Today, I’m responding to a listener's question about what it takes to succeed as a data or AI product manager, especially if you’re coming from roles like design/BI/data visualization, data science/engineering, or traditional software product management. This reader correctly observed that most of my content “seems more targeted at senior leadership” — and had asked if I could address this more IC-oriented topic on the show. I’ll break down why technical chops alone aren’t enough, and how user-centered thinking, business impact, and outcome-focused mindsets are key to real success — and where each of these prior roles brings strengths and/or weaknesses. I’ll also get into the evolving nature of PM roles in the age of AI, and what I think the super-powered AI product manager will look like.

Highlights/ Skip to:

Who can transition into an AI and data product management role? What does it take? (5:29) Software product managers moving into  AI product management (10:05) Designers moving into data/AI product management (13:32) Moving into the AI PM role from the engineering side (21:47) Why the challenge of user adoption and trust is often the blocker to the business value (29:56) Designing change management into AI/data products as a skill (31:26) The challenge of value creation vs. delivery work — and how incentives are aligned for ICs  (35:17) Quantifying the financial value of data and AI product work(40:23)

Quotes from Today’s Episode

“Who can transition into this type of role, and what is this role? I’m combining these two things. AI product management often seems closely tied to software companies that are primarily leveraging AI, or trying to, and therefore, they tend to utilize this AI product management role. I’m seeing less of that in internal data teams, where you tend to see data product management more, which, for me, feels like an umbrella term that may include traditional analytics work, data platforms, and often AI and machine learning. I’m going to frame this more in the AI space, primarily because I think AI tends to capture the end-to-end product than data product management does more frequently.” — Brian (2:55)

“There are three disciplines I’m going to talk about moving into this role. Coming into AI and data PM from design and UX, coming into it from data engineering (or just broadly technical spaces), and then coming into it from software product management. I think software product management and moving into the AI product management - as long as you’re not someone that has two years of experience, and then 18 years of repeating the second year of experience over and over again - and you’ve had a robust product management background across some different types of products; you can show that the domain doesn’t necessarily stop you from producing value. I think you will have the easiest time moving into AI product management because you’ve shown that you can adapt across different industries.” - Brian (9:45)

“Let’s talk about designers next. I’m going to include data visualization, user experience research, user experience design, product design, all those types of broad design, category roles. Moving into data and/or AI product management, first of all, you don’t see too many—I don’t hear about too many designers wanting to move into DPM roles, because oftentimes I don’t think there’s a lot of heavy UI and UX all the time in that space. Or at least the teams that are doing that work feel that’s somebody else’s job because they’re not doing end-to-end product thinking the way I talk about it, so therefore, a lot of times they don’t see the application, the user experience, the human adoption, the change management, they’re just not looking at the world that way, even though I think they should be.” - Brian (13:32)

“Coming at this from the data and engineering side, this is the classic track for data product management. At least that is the way I tend to see it. I believe most companies prefer to develop this role in-house. My biggest concern is that you end up with job title changes, but not necessarily the benefits that are supposed to come with this. I do like learning by doing, but having a coach and someone senior who can coach your other PMs is important because there’s a lot of information that you won’t necessarily get in a class or a course. It’s going to come from experience doing the work.” - Brian (22:26)

“This value piece is the most important thing, and I want to focus on that. This is something I frequently discuss in my training seminar: how do we attach financial value to the work we’re doing? This is both art and science, but it’s a language that anyone in a product management role needs to be comfortable with. If you’re finding it very hard to figure out how your data product contributes financial value because it’s based on this waterfalling of “We own the model, and it’s deployed on a platform.” The platform then powers these other things, which in turn power an application. How do we determine the value of our tool? These things are challenging, and if it’s challenging for you, guess how hard it will be for stakeholders downstream if you haven’t had the practice and the skills required to understand how to estimate value, both before we build something as well as after?” - Brian (31:51)

“If you don’t want to spend your time getting to know how your business makes money or creates value, then [AI and data product management work] is not for you. It’s just not. I would stay doing what you’re doing already or find a different thing because a lot of your time is going to be spent “managing up” for half the time, and then managing the product stuff “down.” Then, sitting in this middle layer, trying to explain to the business what’s going to come out and what the impact is going to be, in language that they care about and understand. You can't be talking about models, model accuracy, data pipelines, and all that stuff. They’re not going to care about any of that. - Brian (34:08)

This week on Experiencing Data, I chat with a new kindred spirit! Recently, I connected with Thabata Romanowski—better known as "T from Data Rocks NZ"—to discuss her experience applying UX design principles to modern analytical data products and dashboards. T walks us through her experience working as a data analyst in the mining sector, sharing the journey of how these experiences laid the foundation for her transition to data visualization. Now, she specializes in transforming complex, industry-specific data sets into intuitive, user-friendly visual representations, and addresses the challenges faced by the analytics teams she supports through her design business. T and I tackle common misconceptions about design in the analytics field, discuss how we communicate and educate non-designers on applying UX design principles to their dashboard and application design work, and address the problem with "pretty charts." We also explore some of the core ideas in T's Design Manifesto, including principles like being purposeful, context-sensitive, collaborative, and humanistic—all aimed at increasing user adoption and business value by improving UX.

Highlights/ Skip to:

I welcome T from Data Rocks NZ onto the show (00:00) T's transition from mining to leading an information design and data visualization consultancy. (01:43) T discusses the critical role of clear communication in data design solutions. (03:39) We address the misconceptions around the role of design in data analytics. (06:54)  T explains the importance of journey mapping in understanding users' needs. (15:25) We discuss the challenges of accurately capturing end-user needs. (19:00)  T and I discuss the importance of talking directly to end-users when developing data products. (25:56)  T shares her 'I like, I wish, I wonder' method for eliciting genuine user feedback. (33:03) T discusses her Data Design Manifesto for creating purposeful, context-aware, collaborative, and human-centered design principles in data. (36:37) We wrap up the conversation and share ways to connect with T. (40:49)

Quotes from Today’s Episode "It's not so much that people…don't know what design is, it's more that they understand it differently from what it can actually do..." - T from Data Rocks NZ (06:59) "I think [misconception about design in technology] is rooted mainly in the fact that data has been very tied to IT teams, to technology teams, and they’re not always up to what design actually does.” - T from Data Rocks NZ (07:42)  “If you strip design of function, it becomes art. So, it’s not art… it’s about being functional and being useful in helping people.” - T from Data Rocks NZ (09:06)

"It’s not that people don’t know, really, that the word design exists, or that design applies to analytics and whatnot; it’s more that they have this misunderstanding that it’s about making things look a certain way, when in fact... It’s about function. It’s about helping people do stuff better." - T from Data Rocks NZ (09:19) “Journey Mapping means that you have to talk to people...  Data is an inherently human thing. It is something that we create ourselves. So, it’s biased from the start. You can’t fully remove the human from the data" - T from Data Rocks NZ (15:36)  “The biggest part of your data product success…happens outside of your technology and outside of your actual analysis. It’s defining who your audience is, what the context of this audience is, and to which purpose do they need that product. - T from Data Rocks NZ (19:08) “[In UX research], a tight, empowered product team needs regular exposure to end customers; there’s nothing that can replace that." - Brian O'Neill (25:58)

“You have two sides [end-users and data team]  that are frustrated with the same thing. The side who asked wasn’t really sure what to ask. And then the data team gets frustrated because the users don’t know what they want…Nobody really understood what the problem is. There’s a lot of assumptions happening there. And this is one of the hardest things to let go.” - T from Data Rocks NZ (29:38) “No piece of data product exists in isolation, so understanding what people do with it… is really important.” - T from Data Rocks NZ (38:51)

Links Design Matters Newsletter: https://buttondown.email/datarocksnz  Website: https://www.datarocks.co.nz/ LinkedIn: https://www.linkedin.com/company/datarocksnz/ BlueSky: https://bsky.app/profile/datarocksnz.bsky.social Mastodon: https://me.dm/@datarocksnz

Today, I chat with Manav Misra, Chief Data and Analytics Officer at Regions Bank. I begin by asking Manav what it was like to come in and implement a user-focused mentality at Regions, driven by his experience in the software industry. Manav details his approach, which included developing a new data product partner role and using effective communication to gradually gain trust and cooperation from all the players on his team. 

Manav then talks about how, over time, he solidified a formal framework for his team to be trained to use this approach and how his hiring is influenced by a product orientation. We also discuss his definition of data product at Regions, which I find to be one of the best I’ve heard to date. Today, Region Bank’s data products are delivering tens of millions of dollars in additional revenue to the bank. Given those results, I also dig into the role of design and designers to better understand who is actually doing the designing of Regions’ data products to make them so successful. Later, I ask Manav what it’s like when designers and data professionals work on the same team and how UX and data visualization design are handled at the bank. 

Towards the end, Manav shares what he has learned from his time at Regions and what he would implement in a new organization if starting over. He also expounds on the importance of empowering his team to ask customers the right questions and how a true client/stakeholder partnership has led to Manav’s most successful data products.

Highlights / Skip to:

Brief history of decision science and how it influenced the way data science and analytics work has been done (and unfortunately still is in many orgs) (1:47) Manav’s philosophy and methods for changing the data science culture at Regions Bank to being product and user-driven (5:19) Manav talks about the size of his team and the data product role within the team as well as what he had to do to convince leadership to buy in to the necessity of the data product partner role (10:54) Quantifying and measuring the value of data products at Regions and some of his results (which include tens of millions of dollars in additional revenue) (13:05) What’s a “data product” at Regions? Manav shares his definition (13:44) Who does the designing of data products at Regions? (17:00) The challenges and benefits of having a team comprised of both designers and data scientists (20:10) Lessons Manav has learned from building his team and culture at Regions (23:09) How Manav coaches his team and gives them the confidence to ask the right questions (27:17) How true partnership has led to Manav’s most successful data products (31:46)

Quotes from Today’s Episode Re: how traditional, non-product oriented enterprises do data work: “As younger people come out of data science programs…that [old] culture is changing. The folks coming into this world now are looking to make an impact and then they want to see what this can do in the real world.” — Manav 

On the role of the Data Product Partner: “We brought in people that had both business knowledge as well as the technical knowledge, so with a combination of both they could talk to the ‘Internal customers,’ of our data products, but they could also talk to the data scientists and our developers and communicate in both directions in order to form that bridge between the two.” — Manav

“There are products that are delivering tens of millions of dollars in terms of additional revenue, or stopping fraud, or any of those kinds of things that the products are designed to address, they’re delivering and over-delivering on the business cases that we created.” — Manav 

“The way we define a data product is this: an end-to-end software solution to a problem that the business has. It leverages data and advanced analytics heavily in order to deliver that solution.” — Manav 

“The deployment and operationalization is simply part of the solution. They are not something that we do after; they’re something that we design in from the start of the solution.” — Brian 

“Design is a team sport. And even if you don’t have a titled designer doing the work, if someone is going to use the solution that you made, whether it’s a dashboard, or report, or an email, or notification, or an application, or whatever, there is a design, whether you put intention behind it or not.” — Brian

“As you look at interactive components in your data product, which are, you know, allowing people to ask questions and then get answers, you really have to think through what that interaction will look like, what’s the best way for them to get to the right answers and be able to use that in their decision-making.” — Manav 

“I have really instilled in my team that tools will come and go, technologies will come and go, [and so] you’ll have to have that mindset of constantly learning new things, being able to adapt and take on new ideas and incorporate them in how we do things.” — Manav

Links Regions Bank: https://www.regions.com/ LinkedIn: https://www.linkedin.com/in/manavmisra/

Today I talked with João Critis from Oi. Oi is a Brazilian telecommunications company that is a pioneer in convergent broadband services, pay TV, and local and long-distance voice transmission. They operate the largest fiber optics network in Brazil which reaches remote areas to promote digital inclusion of the population. João manages a design team at Oi that is responsible for the front end of data products including dashboards, reports, and all things data visualization. 

We begin by discussing João’s role leading a team of data designers. João then explains what data products actually are, and who makes up his team’s users and customers. João goes on to discuss user adoption challenges at Oi and the methods they use to uncover what users need in the last mile. He then explains the specific challenges his team has faced, particularly with middle management, and how his team builds credibility with senior leadership. In conclusion, João reflects on the value of empathy in the design process. 

In this episode, João shares:  

A data product  (4:48) The research process used by his data teams to build journey maps for clients (7:31) User adoption challenges for Oi (15:27) His answer to the question “how do you decide which mouths to feed?” (16:56) The unique challenges of middle management in delivering useful data products (20:33) The importance of empathy in innovation (25:23) What data scientists need to learn about design and vice versa (27:55)

Quotes from Today’s Episode

“We put the final user in the center of our process. We [conduct] workshops involving co-creation and prototyping, and we test how people work with data.” - João (8:22)

"My first responsibility here is value generation. So, if you have to take two or three steps back, another brainstorm, rethink, and rebuild something that works…. [well], this is very common for us.” - João (19:28)

“If you don’t make an impact on the individuals, you’re not going to make an impact on the business. Because as you said, if they don’t use any of the outputs we make, then they really aren’t solutions and no value is created. - Brian (25:07)

“It’s really important to do what we call primary research where you’re directly interfacing as much as possible with the horse’s mouth, no third parties, no second parties. You’ve really got to develop that empathy.” - Brian (25:23)

“When we are designing some system or screen or other digital artifact, [we have to understand] this is not only digital, but a product. We have to understand people, how people interact with systems, with computers, and how people interact with visual presentations.” - João (28:16)

Links Oi: https://www.oi.com.br/ LinkedIn: https://www.linkedin.com/in/critis/ Instagram: https://www.instagram.com/critis/