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Data, AI, & Domain Knowledge in Product Management


Episode 071

Are you ready to unlock the dynamic relationship between data and product management? We sat down with Mike Alvarez, a seasoned tech guru and the CTO and Head of Product for NeuZeit Group, who gave us an exploratory journey of how data can be a product in itself, or a catalyst for fantastic products, and shares valuable insights on how to ensure that data enriches a product without overshadowing its core offerings.


Ever wonder how data technology will change in the era of AI? In the second half of our chat, Mike zeroes in on the power of AI and ML, showing us how these tools can unearth insights we could only dream of just a year or so ago. He also emphasizes that domain knowledge and understanding your field is key to effectively leveraging data and offers some smart advice to product stakeholders looking to amp up their data literacy.


So, whether you're a seasoned tech veteran or a newbie seeking to gain a foothold in the world of data technology, this episode is a must-listen.


 

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Episode Transcript:


Intro (the incomparable Sandra Segrest): 0:03

Welcome to Product Voices, a podcast where we share valuable insights and useful resources to help us all be great in product management. Visit the show's website to access the resources discussed on the show, find more information on our fabulous guests, or to submit your product management question to be answered on our special Q&A episodes. That's all at ProductVoicescom, and be sure to subscribe to the podcast on your favorite platform. Now here's our host, JJ Rourie, CEO of Great Product Management.

JJ Rorie: 0:36

Hello and welcome to Product Voices. This is one of our special data episodes. We are going to be talking to some experts in data and product management and just learn all that we can about why data is important to product management, different aspects of using data, different aspects of different technologies, and really dive deep into data and product management in this series of episodes. So excited to have my guest here today to talk about data and product management and all kinds of other fun things. I've got Mike Alvarez, who is CTO and head of product for NeuZeit Group. Mike has been in data and in product for many years and so he's going to give a really brilliant insight into this conversation. So, Mike, thank you so much for joining me. I'm looking forward to conversing with you and talking about data.

Mike Alvarez: 1:32

Thank you for having me on Product Voices. I'd say product and data are probably two of the most, I guess, for me fascinating things to talk about, so I really appreciate the invitation.

JJ Rorie: 1:42

Yeah, it's going to be fun. So you know, I very briefly gave your intro on what you're doing now, which I want to hear a little bit more if you want to share. But tell me a little bit more about your background and about your journey, because I know you've just got a fascinating background and experience with data and in different aspects in and around products. So give us just a little bit of your bio and your experience and that will set the stage for some of the furthering conversation.

Mike Alvarez: 2:12

Yeah, happy to so. For me, well, I often say I don't really kind of neatly fit in any kind of a box. I think it just you know a diversity of background over the years. But you started out as a software engineer many, many years ago at a company called CompuServe for anybody that's old enough to remember it. But it did that, it did consulting. I did a startup for five years. I did you know about a decade in a large vehicle manufacturing financial services in the company, where I learned a lot about leadership and kind of doing things at an international scale. And then the last almost decade in a healthcare distribution company working as a kind of a leader in the data space. And throughout that time, you know I had some kind of experiences with products, I guess, and some with data. But my last handful was a handful of years really was able to kind of pull all that together and you know I was able to focus on engineering new software products, largely around data or data science insights kind of being embedded in these products. So I was able to kind of pull a lot together in my last, I'll say, three to four years at my last opportunity and currently the last three or four months of actually building a new product. It's part of this new venture, as you mentioned, with Noyte-Thye Group, and we've actually created a product, for it's basically a product manager's co-pilot kind of integrating data and some of the new generative AI technology.

JJ Rorie: 3:51

That sounds fascinating. So you know, I love your background because you've used data and been in and around product in many different ways and I think it's kind of a great microcosm of what data is and can be to product. I mean, data can be a product itself, right. There are many data products out there and then there are many instances and I know you've worked on some where data is either a byproduct or a component or an enabler of great products, right. And so I think you know it's interesting to think about data and you know when I was thinking about this kind of the series of episodes around data, that can really run the gamut. So I want to ask you about your experience and specifically maybe some of your past experience. I want to get to this, to the new thing you're doing as well, which is really cool, but some of your past experiences were, I believe, more around using data or helping certain users, certain personas, certain customers get more out of a product or get more out of the service by implementing and using data right. So it kind of allowed them to do their thing while you and the product and the data kind of enhanced their ability. Is that a fair way to say some of the products you've built. Yeah, absolutely yeah. So tell me about that. Like what it's like you're building a product that the data isn't necessarily the front and center part or, you know, features, functionality of the product, and frankly, that can be a good thing, right the fact it can kind of be invisible. How did you and your team go about making sure the data was valuable but didn't take away from kind of the core of the service or product that was being built in the first place?

Mike Alvarez: 5:53

Yeah. So if I go back, maybe a little bit further, I know, like some of my data architects that worked for me or I worked with, I think they'd always say, you know, and I was more on the engineering technology side of things, and they would say, Mike, it's all about the data dummy. And I'm like, yeah, yeah, it's about the data. But I think I learned a lesson over time and I really began to see that some of the challenges that we were facing as a company and some of the companies I worked for really were, you know, the answer was in the data right. And I really, over time, shifted my focus from, you know, caring more about the technology and caring more about the the data right and how we were aggregating and storing it, because we kind of had this saying that the data knows right. So the data kind of knows, like. I think one of my early hypotheses was like can I predict customer churn right or customer attrition? Because I mean you think about the data, and the data I mean you as a consumer. If you're unhappy with the service that you're paying for month after month, you probably call customer service more. You probably have a kind of a negative sentiment to your voice. You may hold on to your money later, right, or longer. You may kind of just like I'm going to wait the last minute to pay this bill because I'm not happy with the service. Well, those are all things that can be picked up in the data right, and we can figure that out, but it's so. I think it has been historically so hard to pull that information together, to aggregate it and to do all the work that is required to build that into an insight and a product. But I think that became for me over time really a main focus. It's not the only ingredient that goes into the recipe, but to me it's one of the primary ones around data and then also trying to think about or trying to think of data as a product itself, I think is a whole new kind of way to really shape the data and make sure that you're extracting the right level of value out of it.

JJ Rorie:8:05

Yeah, absolutely. I mean because you kind of have to have the end in mind, to make sure you're like you said you're capturing the right data, you're storing it the right way, you're extracting it. All of that, which is again from a user perspective, unless they're a kind of superpower user. On the other end, a lot of users don't care about that. They want us to make it as easy as possible, but it's our job as data and product folks to make that easy. So I want to turn a little bit and ask you about some of the newer at least very highly visibly new kind of transformative technologies and data tools that we're seeing these days, which is, of course, ai and ML and all those things not terribly new, but certainly getting the visibility that they never have before. So these can really be transformative. As I said, they can change the way that data is used. They can change the way that so many things happen in and around products. So do you have expectations for that? Is that something you're focused on? Tell me your thoughts about AI and ML and how those kind of play into the data and product world.

Mike Alvarez: 9:24

Yeah, I think there's a great confluence for anybody who understands product right and then also kind of understands, I guess, data from how do I get insights out of it and then how to leverage data science to kind of, I guess, create that kind of augmented intelligence that would benefit an end customer or an insight that would benefit a customer, and I can use many examples. But I think I don't want to. I kind of want to maybe I wish we were kind of on a video podcast, because it'd be great to really heavily underscore products right, because I think not too many more people really need to understand product management and product discipline. And I think you said I've been a part of companies that were product companies. I as an engineer honestly didn't appreciate it and I think kind of my maybe second or third time through working with product managers are really getting a much greater appreciation for how important that is and how important product management is, into what you're doing, either in a software product or even AI or with data, because I think you're really focused on kind of what problem does that end consumer, that customer, need us to solve, what's most important, and is there value in solving it right? And I think kind of, like I said with a recent example, what we built with a product called Project Sense is really that against a project or product or program managers co-pilot, where we're leveraging kind of a digital engagement model to kind of tease out of the team, the broader team, these signals which indicate kind of their feeling or belief of where this project, program or product is, how it's progressing in time and with that. So we've done a pilot internally, we've gathered data and the example I shared because I was kind of piloted on ourselves and we call it a friends and family pilot, so we gathered data and feedback. But I'll take my trend line of data where you know I scored it, you know highly that that my project or fictitious project was going well, week one, make two. Week three, week four, I felt like it was going poorly, so I scored it low. And then you know, weeks Five and six, I you know, scored a kind of medium high, right. So. So one, we're gathering data that didn't exist before on a project. You can get all kind of metrics like velocity and things like that, but you know, trying to get that signal from a human is really hard. And and Secondly, when you get that data. Even if you look at it, just do a, you know kind of, just do a you know basically kind of line graph on it. You would look at it, say okay, we're doing okay. But if you run it through like a regression algorithm algorithm, you actually see that there's a downward trend to that data. And then it would take three weeks of really high scores, that kind of get me and that feedback sponsor you kind of back to you know kind of level, you know level status basically. So you know, with that example, you're able to kind of you know, gather new data and then run it through an algorithm, right, and then you know, give this insight to this project leader who didn't have that insight prior, right. So I think there's just amazing ways that we can gather, you know, a lot, a lot of better insights out of the technology we have today than was possible even just a year ago.

JJ Rorie: 12:55

That's really fascinating. It's great example of you know how we can use some of these technologies to Do some things and to bring in more, more intelligence actually. And one of the things that I love that example, because projects are constantly, you know, project teams are constantly saying, yeah, we're on track or we're not on track or what have you, and it is somewhat Subjective, anecdotal, right. There's not a lot of, there's not always a lot of data behind it, and so I think using something like that, a tool to show them can, can give the, the overall group, more confidence but also kind of freeze up maybe some of their time. So I think that's a really interesting example. I am curious, though when do you think so? So using, you know, a ML data technology, any kind of technology? Honestly, you could, you could say this with, but but specifically around AI and ML, just because they're so Big right now. There's so there's so much excitement, there's so much concern, etc. Where do you think Domain knowledge comes in right? Domain knowledge about the project, but domain knowledge about the industry, about the product, about the users, and you know how, how, how are you seeing that Kind of playing into this? Can I replace that? Or is this where human intelligence is the differentiating factor. Like? What do you think about domain knowledge, human knowledge playing a part with AI or other data technology, for that matter?

Mike Alvarez: 14:27

Yeah, no, but I think it's critical. I mean I used to, I used to coach my team and saying, you know, in order to solve a problem within a, you know within a space, so whether you're you know being in the healthcare space, so if it's a provider or doctor space, or you know distribution or wherever it is, or financial services, because you know financial services, say banking, sort of like that. I mean you really need to understand the domain, and I think that's one of the mistakes that people Often make is they're like, oh, I can go, I get this great idea and I can go create this new thing over there. And it's like what do you understand about that domain? Well, nothing, but I got this great technology right, I get this great AI technology. It's like, well, and you know they may stumble on something or they may, you know, kind of I I hate to get lucky, but you know they may build something. That's a value. But I think that's where a lot of products that just kind of miss the mark because they're not thinking about it from a product perspective, not thinking about the end consumer and having empathy for the customer, or they really just don't have the domain knowledge to solve the problem. Again, over my career, that's something I've really learned Currently for me. Now in NeuZeit, I'm working with a bunch of project managers, so they've seen a lot of different problems, so I've teased out of them this domain knowledge and obviously I've been part of myself, but I wouldn't call myself a professional project manager. A lot of that went into our product. So I think, regardless of the domain, regardless of the area you're trying to create a solution for, or you're trying to innovate in. I think building that either bringing somebody onto your team that has that deep domain knowledge, or building yourself, or pivoting to a space that you really understand I think is really important.

JJ Rorie:16:13

I think that's great advice. I think technology is truly amazing and some of the technologies that we see and will continue to see and innovate around was really game-changing. But I still think it comes down to, or I still think some of the differentiation can be that domain knowledge, and you hit it right on the head in terms of customer knowledge and knowing your customers. And I think the world is littered with technologies looking for a problem to solve or a solution looking for a problem, and I think that's a great example of start with the user in mind, and then that technology can certainly find some unique ways to solve problems, but it's got to be surrounding that user and that domain. So real good advice there. If you're thinking about product managers or even stakeholders, so others within the product team who maybe historically haven't worked around a lot of data, maybe their products didn't have a lot of data, maybe they're just not experts or have a high level of literacy around data and what that can do for a product, whether it be embedded in the product or whether it be using to analyze a product or what have you Any advice for folks who want to just learn and increase their literacy, their knowledge level here. They don't have to become data experts, but at least have the level of understanding that helps them in their product role. Any advice for folks how to learn?

Mike Alvarez: 17:54

Yeah, I think I want to say I learned it the hard way, but I definitely learned through experience, I guess and how to really treat data as a product. I think we so in my last company we kind of quickly gravitated towards that because we were really a it was very much a product-centric organization, but by and large I don't think we were treating data as a product. And I really wanted to. As we were building a new platform, I really wanted to fully explore that because it felt like it was the right thing to do and it's a lot of the same reasons you want to build a software product with that same mentality, right as like which problem I'm solving? Is anybody find this useful? What's the value of building it this way? We had started on that path and then we stumbled upon Zhamak Dehghani's work with Datamesh. That's one of the you know kind of. One of the first principles she really described is kind of this domain-driven design going to treating data as a product. So that's a great resource to turn to for people you know, and then just challenging yourself to around. You know it can't, because I honestly won't be sort of that journey. I didn't. I didn't have. I felt like it was the right direction to go, but I didn't have you know the exact recipe in order to kind of create the product and over time it's. Another piece of advice is just continue to challenge yourself. You know, against those kind of product principles and say, you know, is this valuable right? Go out and and you know anybody that's going to use that, that data product you're creating Go, go talk to them right at what. You know what kind of insights that they need to get out of that product. It is what you're building useful to them, right? And you know, if not, then how can you pivot and make it useful. But I think you know you can definitely apply you know, the product management principles to to data. But it does take some Even you know for me who I worked in data for a long time but it just it took me a little while to really kind of shift fully, fully dive as deep as I needed to and shift my thinking over to okay, this is what it means to really be a data product.

JJ Rorie: 20:09

Yeah, you know it's, it's really interesting and I love this conversation For many reasons, but but one particular one is that it's just illustrated so much that at the core of Every product, regardless of what it is is, is the user and the customer and the business, and are we doing things that matter to them Right? And the solution? And and I often say that the product managers are, you know, they're experts on the problem who cares about the solution? And and I say that for a reason and dramatic for effect Of course we care about the solution, but if we don't get the product right, excuse me, if we don't get the problem right, which means we don't understand the customer, the user, did they really need this? Do they know they need this? Do they know they have this problem? If we don't get all of that right, does it matter how cold the solution is, doesn't matter how amazing the solution is, it's just not going to resonate, and so you know it's. We've talked about data a lot Intentionally, but, but at the end of the day, it's really about what can that data do, what can that solution do? And I think that's a really important part of this conversation, and anyone out there listening who, either you know, owns a data product or wants to use data to be a better product manager. That's great, but, you know, make sure that you're also focusing on the things that is core to every product in the world, which is is the solving a problem for some user.

Mike Alvarez: 21:38

Yeah, totally, and I heard a great quote and I can't remember which podcast I heard an honor, which leader to attribute it to, but I think it was something like we're not in gymnastics, so there are no extra points for difficult.

JJ Rorie: 21:48

I love that right.

Mike Alvarez:21:50

Just I mean, and and I'll. I saw a bit. You know this is a lot of engineers in this way. But I, you know, when I learned Multi-threading in my early days of C++, everything was multi-threaded. It didn't have to be multi-threaded, I just made it multi-threaded because it was really fun. And you know, I see the same trait in engineers. Or you know data scientists that work for me. You know they they would just get, so, you know, kind of into solving the problem and getting the technology, and even today I'm still drawn to that. But it's really that kind of that product mentality that keeps me back. Okay, that thing is exciting, that thing's fun. You know, in my own free time I can play with it. But today I got to get this feature done Because that's the next most important feature that we're gonna do.

JJ Rorie: 22:32

Yeah, I actually love that that quote. No extra points for difficulty. Wow, I think we all, we all need to adhere to that from time to time, because the truth is, most of us in product management Are natural problem solvers. Whether we came from engineering or not, it's just kind of part of who we are. We're naturally curious. It's why we, you know, somehow ended up in product management, and so we, we love these solutions, we love tinkering and playing and you know, to your point, there's there's a time and place for that and it certainly can help. But you know, we've got to make sure that we're not over complicating things or or doing doing that in lieu of the other important stuff. So, I love, that's a. That's a perfect way. I'm gonna get that on a on a t-shirt, I think. No extra points for difficulty. That's awesome. Yeah, that's a good tagline. Mike Alvarez, this has been such a fun conversation and really enlightening for me. I I've loved learning from your experience and you have such a really deep, you know, understanding and spirit experience level of data and I love, kind of how you've pivoted and turned it into, you know, this new product and new new group that you're you're starting out and co-founding and, wow, really, really fun. I can't wait to keep keep looking at what you're doing there, but thank you so much for joining me and for sharing your wisdom about data and about products and all of this fun stuff. So we've been talking about. Thanks, mike enjoyed the conversation.

Mike Alvarez: 23:54

Great Thanks, JJ, for having me on the podcast.

JJ Rorie: 24:00

And thank you all for listening to product voices. Hope to see you on the next episode.

Intro (the incomparable Sandra Segrest): 24:02

Thank you for listening to product voices hosted by JJ Rorie. To find more information on our guests, resources discussed during the episode, or to submit a question for our Q&A episodes, visit the show's website, productVoices.com, and be sure to subscribe to the podcast on your favorite platform.


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