you all in the on another episode of CDO Matters sometime very soon. Bye for now.

Source: The CDO Matters Podcast