Experiencing Data w/ Brian T. O’Neill (UX for AI Data Products, SAAS Analytics, Data Product Management)

Jeremy Forman joins us to open up about the hurdles– and successes that come with building data products for pharmaceutical companies. Although he’s new to Pfizer, Jeremy has years of experience leading data teams at organizations like Seagen and the Bill and Melinda Gates Foundation. He currently serves in a more specialized role in Pfizer’s R&D department, building AI and analytical data products for scientists and researchers. .     Jeremy gave us a good luck at his team makeup, and in particular, how his data product analysts and UX designers work with pharmaceutical scientists and domain experts to build data-driven solutions..  We talked a good deal about how and when UX design plays a role in Pfizer’s data products, including a GenAI-based application they recently launched internally.       Highlights/ Skip to: (1:26) Jeremy's background in analytics and transition into working for Pfizer (2:42) Building an effective AI analytics and data team for pharma R&D (5:20) How Pfizer finds data products managers (8:03) Jeremy's philosophy behind building data products and how he adapts it to Pfizer (12:32) The moment Jeremy heard a Pfizer end-user use product management research language and why it mattered (13:55) How Jeremy's technical team members work with UX designers (18:00) The challenges that come with producing data products in the medical field (23:02) How to justify spending the budget on UX design for data products (24:59) The results we've seen having UX design work on AI / GenAI products (25:53) What Jeremy learned at the  Bill & Melinda Gates Foundation with regards to UX and its impact on him now (28:22) Managing the "rough dance" between data science and UX (33:22) Breaking down Jeremy's GenAI application demo from CDIOQ (36:02) What would Jeremy prioritize right now if his team got additional funding (38:48) Advice Jeremy would have given himself 10 years ago (40:46) Where you can find more from Jeremy     Quotes from Today’s Episode “We have stream-aligned squads focused on specific areas such as regulatory, safety and quality, or oncology research. That’s so we can create functional career pathing and limit context switching and fragmentation. They can become experts in their particular area and build a culture within that small team. It’s difficult to build good [pharma] data products. You need to understand the domain you’re supporting. You can’t take somebody with a financial background and put them in an Omics situation. It just doesn’t work. And we have a lot of the scars, and the failures to prove that.” - Jeremy Forman (4:12) “You have to have the product mindset to deliver the value and the promise of AI data analytics. I think small, independent, autonomous, empowered squads with a product leader is the only way that you can iterate fast enough with [pharma data products].” - Jeremy Forman (8:46) “The biggest challenge is when we say data products. It means a lot of different things to a lot of different people, and it’s difficult to articulate what a data product is. Is it a view in a database? Is it a table? Is it a query? We’re all talking about it in different terms, and nobody’s actually delivering data products.” - Jeremy Forman (10:53) “I think when we’re talking about [data products] there’s some type of data asset that has value to an end-user, versus a report or an algorithm. I think it’s even hard for UX people to really understand how to think about an actual data product. I think it’s hard for people to conceptualize, how do we do design around that? It’s one of the areas I think I’ve seen the biggest challenges, and I think some of the areas we’ve learned the most. If you build a data product, it’s not accurate, and people are getting results that are incomplete… people will abandon it quickly.” - Jeremy Forman (15:56) “ I think that UX design and AI development or data science work is a magical partnership, but they often don’t know how to work with each other. That’s been a challenge, but I think investing in that has been critical to us. Even though we’ve had struggles… I think we’ve also done a good job of understanding the [user] experience and impact that we want to have. The prototype we shared [at CDIOQ] is driven by user experience and trying to get information in the hands of the research organization to understand some portfolio types of decisions that have been made in the past. And it’s been really successful.” - Jeremy Forman (24:59) “If you’re having technology conversations with your business users, and you’re focused only the technology output, you’re just building reports. [After adopting If we’re having technology conversations with our business users and only focused on the technology output, we’re just building reports. [After we adopted  a human-centered design approach], it was talking [with end-users] about outcomes, value, and adoption. Having that resource transformed the conversation, and I felt like our quality went up. I felt like our output went down, but our impact went up. [End-users] loved the tools, and that wasn’t what was happening before… I credit a lot of that to the human-centered design team.” - Jeremy Forman (26:39) “When you’re thinking about automation through machine learning or building algorithms for [clinical trial analysis], it becomes a harder dance between data scientists and human-centered design. I think there’s a lack of appreciation and understanding of what UX can do. Human-centered design is an empathy-driven understanding of users’ experience, their work, their workflow, and the challenges they have. I don’t think there’s an appreciation of that skill set.” - Jeremy Forman (29:20) “Are people excited about it? Is there value? Are we hearing positive things? Do they want us to continue? That’s really how I’ve been judging success. Is it saving people time, and do they want to continue to use it? They want to continue to invest in it. They want to take their time as end-users, to help with testing, helping to refine it. Those are the indicators. We’re not generating revenue, so what does the adoption look like? Are people excited about it? Are they telling friends? Do they want more? When I hear that the ten people [who were initial users] are happy and that they think it should be rolled out to the whole broader audience, I think that’s a good sign.” - Jeremy Forman (35:19)   Links Referenced LinkedIn: https://www.linkedin.com/in/jeremy-forman-6b982710/

The relationship between AI and ethics is both developing and delicate. On one hand, the GenAI advancements to date are impressive. On the other, extreme care needs to be taken as this tech continues to quickly become more commonplace in our lives. In today’s episode, Ovetta Sampson and I examine the crossroads ahead for designing AI and GenAI user experiences.     While professionals and the general public are eager to embrace new products, recent breakthroughs, etc.; we still need to have some guard rails in place. If we don’t, data can easily get mishandled, and people could get hurt. Ovetta possesses firsthand experience working on these issues as they sprout up. We look at who should be on a team designing an AI UX, exploring the risks associated with GenAI, ethics, and need to be thinking about going forward.     Highlights/ Skip to: (1:48) Ovetta's background and what she brings to Google’s Core ML group (6:03) How Ovetta and her team work with data scientists and engineers deep in the stack (9:09)  How AI is changing the front-end of applications (12:46) The type of people you should seek out to design your AI and LLM UXs (16:15) Explaining why we’re only at the very start of major GenAI breakthroughs (22:34) How GenAI tools will alter the roles and responsibilities of designers, developers, and product teams (31:11) The potential harms of carelessly deploying GenAI technology (42:09) Defining acceptable levels of risk when using GenAI in real-world applications (53:16) Closing thoughts from Ovetta and where you can find her     Quotes from Today’s Episode “If artificial intelligence is just another technology, why would we build entire policies and frameworks around it? The reason why we do that is because we realize there are some real thorny ethical issues [surrounding AI]. Who owns that data? Where does it come from? Data is created by people, and all people create data. That’s why companies have strong legal, compliance, and regulatory policies around [AI], how it’s built, and how it engages with people. Think about having a toddler and then training the toddler on everything in the Library of Congress and on the internet. Do you release that toddler into the world without guardrails? Probably not.” - Ovetta Sampson (10:03) “[When building a team] you should look for a diverse thinker who focuses on the limitations of this technology- not its capability. You need someone who understands that the end destination of that technology is an engagement with a human being.  You need somebody who understands how they engage with machines and digital products. You need that person to be passionate about testing various ways that relationships can evolve. When we go from execution on code to machine learning, we make a shift from [human] agency to a shared-agency relationship. The user and machine both have decision-making power. That’s the paradigm shift that [designers] need to understand. You want somebody who can keep that duality in their head as they’re testing product design.” - Ovetta Sampson (13:45) “We’re in for a huge taxonomy change. There are words that mean very specific definitions today. Software engineer. Designer. Technically skilled. Digital. Art. Craft. AI is changing all that. It’s changing what it means to be a software engineer. Machine learning used to be the purview of data scientists only, but with GenAI, all of that is baked in to Gemini. So, now you start at a checkpoint, and you’re like, all right, let’s go make an API, right? So, the skills, the understanding, the knowledge, the taxonomy even, how we talk about these things, how do we talk about the machine who speaks to us talks to us, who could create a podcast out of just voice memos?” - Ovetta Sampson (24:16) “We have to be very intentional [when building AI tools], and that’s the kind of folks you want on teams. [Designers] have to go and play scary scenarios. We have to do that. No designer wants to be “Negative Nancy,” but this technology has huge potential to harm. It has harmed. If we don’t have the skill sets to recognize, document, and minimize harm, that needs to be part of our skill set.  If we’re not looking out for the humans, then who actually is?” - Ovetta Sampson (32:10) “[Research shows] things happen to our brain when we’re exposed to artificial intelligence… there are real human engagement risks that are an opportunity for design.  When you’re designing a self-driving car, you can’t just let the person go to sleep unless the car is fully [automated] and every other car on the road is self-driving. If there are humans behind the wheel, you need to have a feedback loop system—something that’s going to happen [in case] the algorithm is wrong. If you don’t have that designed, there’s going to be a large human engagement risk that a car is going to run over somebody who’s [for example] pushing a bike up a hill[...] Why? The car could not calculate the right speed and pace of a person pushing their bike. It had the speed and pace of a person walking, the speed and pace of a person on a bike, but not the two together. Algorithms will be wrong, right?” - Ovetta Sampson (39:42) “Model goodness used to be the purview of companies and the data scientists. Think about the first search engines. Their model goodness was [about] 77%. That’s good, right? And then people started seeing photos of apes when [they] typed in ‘black people.’ Companies have to get used to going to their customers in a wide spectrum and asking them when they’re [models or apps are] right and wrong.  They can’t take on that burden themselves anymore. Having ethically sourced data input and variables is hard work. If you’re going to use this technology, you need to put into place the governance that needs to be there.” - Ovetta Sampson (44:08)

Sometimes DIY UI/UX design only gets you so far—and you know it’s time for outside help. One thing prospects from SAAS analytics and data-related product companies often ask me is how things are like in the other guy/gal’s backyard. They want to compare their situation to others like them. So, today, I want to share some of the common “themes” I see that usually are the root causes of what leads to a phone call with me.      By the time I am on the phone with most prospects who already have a product in market, they’re usually either having significant problems with 1 or more of the following: sales friction (product value is opaque); low adoption/renewal worries (user apathy), customer complaints about UI/UX being hard to use; velocity (team is doing tons of work, but leader isn’t seeing progress)—and the like.      I’m hoping today’s episode will explain some of the root causes that may lead to these issues — so you can avoid them in your data product building work!       Highlights/ Skip to: (10:47) Design != "front-end development" or analyst work (12:34)  Liking doing UI/UX/viz design work vs. knowing  (15:04)  When a leader sees lots of work being done, but the UX/design isn’t progressing (17:31) Your product’s UX needs to convey some magic IP/special sauce…but it isn’t (20:25) Understanding the tradeoffs of using libraries, templates, and other solution’s design as a foundation for your own  (25:28) The sunk cost bias associated with POCs and “we’ll iterate on it” (28:31) Relying on UI/UX "customization" to please all customers (31:26) The hidden costs of abstraction of system objects, UI components, etc.  to make life easier for engineering and technical teams (32:32) Believing you’ll know the design is good “when you see it” (and what you don’t know you don’t know) (36:43) Believing that because the data science/AI/ML modeling under your solution was, accurate, difficult, and/or expensive makes it automatically worth paying for      Quotes from Today’s Episode The challenge is often not knowing what you don’t know about a project. We often end up focusing on building the tech [and rushing it out] so we can get some feedback on it… but product is not about getting it out there so we can get feedback. The goal of doing product well is to produce value, benefits, or outcomes. Learning is important, but that’s not what the objective is. The objective is benefits creation. (5:47) When we start doing design on a project that’s not design actionable, we build debt and sometimes can hurt the process of design. If you start designing your product with an entire green space, no direction, and no constraints, the chance of you shipping a good v1 is small. Your product strategy needs to be design-actionable for the team to properly execute against it. (19:19) While you don’t need to always start at zero with your UI/UX design, what are the parts of your product or application that do make sense to borrow , “steal” and cheat from? And when does it not?  It takes skill to know when you should be breaking the rules or conventions. Shortcuts often don’t produce outsized results—unless you know what a good shortcut looks like.  (22:28) A proof of concept is not a minimum valuable product. There’s a difference between proving the tech can work and making it into a product that’s so valuable, someone would exchange money for it because it’s so useful to them. Whatever that value is, these are two different things. (26:40) Trying to do a little bit for everybody [through excessive customization] can often result in nobody understanding the value or utility of your solution. Customization can hide the fact the team has decided not to make difficult choices. If you’re coming into a crowded space… it’s like’y not going to be a compelling reason to [convince customers to switch to your solution]. Customization can be a tax, not a benefit. (29:26) Watch for the sunk cost bias [in product development]. [Buyers] don’t care how the sausage was made. Many don’t understand how the AI stuff works, they probably don’t need to understand how it works. They want the benefits downstream from technology wrapped up in something so invaluable they can’t live without it.  Watch out for technically right, effectively wrong. (39:27)

In today’s episode, I’m joined by John Felushko, a product manager at LabStats who impressed me after we recently had a 1x1 call together. John and his team have developed a successful product that helps universities track and optimize their software and hardware usage so schools make smart investments. However, John also shares how culture and value are very tied together—and why their product isn’t a fit for every school, and every country. John shares how important  customer relationships are , how his team designs great analytics user experiences, how they do user research, and what he learned making high-end winter sports products that’s relevant to leading a SAAS analytics product. Combined with John’s background in history and the political economy of finance, John paints some very colorful stories about what they’re getting right—and how they’ve course corrected over the years at LabStats.      Highlights/ Skip to: (0:46) What is the LabStats product  (2:59) Orienting analytics around customer value instead of IT/data (5:51) "Producer of Persistently Profitable Product Process" (11:22) How they make product adjustments based on previous failures (15:55) Why a lack of cultural understanding caused LabStats to fail internationally (18:43) Quantifying value beyond dollars and cents (25:23) How John is able to work so closely with his customers without barriers (30:24) Who makes up the LabStats product research team (35:04) ​​How strong customer relationships help inform the UX design process (38:29) Getting senior management to accept that you can't regularly and accurately predict when you’ll be feature-complete and ship (43:51) Where John learned his skills as a successful product manager (47:20) Where you can go to cultivate the non-technical skills to help you become a better SAAS analytics product leader (51:00) What advice would John Felushko have given himself 10 years ago? (56:19) Where you can find more from John Felushko   Quotes from Today’s Episode “The product process is [essentially] really nothing more than the scientific method applied to business. Every product is an experiment - it has a hypothesis about a problem it solves. At LabStats [we have a process] where we go out and clearly articulate the problem. We clearly identify who the customers are, and who are [people at other colleges] having that problem. Incrementally and as inexpensively as possible, [we] test our solutions against those specific customers. The success rate [of testing solutions by cross-referencing with other customers] has been extremely high.” - John Felushko (6:46) “One of the failures I see in Americans is that we don’t realize how much culture matters. Americans have this bias to believe that whatever is valuable in my culture is valuable in other cultures. Value is entirely culturally determined and subjective. Value isn’t a number on a spreadsheet. [LabStats positioned our producty] as something that helps you save money and be financially efficient. In French government culture, financial efficiency is not a top priority. Spending government money on things like education is seen as a positive good. The more money you can spend on it, the better.  So, the whole message of financial efficiency wasn’t going to work in that market.” - John Felushko (16:35) “What I’m really selling with data products is confidence. I’m selling assurance. I’m selling an emotion. Before I was a product manager, I spent about ten years in outdoor retail, selling backpacks and boots. What I learned from that is you’re always selling emotion, at every level. If you can articulate the ROI, the real value is that the buyer has confidence they bought the right thing.” - John Felushko (20:29) “[LabStats] has three massive, multi-million dollar horror stories in our past where we [spent] millions of dollars in development work for no results. No ROI. Horror stories are what shape people’s values more than anything else. Avoiding negative outcomes is what people avoid more than anything else. [It’s important to] tell those stories and perpetuate those [lessons] through the culture of your organization. These are the times we screwed up, and this is what we learned from it—do you want to screw up like that again because we learned not to do that.” - John Felushko (38:45) “There’s an old description of a product manager, like, ‘Oh, they come across as the smartest person in the room.’ Well, how do you become that person? Expand your view, and expand the amount of information you consume as widely as possible. That’s so important to UX design and thinking about what went wrong. Why are some customers super happy and some customers not? What is the difference between those two groups of people? Is it culture? Is it time? Is it mental ability? Is it the size of the screen they’re looking at my product on? What variables can I define and rule out, and what data sources do I have to answer all those questions? It’s just the normal product manager thing—constant curiosity.” -John Felushko (48:04)