In this episode, I sat down with tech humanist Kate O’Neill to explore how organizations can balance human-centered design in a time when everyone is racing to find ways to leverage AI in their businesses. Kate introduced her “Now–Next Continuum,” a framework that distinguishes digital transformation (catching up) from true innovation (looking ahead). We dug into real-world challenges and tensions of moving fast vs. creating impact with AI, how ethics fits into decision making, and the role of data in making informed decisions. 

Kate stressed the importance of organizations having clear purpose statements and values from the outset, proxy metrics she uses to gauge human-friendliness, and applying a “harms of action vs. harms of inaction” lens for ethical decisions. Her key point: human-centered approaches to AI and technology creation aren’t slow; they create intentional structures that speed up smart choices while avoiding costly missteps.

Highlights/ Skip to:

How Kate approaches discussions with executives about moving fast, but also moving in a human-centered way when building out AI solutions (1:03) Exploring the lack of technical backgrounds among many CEOs and how this shapes the way organizations make big decisions around technical solutions (3:58)  FOMO and the “Solution in Search of a Problem” problem in Data (5:18)  Why ongoing ethnographic research and direct exposure to users are essential for true innovation (11:21)  Balancing organizational purpose and human-centered tech decisions, and why a defined purpose must precede these decisions (18:09) How organizations can define, measure, operationalize, and act on ethical considerations in AI and data products (35:57) Risk management vs. strategic optimism: balancing risk reduction with embracing the art of the possible when building AI solutions (43:54)

Quotes from Today’s Episode "I think the ethics and the governance and all those kinds of discussions [about the implications of digital transformation] are all very big word - kind of jargon-y kinds of discussions - that are easy to think aren't important, but what they all tend to come down to is that alignment between what the business is trying to do and what the person on the other side of the business is trying to do." –Kate O’Neill

" I've often heard the term digital transformation used almost interchangeably with the term innovation. And I think that that's a grave disservice that we do to those two concepts because they're very different. Digital transformation, to me, seems as if it sits much more comfortably on the earlier side of the Now-Next Continuum. So, it's about moving the past to the present… Innovation is about standing in the present and looking to the future and thinking about the art of the possible, like you said. What could we do? What could we extract from this unstructured data (this mess of stuff that’s something new and different) that could actually move us into green space, into territory that no one’s doing yet? And those are two very different sets of questions. And in most organizations, they need to be happening simultaneously." –Kate O’Neill

"The reason I chose human-friendly [as a term] over human-centered partly because I wanted to be very honest about the goal and not fall back into, you know, jargony kinds of language that, you know, you and I and the folks listening probably all understand in a certain way, but the CEOs and the folks that I'm necessarily trying to get reading this book and make their decisions in a different way based on it." –Kate O’Neill

“We love coming up with new names for different things. Like whether something is “cloud,” or whether it’s like, you know, “SaaS,” or all these different terms that we’ve come up with over the years… After spending so long working in tech, it is kind of fun to laugh at it. But it’s nice that there’s a real earnestness [to it]. That’s sort of evergreen [laugh]. People are always trying to genuinely solve human problems, which is what I try to tap into these days, with the work that I do, is really trying to help businesses—business leaders, mostly, but a lot of those are non-tech leaders, and I think that’s where this really sticks is that you get a lot of people who have ascended into CEO or other C-suite roles who don’t come from a technology background.” 

–Kate O’Neill

"My feeling is that if you're not regularly doing ethnographic research and having a lot of exposure time directly to customers, you’re doomed. The people—the makers—have to be exposed to the users and stakeholders.  There has to be ongoing work in this space; it can't just be about defining project requirements and then disappearing. However, I don't see a lot of data teams and AI teams that have non-technical research going on where they're regularly spending time with end users or customers such that they could even imagine what the art of the possible could be.”

–Brian T. O’Neill

Links

KO Insights: https://www.koinsights.com/ LinkedIn for Kate O’Neill: https://www.linkedin.com/in/kateoneill/ Kate O’Neill Book: What Matters Next: A Leader's Guide to Making Human-Friendly Tech Decisions in a World That's Moving Too Fast

Todd Olson joins me to talk about making analytics worth paying for and relevant in the age of AI. The CEO of Pendo, an analytics SAAS company, Todd shares how the company evolved to support a wider audience by simplifying dashboards, removing user roadblocks, and leveraging AI to both generate and explain insights. We also talked about the roles of product management at Pendo. Todd views AI product management as a natural evolution for adaptable teams and explains how he thinks about hiring product roles in 2025. Todd also shares how he thinks about successful user adoption of his product around “time to value” and “stickiness” over vanity metrics like time spent. 

Highlights/ Skip to:

How Todd has addressed analytics apathy over the past decade at Pendo (1:17) Getting back to basics and not barraging people with more data and power (4:02) Pendo’s strategy for keeping the product experience simple without abandoning power users (6:44) Whether Todd is considering using an LLM (prompt-based) answer-driven experience with Pendo's UI (8:51) What Pendo looks for when hiring product managers right now, and why (14:58) How Pendo evaluates AI product managers, specifically (19:14) How Todd Olson views AI product management compared to traditional software product management (21:56) Todd’s concerns about the probabilistic nature of AI-generated answers in the product UX (27:51) What KPIs Todd uses to know whether Pendo is doing enough to reach its goals (32:49)   Why being able to tell what answers are best will become more important as choice increases (40:05)

Quotes from Today’s Episode

“Let’s go back to classic Geoffrey Moore Crossing the Chasm, you’re selling to early adopters. And what you’re doing is you’re relying on the early adopters’ skill set and figuring out how to take this data and connect it to business problems. So, in the early days, we didn’t do anything because the market we were selling to was very, very savvy; they’re hungry people, they just like new things. They’re getting data, they’re feeling really, really smart, everything’s working great. As you get bigger and bigger and bigger, you start to try to sell to a bigger TAM, a bigger audience, you start trying to talk to the these early majorities, which are, they’re not early adopters, they’re more technology laggards in some degree, and they don’t understand how to use data to inform their job. They’ve never used data to inform their job. There, we’ve had to do a lot more work.” Todd (2:04 - 2:58) “I think AI is amazing, and I don’t want to say AI is overhyped because AI in general is—yeah, it’s the revolution that we all have to pay attention to. Do I think that the skills necessary to be an AI product manager are so distinct that you need to hire differently? No, I don’t. That’s not what I’m seeing. If you have a really curious product manager who’s going all in, I think you’re going to be okay. Some of the most AI-forward work happening at Pendo is not just product management. Our design team is going crazy. And I think one of the things that we’re seeing is a blend between design and product, that they’re always adjacent and connected; there’s more sort of overlappiness now.” Todd (22:41 - 23:28) “I think about things like stickiness, which may not be an aggregate time, but how often are people coming back and checking in? And if you had this companion or this agent that you just could not live without, and it caused you to come into the product almost every day just to check in, but it’s a fast check-in, like, a five-minute check-in, a ten-minute check-in, that’s pretty darn sticky. That’s a good metric. So, I like stickiness as a metric because it’s measuring [things like], “Are you thinking about this product a lot?” And if you’re thinking about it a lot, and like, you can’t kind of live without it, you’re going to go to it a lot, even if it’s only a few minutes a day. Social media is like that. Thankfully I’m not addicted to TikTok or Instagram or anything like that, but I probably check it nearly every day. That’s a pretty good metric. It gets part of my process of any products that you’re checking every day is pretty darn good. So yeah, but I think we need to reframe the conversation not just total time. Like, how are we measuring outcomes and value, and I think that’s what’s ultimately going to win here.” Todd (39:57)

Links

LinkedIn: https://www.linkedin.com/in/toddaolson/  X: https://x.com/tolson  [email protected] 

R&D for materials-based products can be expensive, because improving a product’s materials takes a lot of experimentation that historically has been slow to execute. In traditional labs, you might change one variable, re-run your experiment, and see if the data shows improvements in your desired attributes (e.g. strength, shininess, texture/feel, power retention, temperature, stability, etc.). However, today, there is a way to leverage machine learning and AI to reduce the number of experiments a material scientist needs to run to gain the improvements they seek. Materials scientists spend a lot of time in the lab—away from a computer screen—so how do you design a desirable informatics SAAS that actually works, and fits into the workflow of these end users?    

As the Chief Product Officer at MaterialsZone, Ori Yudilevich came on Experiencing Data with me to talk about this challenge and how his PM, UX, and data science teams work together to produce a SAAS product that makes the benefits of materials informatics so valuable that materials scientists depend on their solution to be time and cost-efficient with their R&D efforts.   

We covered:

(0:45) Explaining what Ori does at MaterialZone and who their product serves (2:28) How Ori and his team help make material science testing more efficient through their SAAS product (9:37) How they design a UX that can work across various scientific domains (14:08) How “doing product” at MaterialsZone matured over the past five years (17:01) Explaining the "Wizard of Oz" product development technique (21:09) The importance of integrating UX designers into the "Wizard of Oz" (23:52) The challenges MaterialZone faces when trying to get users to adopt to their product (32:42) Advice Ori would've given himself five years ago (33:53) Where you can find more from MaterialsZone and Ori

Quotes from Today’s Episode

“The fascinating thing about materials science is that you have this variety of domains, but all of these things follow the same process. One of the problems [consumer goods companies] face is that they have to do lengthy testing of their products. This is something you can use machine learning to shorten. [Product research] is an iterative process that typically takes a long time. Using your data effectively and using machine learning to predict what can happen, what’s better to try out, and what will reduce costs can accelerate time to market.” - Ori Yudilevich (3:47) “The difference [in time spent testing a product] can be up to 70% [i.e. you can run 70% fewer experiments using ML.]  That [also] means 70% less resources you’re using. Under the ‘old system’ of trial and error, you were just trying out a lot of things. The human mind cannot process a large number of parameters at once, so [a materials scientist] would just start playing only with [one parameter at a time]. You’ll have many experiments where you just try to optimize [for] one parameter, but then you might have 20, 30, or 100 more [to test]. Using machine learning, you can change a lot of parameters at once. The model can learn what has the most effect, what has a positive effect, and what has a negative effect. The differences can be really huge.” - Ori Yudilevich (5:50) “Once you go deeper into a use case, you see that there are a lot of differences. The types of raw materials, the data structure, the quantity of data, etc. For example, with batteries, you have lots of data because you can test hundreds all at once. Whereas with something like ceramics, you don’t try so many [experiments]. You just can’t. It’s much slower. You can’t do so many [experiments] in parallel. You have much less data. Your models are different, and your data structure is different. But there’s also quite a lot of commonality because you’re storing the data. In the end, you have each domain, some raw materials, formulations, tests that you’re doing, and different statistical plots that are very common.” - Ori Yudilvech (11:24) “We’ll typically do what we call the ‘Wizard of Oz’ technique. You simulate as if you have a feature, but you’re actually working for your client behind the scenes. You tell them [the simulated feature] is what you’re doing, but then measure [the client’s response] to understand if there’s any point in further developing that feature. Once you validate it, have enough data, and know where the feature is going, then you’ll start designing it and releasing it in incremental stages. We’ve made a lot of progress in how we discover opportunities and how we build something iteratively to make sure that we’re always going in the right direction” - Ori Yudilevich (15:56) “The main problem we’re encountering is changing the mindset of users. Our users are not people who sit in front of a computer. These are researchers who work in [a materials science] lab. The challenge [we have] is getting people to use the platform more. To see it’s worth [their time] to look at some insights, and run the machine learning models. We’re always looking for ways to make that transition faster… and I think the key is making [the user experience] just fun, easy, and intuitive.” - Ori Yudilevich (24:17) “Even if you make [the user experience] extremely smooth, if [users] don’t see what they get out of it, they’re still not going to [adopt your product] just for the sake of doing it. What we find is if this [product] can actually make them work faster or develop better products– that gets them interested. If you’re adopting these advanced tools, it makes you a better researcher and worker. People who [adopt those tools] grow faster. They become leaders in their team, and they slowly drag the others in.” - Ori Yudilevich (26:55) “Some of [MaterialsZone’s] most valuable employees are the people who have been users. Our product manager is a materials scientist. I’m not a material scientist, and it’s hard to imagine being that person in the lab. What I think is correct turns out to be completely wrong because I just don’t know what it’s like. Having [material scientists] who’ve made the transition to software and data science? You can’t replace that.” - Ori Yudilevich (31:32)

Links Referenced Website: https://www.materials.zone

LinkedIn: https://www.linkedin.com/in/oriyudilevich/

Email: [email protected]

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)

In today’s episode, I’m going to perhaps work myself out of some consulting engagements, but hey, that’s ok! True consulting is about service—not PPT decks with strategies and tiers of people attached to rate cards. Specifically today, I decided to reframe a topic and approach it from the opposite/negative side. So, instead of telling you when the right time is to get UX design help for your enterprise SAAS analytics or AI product(s), today I’m going to tell you when you should NOT get help! 

Reframing this was really fun and made me think a lot as I recorded the episode. Some of these reasons aren’t necessarily representative of what I believe, but rather what I’ve heard from clients and prospects over 25 years—what they believe. For each of these, I’m also giving a counterargument, so hopefully, you get both sides of the coin. 

Finally, analytical thinkers, especially data product managers it seems, often want to quantify all forms of value they produce in hard monetary units—and so in this episode, I’m also going to talk about other forms of value that products can create that are worth paying for—and how mushy things like “feelings” might just come into play ;-)  Ready?

Highlights/ Skip to:

(1:52) Going for short, easy wins (4:29) When you think you have good design sense/taste  (7:09) The impending changes coming with GenAI (11:27) Concerns about "dumbing down" or oversimplifying technical analytics solutions that need to be powerful and flexible (15:36) Agile and process FTW? (18:59) UX design for and with platform products (21:14) The risk of involving designers who don’t understand data, analytics, AI, or your complex domain considerations  (30:09) Designing after the ML models have been trained—and it’s too late to go back  (34:59) Not tapping professional design help when your user base is small , and you have routine access and exposure to them   (40:01) Explaining the value of UX design investments to your stakeholders when you don’t 100% control the budget or decisions 

Quotes from Today’s Episode “It is true that most impactful design often creates more product and engineering work because humans are messy. While there sometimes are these magic, small GUI-type changes that have big impact downstream, the big picture value of UX can be lost if you’re simply assigning low-level GUI improvement tasks and hoping to see a big product win. It always comes back to the game you’re playing inside your team: are you working to produce UX and business outcomes or shipping outputs on time? ” (3:18) “If you’re building something that needs to generate revenue, there has to be a sense of trust and belief in the solution. We’ve all seen the challenges of this with LLMs. [when] you’re unable to get it to respond in a way that makes you feel confident that it understood the query to begin with. And then you start to have all these questions about, ‘Is the answer not in there,’ or ‘Am I not prompting it correctly?’ If you think that most of this is just an technical data science problem, then don’t bother to invest in UX design work… ” (9:52) “Design is about, at a minimum, making it useful and usable, if not delightful. In order to do that, we need to understand the people that are going to use it. What would an improvement to this person’s life look like? Simplifying and dumbing things down is not always the answer. There are tools and solutions that need to be complex, flexible, and/or provide a lot of power – especially in an enterprise context. Working with a designer who solely insists on simplifying everything at all costs regardless of your stated business outcome goals is a red flag—and a reason not to invest in UX design—at least with them!“ (12:28)“I think what an analytics product manager [or] an AI product manager needs to accept is there are other ways to measure the value of UX design’s contribution to your product and to your organization. Let’s say that you have a mission-critical internal data product, it’s used by the most senior executives in the organization, and you and your team made their day, or their month, or their quarter. You saved their job. You made them feel like a hero. What is the value  of giving them that experience and making them feel like those things… What is that worth when a key customer or colleague feels like you have their back with this solution you created? Ideas that spread, win, and if these people are spreading your idea, your product, or your solution… there’s a lot of value in that.” (43:33)

“Let’s think about value in non-financial terms. Terms like feelings. We buy insurance all the time. We’re spending money on something that most likely will have zero economic value this year because we’re actually trying not to have to file claims. Yet this industry does very well because the feeling of security matters. That feeling is worth something to a lot of people. The value of feeling secure is something greater than whatever the cost of the insurance plan. If your solution can build feelings of confidence and security, what is that worth? Does “hard to measure precisely” necessarily mean “low value?”  (47:26)

Due to a technical glitch that ended up unpublishing this episode right after it originally was released, Episode 151 is a replay of my conversation with Zalak Trivdei from this past March . Please enjoy our chat if you missed it the first time around!

Thanks,

Brian

Links Original Episode: https://designingforanalytics.com/resources/episodes/139-monetizing-saas-analytics-and-the-challenges-of-designing-a-successful-embedded-bi-product-promoted-episode/ 

Sigma Computing: https://sigmacomputing.com

Email: [email protected] 

LinkedIn: https://www.linkedin.com/in/trivedizalak/

Sigma Computing Embedded: https://sigmacomputing.com/embedded

About Promoted Episodes on Experiencing Data: https://designingforanalytics.com/promoted

Welcome back! In today's solo episode, I share the top five struggles that enterprise SAAS leaders have in the analytics/insight/decision support space that most frequently leads them to think they have a UI/UX design problem that has to be addressed. A lot of today's episode will talk about "slow creep," unaddressed design problems that gradually build up over time and begin to impact both UX and your revenue negatively. I will also share 20 UI and UX design problems I often see (even if clients do not!) that, when left unaddressed, may create sales friction, adoption problems, churn, or unhappy end users. If you work at a software company or are directly monetizing an ML or analytical data product, this episode is for you! 

Highlights/ Skip to 

I discuss how specific UI/UX design problems can significantly impact business performance (02:51) I discuss five common reasons why enterprise software leaders typically reach out for help (04:39) The 20 common symptoms I've observed in client engagements that indicate the need for professional UI/UX intervention or training (13:22) The dangers of adding too many features or customization and how it can overwhelm users (16:00) The issues of integrating  AI into user interfaces and UXs without proper design thinking  (30:08) I encourage listeners to apply the insights shared to improve their data products (48:02)

Quotes from Today’s Episode “One of the problems with bad design is that some of it we can see and some of it we can't — unless you know what you're looking for." - Brian O’Neill (02:23) “Design is usually not top of mind for an enterprise software product, especially one in the machine learning and analytics space. However, if you have human users, even enterprise ones, their tolerance for bad software is much lower today than in the past.” Brian O’Neill - (13:04) “Early on when you're trying to get product market fit, you can't be everything for everyone. You need to be an A+ experience for the person you're trying to satisfy.” -Brian O’Neill (15:39) “Often when I see customization, it is mostly used as a crutch for not making real product strategy and design decisions.”  - Brian O’Neill (16:04)  "Customization of data and dashboard products may be more of a tax than a benefit. In the marketing copy, customization sounds like a benefit...until you actually go in and try to do it. It puts the mental effort to design a good solution on the user." - Brian O’Neill (16:26) “We need to think strategically when implementing Gen AI or just AI in general into the product UX because it won’t automatically help drive sales or increase business value.” - Brian O’Neill (20:50)  “A lot of times our analytics and machine learning tools… are insight decision support products. They're supposed to be rooted in facts and data, but when it comes to designing these products, there's not a whole lot of data and facts that are actually informing the product design choices.” Brian O’Neill - (30:37) “If your IP is that special, but also complex, it needs the proper UI/UX design treatment so that the value can be surfaced in such a way someone is willing to pay for it if not also find it indispensable and delightful.” - Brian O’Neill (45:02)

Links The (5) big reasons AI/ML and analytics product leaders invest in UI/UX design help: https://designingforanalytics.com/resources/the-5-big-reasons-ai-ml-and-analytics-product-leaders-invest-in-ui-ux-design-help/  Subscribe for free insights on designing useful, high-value enterprise ML and analytical data products: https://designingforanalytics.com/list  Access my free frameworks, guides, and additional reading for SAAS leaders on designing high-value ML and analytical data products: https://designingforanalytics.com/resources Need help getting your product’s design/UX on track—so you can see more sales, less churn, and higher user adoption? Schedule a free 60-minute Discovery Call with me and I’ll give you my read on your situation and my recommendations to get ahead:https://designingforanalytics.com/services/

This week on Experiencing Data, something new as promised at the beginning of the year. Today, I’m exploring the world of embedded analytics with Zalak Trivedi from Sigma Computing—and this is also the first approved Promoted Episode on the podcast. In today’s episode, Zalak shares his journey as the product lead for Sigma’s embedded analytics and reporting solution which seeks to accelerate and simplify the deployment of decision support dashboards to their SAAS companies’ customers. Right there, we have the first challenge that Zalak was willing to dig into with me: designing a platform UX when we have multiple stakeholder and user types. In Sigma’s case, this means Sigma’s buyers, the developers that work at these SAAS companies to integrate Sigma into their products, and then the actual customers of these SAAS companies who will be the final end users of the resulting dashboards.  also discuss the challenges of creating products that serve both beginners and experts and how AI is being used in the BI industry.  

Highlights/ Skip to:

I introduce Zalak Trivedi from Sigma Computing onto the show (03:15) Zalak shares his journey leading the vision for embedded analytics at Sigma and explains what Sigma looks like when implemented into a customer’s SAAS product . (03:54) Zalak and I discuss the challenge of integrating Sigma's analytics into various companies' software, since they need to account for a variety of stakeholders. (09:53) We explore Sigma's team approach to user experience with product management, design, and technical writing (15:14) Zalak reveals how Sigma leverages telemetry to understand and improve user interactions with their products (19:54) Zalak outlines why Sigma is a faster and more supportive alternative to building your own analytics (27:21) We cover data monetization, specifically looking at how SAAS companies can monetize analytics and insights (32:05) Zalak highlights how Sigma is integratingAI into their BI solution (36:15) Zalak share his customers' current pain points and interests (40:25)  We wrap up with final thoughts and ways to connect with Zalak and learn more about Sigma (49:41) 

Quotes from Today’s Episode "Something I’m really excited about personally that we are working on is [moving] beyond analytics to help customers build entire data applications within Sigma. This is something we are really excited about as a company, and marching towards [achieving] this year." - Zalak Trivedi (04:04)

“The whole point of an embedded analytics application is that it should look and feel exactly like the application it’s embedded in, and the workflow should be seamless.” - Zalak Trivedi (09:29) 

“We [at Sigma] had to switch the way that we were thinking about personas. It was not just about the analysts or the data teams; it was more about how do we give the right tools to the [SAAS] product managers and developers to embed Sigma into their product.” - Zalak Trivedi (11:30)  “You can’t not have a design, and you can’t not have a user experience. There’s always an experience with every tool, solution, product that we use, whether it emerged organically as a byproduct, or it was intentionally created through knowledge data... it was intentional” - Brian O’Neill (14:52) 

“If we find that [in] certain user experiences,people are tripping up, and they’re not able to complete an entire workflow, we flag that, and then we work with the product managers, or [with] our customers essentially, and figure out how we can actually simplify these experiences.” - Zalak Trivedi (20:54)

“We were able to convince many small to medium businesses and startups to sign up with Sigma. The success they experienced after embedding Sigma was tremendous. Many of our customers managed to monetize their existing data within weeks, or at most, a couple of months, with lean development teams of two to three developers and a few business-side personnel, generating seven-figure income streams from that.” - Zalak Trivedi (32:05)

“At Sigma, our stance is, let’s not just add AI for the sake of adding AI. Let’s really identify [where] in the entire user journey does the intelligence really lie, and where are the different friction points, and let’s enhance those experiences.” - Zalak Trivedi (37:38)  “Every time [we at Sigma Computing] think about a new feature or functionality, we have to ensure it works for both the first-degree persona and the second-degree persona, and consider how it will be viewed by these different personas, because that is not the primary persona for which the foundation of the product was built." - Zalak Trivedi (48:08)

Links Sigma Computing: https://sigmacomputing.com

Email: [email protected] 

LinkedIn: https://www.linkedin.com/in/trivedizalak/

Sigma Computing Embedded: https://sigmacomputing.com/embedded

About Promoted Episodes on Experiencing Data: https://designingforanalytics.com/promoted

In this episode of Experiencing Data, I speak with Ellen Chisa, Partner at BoldStart Ventures, about what she’s seeing in the venture capital space around AI-driven products and companies—particularly with all the new GenAI capabilities that have emerged in the last year. Ellen and I first met when we were both engaged in travel tech startups in Boston over a decade ago, so it was great to get her current perspective being on the “other side” of products and companies working as a VC.  Ellen draws on her experience in product management and design to discuss how AI could democratize software creation and streamline backend coding, design integration, and analytics. We also delve into her work at Dark and the future prospects for developer tools and SaaS platforms. Given Ellen’s background in product management, human-centered design, and now VC, I thought she would have a lot to share—and she did!

Highlights/ Skip to: I introduce the show and my guest, Ellen Chisa (00:00) Ellen discusses her transition from product and design to venture capital with BoldStart Ventures. (01:15) Ellen notes a shift from initial AI prototypes to more refined products, focusing on building and testing with minimal data. (03:22) Ellen mentions BoldStart Ventures' focus on early-stage companies providing developer and data tooling for businesses.  (07:00) Ellen discusses what she learned from her time at Dark and Lola about narrowing target user groups for technology products (11:54) Ellen's Insights into the importance of user experience is in product design and the process venture capitalists endure to make sure it meets user needs (15:50) Ellen gives us her take on the impact of AI on creating new opportunities for data tools and engineering solutions, (20:00) Ellen and I explore the future of user interfaces, and how AI tools could enhance UI/UX for end users. (25:28) Closing remarks and the best way to find Ellen on online (32:07)

Quotes from Today’s Episode “It's a really interesting time in the venture market because on top of the Gen AI wave, we obviously had the macroeconomic shift. And so we've seen a lot of people are saying the companies that come out now are going to be great companies because they're a little bit more capital-constrained from the beginning, typically, and they'll grow more thoughtfully and really be thinking about how do they build an efficient business.”- Ellen Chisa (03: 22) 

“We have this big technological shift around AI-enabled companies, and I think one of the things I’ve seen is, if you think back to a year ago, we saw a lot of early prototyping, and so there were like a couple of use cases that came up again and again.”-Ellen Chisa (3:42)

“I don't think I've heard many pitches from founders who consider themselves data scientists first. We definitely get some from ML engineers and people who think about data architecture, for sure..”- Ellen Chisa (05:06)  

“I still prefer GUI interfaces to voice or text usually, but I think that might be an uncanny valley sort of thing where if you think of people who didn’t have technology growing up, they’re more comfortable with the more human interaction, and then you get, like, a chunk of people who are digital natives who prefer it.”- Ellen Chisa (24:51)

[Citing some excellent Boston-area restaurants!] “The Arc browser just shipped a bunch of new functionality, where instead of opening a bunch of tabs, you can say, “Open the recipe pages for Oleana and Sarma,” and it just opens both of them, and so it’s like multiple search queries at once.” - Ellen Chisa (27:22)

“The AI wave of  technology biases towards people who already have products [in the market] and have existing datasets, and so I think everyone [at tech companies] is getting this big, top-down mandate from their executive team, like, ‘Oh, hey, you have to do something with AI now.’”- Ellen Chisa (28:37)

“I think it’s hard to really grasp what an LLM is until you do a fair amount of experimentation on your own. The experience of asking ChatGPT a simple search question compared to the experience of trying to train it to do something specific for you are quite different experiences. Even beyond that, there’s a tool called superwhisper that I like that you can take audio content and end up with transcripts, but you can give it prompts to change your transcripts as you’re going. So, you can record something, and it will give you a different output if you say you’re recording an email compared to [if] you’re recording a journal entry compared to [if] you’re recording the transcript for a podcast.”- Ellen Chisa (30:11)

Links Boldstart ventures: https://boldstart.vc/ LinkedIn: https://www.linkedin.com/in/ellenchisa/ Personal website: https://ellenchisa.com Email: [email protected] 

Today I’m joined by Nick Zervoudis, Data Product Manager at CKDelta. As we dive into his career and background, Nick shares insights into his approach when it comes to developing both internal and external data products. Nick explains why he feels that a software engineering approach is the best way to develop a product that could have multiple applications, as well as the unique way his team is structured to best handle the needs of both internal and external customers. He also talks about the UX design course he took, how that affected his data product work and research with users, and his thoughts on dashboard design. We discuss common themes he’s observed when data product teams get it wrong, and how he manages feelings of imposter syndrome in his career as a DPM. 

Highlights/ Skip to:

I introduce Nick, who is a Data Product Manager at CKDelta (00:35) Nick’s mindset around data products and how his early career in consulting shaped his approach (01:30) How Nick defines a data product and why he focuses more on the process rather than the end product (03:59) The types of data products that Nick has helped design and his work on both internal and external projects at CKDelta (07:57) The similarities and differences of working with internal versus external stakeholders (12:37) Nick dives into the details of the data products he has built and how they feed into complex use cases (14:21) The role that Nick plays in the Delta Power SaaS application and how the CKDelta team is structured around that product (17:14) Where Nick sees data products going wrong and how he’s found value in filling those gaps (23:30) Nick’s view on how a digital-first mindset affects the scalability of data products (26:15) Why Nick is often heavily involved in the design element of data product development and the course he took that helped shape his design work (28:55) The imposter syndrome that Nick has experienced when implementing this new strategy to data product design (36:51) Why Nick feels that figuring things out yourself is an inherent part of the DPM role (44:53) Nick shares the origins and information on the London Data Product Management meetup (46:08)

Quotes from Today’s Episode “What I’m always trying to do is see, how can we best balance the customer’s need to get exactly the data point or insight that they’re after to the business need. ... There’s that constant tug of war between customization and standardization that I have the joy of adjudicating. I think it’s quite fun.” — Nick Zervoudis (16:40)

“I’ve had times where I was hired, told, 'You’re going to be the product manager for this data product that we have,' as if it’s already, to some extent built and maybe the challenge is scaling it or bringing it to more customers or improving it, and then within a couple of weeks of starting to peek under the hood, realizing that this thing that is being branded a product is actually a bunch of projects hiding under a trench coat.” — Nick Zervoudis (24:04)

“If I just speak to five users because they’re the users, they’ll give me the insight I need. […] Even when you have a massive product with a huge user base, people face the same issues.” — Nick Zervoudis (33:49)

“For me, it’s more about making sure that you’re bringing that more software engineering way of building things, but also, before you do that, knowing that your users' needs are going to [be varied]. So, it’s a combination of both, are we building the right thing—in other words, a product that’s flexible enough to meet the different needs of different users—but also, are we building it in the right way?” – Nick Zervoudis (27:51)

“It’s not to say I’m the only person thinking about [UX design], but very often, I’m the one driving it.” – Nick Zervoudis (30:55)

“You’re never going to be as good at the thing your colleague does because their job almost certainly is to be a specialist: they’re an architect, they’re a designer, they’re a developer, they’re a salesperson, whereas your job [as a DPM] is to just understand it enough that you can then pass information across other people.” – Nick Zervoudis (41:12)

“Every time I feel like an imposter, good. I need to embrace that, because I need to be working with people that understand something better than me. If I’m not, then maybe something’s gone wrong there. That’s how I’ve actually embraced impostor syndrome.” – Nick Zervoudis (41:35)

Links CKDelta: https://www.ckdelta.ie LinkedIn: https://www.linkedin.com/in/nzervoudis/

Today I’m continuing my conversation with Nadiem von Heydebrand, CEO of Mindfuel. In the conclusion of this special 2-part episode, Nadiem and I discuss the role of a Data Product Manager in depth. Nadiem reveals which fields data product managers are currently coming from, and how a new data product manager with a non-technical background can set themselves up for success in this new role. He also walks through his portfolio approach to data product management, and how to prioritize use cases when taking on a data product management role. Toward the end, Nadiem also shares personal examples of how he’s employed these strategies, why he feels it’s so important for engineers to be able to see and understand the impact of their work, and best practices around developing a data product team. 

Highlights / Skip to:

Brian introduces Nadiem and gives context for why the conversation with Nadiem led to a two-part episode (00:35) Nadiem summarizes his thoughts on data product management and adds context on which fields he sees data product managers currently coming from (01:46) Nadiem’s take on whether job listings for data product manager roles still have too many technical requirements (04:27) Why some non-technical people fail when they transition to a data product manager role and the ways Nadiem feels they can bolster their chances of success (07:09) Brian and Nadiem talk about their views on functional data product team models and the process for developing a data product as a team (10:11) When Nadiem feels it makes sense to hire a data product manager and adopt a portfolio view of your data products (16:22) Nadiem’s view on how to prioritize projects as a new data product manager (19:48) Nadiem shares a story of when he took on an interim role as a head of data and how he employed the portfolio strategies he recommends (24:54) How Nadiem evaluates perceived usability of a data product when picking use cases (27:28) Nadiem explains why understanding go-to-market strategy is so critical as a data product manager (30:00) Brian and Nadiem discuss the importance of today’s engineering teams understanding the value and impact of their work (32:09) How Nadiem and his team came up with the idea to develop a SaaS product for data product managers (34:40)

Quotes from Today’s Episode “So, data product management [...] is a combination of different capabilities [...]  [including] product management, design, data science, and machine learning. We covered this in viability, desirability, feasibility, and datability. So, these are four dimensions [that] you combine [...] together to become a data product manager.” — Nadiem von Heydebrand (02:34)

“There is no education for data product management today, there’s no university degree. ... So, there’s nobody out there—from my perspective—who really has all the four dimensions from day one. It’s more like an evolution: you’re coming from one of the [parallel business] domains or from one of the [parallel business] fields and then you extend your skill set over time.” — Nadiem von Heydebrand (03:04)

“If a product manager has very good communication skills and is able to break down the needs in a proper way or in a good understandable way to its tech lead, or its engineering lead or data science lead, then I think it works out super well. If this bridge is missing, then it becomes a little bit tricky because then the distance between the product manager and the development team is too far.” – Nadiem von Heydebrand (09:10)

“I think every data leader out there has an Excel spreadsheet or a list of prioritized use cases or the most relevant use cases for the business strategy… You can think about this list as a portfolio. You know, some of these use cases are super valuable; some of these use cases maybe will not work out, and you have to identify those which are bringing real return on investment when you put effort in there.” – Nadiem von Heydebrand (19:01)

“I’m not a magician for data product management. I just focused on a very strategic view on my portfolio and tried to identify those cases and those data products where I can believe I can easily develop them, I have a high degree of adoption with my lines of business, and I can truly measure the added revenue and the impact.” – Nadiem von Heydebrand (26:31)

“As a true data product manager, from my point of view, you are someone who is empathetic for the lines of businesses, to understand what their underlying needs and what the problems are. At the same time, you are a business person. You try to optimize the portfolio for your own needs, because you have business goals coming from your leadership team, from your head of data, or even from the person above, the CTO, CIO, even CEO. So, you want to make sure that your value contribution is always transparent, and visible, measurable, tangible.” – Nadiem von Heydebrand (29:20)

“If we look into classical product management, I mean, the product manager has to understand how to market and how to go to the market. And it’s this exactly the same situation with data product managers within your organization. You are as successful as your product performs in the market. This is how you measure yourself as a data product manager. This is how you define success for yourself.” – Nadiem von Heydebrand (30:58)

Links Mindfuel: https://mindfuel.ai/ LinkedIn: https://www.linkedin.com/in/nadiemvh/ Delight Software - the SAAS tool for data product managers to manage their portfolio of data products: https://delight.mindfuel.ai

Today I’m chatting with Katy Pusch, Senior Director of Product and Integration for Cox2M. Katy describes the lessons she’s learned around making sure that the “juice is always worth the squeeze” for new users to adopt data solutions into their workflow. She also explains the methodologies she’d recommend to data & analytics professionals to ensure their IOT and data products are widely adopted. Listen in to find out why this former analyst turned data product leader feels it’s crucial to focus on more than just delivering data or AI solutions, and how spending more time upfront performing qualitative research on users can wind up being more efficient in the long run than jumping straight into development.

Highlights/ Skip to:

What Katy does at Cox2M, and why the data product manager role is so hard to define (01:07) Defining the value of the data in workflows and how that’s approached at Cox2M (03:13) Who buys from Cox2M and the customer problems that Katy’s product solves (05:57) How Katy approaches the zero-to-one process of taking IOT sensor data and turning it into a customer experience that provides a valuable solution (08:00) What Katy feels best motivates the adoption of a new solution for users (13:21) Katy describes how she spends more time upfront before development to ensure she’s solving the right problems for users (16:13) Katy’s views on the importance of data science & analytics pros being able to communicate in the language of their audience (20:47) The differences Katy sees between designing data products for sophisticated data users vs a broader audience (24:13) The methods Katy uses to effectively perform qualitative research and her triangulation method to surface the real needs of end users (27:29) Katy’s views on the most valuable skills for future data product managers (35:24)

Quotes from Today’s Episode “I’ve had the opportunity to get a little bit closer to our customers than I was in the beginning parts of my tenure here at Cox2M. And it’s just like a SaaS product in the sense that the quality of your data is still dependent on your customers’ workflows and their ability to engage in workflows that supply accurate data. And it’s been a little bit enlightening to realize that the same is true for IoT.” – Katy Pusch (02:11)

“Providing insights to executives that are [simply] interesting is not really very impactful. You want to provide things that are actionable and that drive the business forward.” – Katy Pusch (4:43)

“So, there’s one side of it, which is [the] happy path: figure out a way to embed your product in the customer’s existing workflow. That’s where the most success happens. But in the situation we find ourselves in right now with [this IoT solution], we do have to ask them to change their workflow.”-- Katy Pusch (12:46)

“And the way to communicate [the insight to other stakeholders] is not with being more precise with your numbers [or adding] statistics. It’s just to communicate the output of your analysis more clearly to the person who needs to be able to make a decision.” -- Katy Pusch (23:15)

“You have to define ‘What decision is my user making on a repeated basis that is worth building something that it does automatically?’ And so, you say, ‘What are the questions that my user needs answers to on a repeated basis?’ … At its essence, you’re answering three or four questions for that user [that] have to be the most important [...] questions for your user to add value. And that can be a difficult thing to derive with confidence.” – Katy Pusch (25:55)

“The piece of workflow [on the IOT side] that’s really impactful there is we’re asking for an even higher degree of change management in that case because we’re asking them to attach this device to their vehicle, and then detach it at a different point in time and there’s a procedure in the solution to allow for that, but someone at the dealership has to engage in that process. So, there’s a change management in the workflow that the juice has to be worth the squeeze to encourage a customer to embark in that journey with you.” – Katy Pusch (12:08)

“Finding people in your organization who have the appetite to be cross-functionally educated, particularly in a data arena, is very important [to] help close some of those communication gaps.” – Katy Pusch (37:03)

Building a SAAS business that focuses on building a research tool, more than building a data product, is how Jonathan Kay, CEO and Co-Founder of Apptopia frames his company’s work. Jonathan and I worked together when Apptopia pivoted from its prior business into a mobile intelligence platform for brands. Part of the reason I wanted to have Jonathan talk to you all is because I knew that he would strip away all the easy-to-see shine and varnish from their success and get really candid about what worked…and what hasn’t…during their journey to turn a data product into a successful SAAS business. So get ready: Jonathan is going to reveal the very curvy line that Apptopia has taken to get where they are today. 

In this episode, Jonathan also describes one of the core product design frameworks that Apptopia is currently using to help deliver actionable insights to their customers. For Jonathan, Apptopia’s research-centric approach changes the ways in which their customers can interact with data and is helping eliminate the lull between “the why” and “the actioning” with data.

Here are some of the key parts of  the interview:

An introduction to Apptopia and how they serve brands in the world of mobile app data (00:36) The current UX gaps that Apptopia is working to fill (03:32) How Apptopia balances flexibility with ease-of-use  (06:22) How Apptopia establishes the boundaries of its product when it’s just one part of a user’s overall workflow (10:06) The challenge of “low use, low trust” and getting “non-data” people to act (13:45) Developing strong conclusions and opinions and presenting them to customers (18:10) How Apptopia’s product design process has evolved when working with data, particularly at the UI level (21:30) The relationship between Apptopia’s buyer, versus the users of the product and how they balance the two (24:45) Jonathan’s advice for hiring good data product design and management staff (29:45) How data fits into Jonathan’s own decision making as CEO of Apptopia (33:21) Jonathan’s advice for emerging data product leaders (36:30)

Quotes from Today’s Episode  

“I want to just give you some props on the work that you guys have done and seeing where it's gone from when we worked together. The word grit, I think, is the word that I most associate with you and Eli [former CEO, co-founder] from those times. It felt very genuine that you believed in your mission and you had a long-term vision for it.” - Brian T. O’Neill (@rhythmspice) (02:08)

“A research tool gives you the ability to create an input, which might be, ‘I want to see how Netflix is performing.’ And then it gives you a bunch of data. And it gives you good user experience that allows you to look for the answer to the question that’s in your head, but you need to start with a question. You need to know how to manipulate the tool. It requires a huge amount of experience and understanding of the data consumer in order to actually get the answer to the question. For me, that feels like a miss because I think the amount of people who need and can benefit from data, and the amount of people who know how to instrument the tools to get the answers from the data—well, I think there’s a huge disconnect in those numbers. And just like when I take my car to get service, I expected the car mechanic knows exactly what the hell is going on in there, right? Like, our obligation as a data provider should be to help people get closer to the answer. And I think we still have some room to go in order to get there.” - Jonathan Kay (@JonathanCKay) (04:54)

“You need to present someone the what, the why, etc.—then the research component [of your data product] is valuable. And so it’s not that having a research tool isn’t valuable. It’s just, you can’t have the whole thing be that. You need to give them the what and the why first.” - Jonathan Kay (@JonathanCKay) (08:45) “You can't put equal resources into everything. Knowing the boundaries of your data product is important, but it's a hard thing to know sometimes where to draw those. A leader has to ask, ‘am I getting outside of my sweet spot? Is this outside of the mission?’ Figuring the right boundaries goes back to customer research.” - Brian T. O’Neill (@rhythmspice) (12:54)

“What would I have done differently if I was starting Apptopia today? I would have invested into the quality of the data earlier. I let the product design move me into the clouds a little bit, because sometimes you're designing a product and you're designing visuals, but we were doing it without real data. One of the biggest things that I've learned over a lot of mistakes over a long period of time, is that we've got to incorporate real data in the design process.” - Jonathan Kay (@JonathanCKay) (20:09)

“We work with one of the biggest food manufacturer distributors in the world, and they were choosing between us and our biggest competitor, and what they essentially did was [say] “I need to put this report together every two weeks. I used your competitor’s platform during a trial and your platform during the trial, and I was able to do it two hours faster in your platform, so I chose you—because all the other checkboxes were equal. However, at the end of the day, if we could get two hours a week back by using your tool, saving time and saving money and making better decisions, they’re all equal ROI contributors.” - Jonathan Kay on UX (@JonathanCKay) (27:23)

“In terms of our product design and management hires, we're typically looking for people who have not worked at one company for 10 years. We've actually found a couple phenomenal designers that went from running their own consulting company to wanting to join full time. That was kind of a big win because one of them had a huge breadth of experience working with a bunch of different products in a bunch of different spaces.”- Jonathan Kay (@JonathanCKay) (30:34)

“In terms of how I use data when making decisions for Apptopia, here’s an example. If you break our business down into different personas, my understanding one time was that one of our personas was more stagnant. The data however, did not support that. And so we're having a resource planning meeting, and I'm saying, ‘let's pull back resources a little bit,’ but [my team is] showing me data that says my assumption on that customer segment is actually incorrect. I think entrepreneurs and passionate people need data more because we have so much conviction in our decisions—and because of that,I'm more likely to make bad decisions. Theoretically good entrepreneurs should have good instincts, and you need to trust those, but what I’m saying is, you also need to check those. It's okay to make sure that your instinct is correct, right? And one of the ways that I’ve gotten more mature is by forcing people to show me data to either back up my decision in either direction and being comfortable being wrong. And I am wrong at least half of the time with those things!” - Jonathan Kay (@JonathanCKay) (34:09)