Lynn Pausic: Hey, everybody my name is Lynn Posick, and I'm gonna be chatting today about ux design for data, intensive products and expert users and little bit more about me. So I'm 1 of the co-founders of a company called Expiro, and we do a lot of work with domain expert and users. I'm a huge enthusiast about understanding data and how we can use data to drive
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Lynn Pausic: these amazing experiences that we all build. And we're going to jump into more of that today.
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Lynn Pausic: So real quick. Who we are.
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Lynn Pausic: we are expiro. And a lot of what we do is design led design first, st solutions for companies. We build software solutions. We do product strategy. Ux design AI architecture development. And we do it across a variety of industries. The common thread across all the industries in which we work.
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Lynn Pausic: Is that we're typically dealing with some pretty thorny problems. So massive data sets expert users. A lot of enterprise and and b 2 b software.
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Lynn Pausic: Excuse me. And you know, over the years. We've developed different approaches. We've developed
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Lynn Pausic: some technology spotting those patterns over the years across industries that helps us accelerate and build the solutions that we build. And since we deal with a lot of domain experts and large data sets and complex domains. That's you know what we're gonna be talking about today. So just a wide sampling of to give you a flavor for some of the things that we build. And we're gonna touch on a few of them.
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Lynn Pausic: These examples here in the next few minutes. But suffice to say, lots of complex workflows, massive data sets data, visualization, and all of which this technology is meant to augment the instincts and the expertise of the domain experts on which we work.
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Lynn Pausic: So let's get to it a little bit more about why we do what we do and why a lot of folks kind of in this kind of space, particularly around Ux
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Lynn Pausic: do what they do. So you know, we've we believe for a long time. That enterprise software that's used in the workplace. Typically in A, b 2 b kind of audience segment. It should be every bit as delightful. And provide the value and be easy to use just the same as your favorite app or website, or something that might be simpler, that maybe you use as a consumer.
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Lynn Pausic: or maybe other b 2 b products. There's, you know, historically, been a lot of technology in the workplace that can be very difficult to use. Lots of complexity to it, and quite frankly, a lot of valuable time and investment is wasted on branding a lot just to use the software. And then you compound that
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Lynn Pausic: with. If you're talking about domain experts, they might be using the software to make critical decisions. And
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Lynn Pausic: they have to know that the software is reliable and trustworthy. You put all that together. And it could. It could be a real challenge. And so that's that's the challenge we take on every day at expiro and know a lot of the ux folks. That I personally, you know, work with and and touch base with our clients, and over the years. You know, they're faced with a lot of these challenges as well, real quick.
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Lynn Pausic: just to provide some additional context. When I said, we focus on domain expert users. This is kind of what we're talking about.
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Lynn Pausic: We're not gonna talk about all this in great detail today. But you know fundamentally domain. Expert users come with a wealth of knowledge that they bring to bear on the technology and and that they they want to help augment their knowledge and
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Lynn Pausic: have the technology work harder for them so that it's providing better criteria for making decisions and things. So domain experts, you know, they're gonna approach things differently. They might have an end goal in mind. But the path they're taking the software is very exploratory. And if you juxtapose that with, let's say, you know, a piece of consumer software, or even B Twob, software that maybe isn't as complex. You know, you might be able to
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Lynn Pausic: always have a guided workflow experience right like with a wizard or something.
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Lynn Pausic: domain. Experts also will question the integrity of an outcome. So whether it's an AI driven outcome or a calculation. Consumers, you know, right? We unwittingly and unknowingly just kind of accept what we say. Amazon makes a recommendation for us. We say great! I didn't know I needed that spider-man lunchbox. But, man Amazon, you know me, you know. I like spider-man. That's not the case. Whenever you're dealing with critical
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Lynn Pausic: tasks and information. And these experts are running on their instincts. Because they've been doing this a long time, and they said, sort of have a gut and visceral reaction to what they think the answer could be. So if you have something come in on the left field, you better be able to explain it.
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Lynn Pausic: to the user or they're gonna you know, lose faith in in the software that you're building, anyway. So that that's some examples of kind of the difference in the audience that we're talking about here. And so I'm gonna dive in now and and kind of connect the dots a bit more between the domain expert audience segment, and then talk about data and how data can drive ux. And so at the heart of it.
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Lynn Pausic: You know, one of the things that our approach is if you wanna solve a business problem. You know, you don't solve a business problem by pushing pixels. It's wonderful to have beautiful looking experiences. User experiences that embody a brand and all that stuff can be important. But the end of the day. Our users need the right data at the right place.
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Lynn Pausic: And it has to. You know, many of these user experiences are are data driven right? So it has to work well together. And so what we're doing as designers these days right is, we're S really solving business problems at the intersection of data and design. So we have to understand the problems that users are facing and then understand the data and the technology that's gonna support.
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Lynn Pausic: The ux design that helps the user get to their goal and solve that problem right? Gone are the days when we make something look beautiful. Without
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Lynn Pausic: understanding the feasibility of the data or the tech stack underneath. Just one way we think about it
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Lynn Pausic: kind of a more holistic view. So what I was talking about there a couple of minutes ago. If you look at the bottom layer here in this expanded user experience if you will, the key things are your user audience. So if they're domain experts or otherwise understanding them and their needs and really zeroing in on the problems you have to solve.
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Lynn Pausic: And then the functionality that can address those problems. And then do we have the data, and then the system architecture, and so on and so forth, to support it. Right? That bottom layer, that foundational layer is super important. That's where the business problems are solved right as you go further up the stack. It's really important to have good design. It's really important to have it look great as well, but
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Lynn Pausic: you start to diminish the value the further up that you go if you don't have that bottom
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Lynn Pausic: foundation. Correct. So what we like to say is, you know, what you want to do is go for a holistic. You want everything working together. So your strategy, your design, your technology, and your data. So once you kind of start to be able to string those things together. What's really cool is you could tell
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Lynn Pausic: stories with the data, right? So even if you have a massive data set. Once you start to understand
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Lynn Pausic: clearly identify the problems that the users are trying to solve. And you can juxtapose that with the data that's available. Now, you could start to do really cool things like over here on the right.
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Lynn Pausic: we've got on the top. There is some time series data. So you know, a common problem with time series data. So imagine to communicate what that is a little bit more. Imagine that you have a sensor that's reporting in data every
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Lynn Pausic: every second, or every 15 seconds, or something. About the maybe the the the temperature or the wind, or or whatever it might be right, or the location of a tractor tiller.
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Lynn Pausic: that's a lot of information coming in right. And unfortunately, a lot of that stuff still ends up in a format and in a way that it's really difficult for users to go through right? So being able to think about the problem you're trying to solve. And then how to convey how to use the data, to tell a story to the user, what they want to see in this case and that top screen there on the right. What we're able to tell the user there
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Lynn Pausic: is that there are certain events happening at certain times. And so, rather than having to go through piles of tabular information, we can tell a great story at a glance, for the user of what different events are happening in this case, looking at, maybe when someone logged in and visited something when certain events happened across an account, say in a bank
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Lynn Pausic: context and the same thing with the data in the bottom. Right there, right? The story we can tell there with that type of visualization. And the experience is, we're telling the user how things are connected. And so at a glance they can understand that story. Oh, I'm looking at a fraud ring. Let's say, if I'm a fraud investigator and I could quickly see the story of
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Lynn Pausic: the different
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Lynn Pausic: groups, the different accounts. The different things that maybe I should be looking at right very hard to see. If you don't have a good grasp, you know of of the data and be able to marry that with the right user experience. So
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Lynn Pausic: the better you get at doing this and marrying the 2, you know, the more value that you're gonna provide to user audience, and so now you start to think about layering on. We've got, we know we have some workflows. We have navigation. We have data visualization. So we're starting to frame this user experience. And we understand the data and the underlying technology that's gonna power that then we could start to kind of
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Lynn Pausic: get in there and really start to to
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Lynn Pausic: look at. Well, what is the best way to represent this information? Right? Once we understand the shape of the data we can and how what the user is trying to do. We can start to look at different ways to present that and data visualizations can be super powerful, right? At a glance, providing information to the user.
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Lynn Pausic: And so we have some several different examples here and there are lots of different examples because same data set. But the user is trying to learn different things right and represent in different ways. That can be one of the trickiest bits about when you're trying to take data and tell a good story with it that is valuable to a user and represent it in a certain way
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Lynn Pausic: we need to think about how we're going to marry the shape of the data that we have
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Lynn Pausic: with the right construct. So 1st trying to understand what the user is trying to do right? So the user might have a very specific goal in mind.
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Lynn Pausic: But what is it they're really trying to do? Are they trying to discover something they don't know? Are they trying to prove something? Do they just wanna see things in a fixed way? Every time they come to a dashboard or something. And there are lots of different ways of thinking about how we're gonna represent that. And here we have a couple of examples more declarative data, driven exploratory, conceptual
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Lynn Pausic: and depending on the quadrant that's most pertinent to the problem that your user is interested in solving. That's going to lead you to selecting a different type of data visualization. Right? So what we're looking for here is the intersection of the data that we have and some different data. Visualizations are available. Now, this is just kind of a simple
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Lynn Pausic: visualization of visualizations. To to try to inspire you to think about how you're gonna match your user goal and what they wanna see and their ability to see it, you know, just because they're domain experts, or any user. For that matter, some people are more proficient than others and being able to understand data visualization. So it's always very important to understand who user audiences.
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Lynn Pausic: But this can be a great way to be able to think about and start to put down different data visualizations, to get in front of your users. Once you know the kind of data that you have and the kind of data that you could actually light up. So not all data is gonna be the same, right? The data that might be available to light up
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Lynn Pausic: a let's say a pie chart might be very different than the data that is needed, let's say, for aggregating, you know, across a distribution or something in a scatter plot.
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Lynn Pausic: very different. And so you have to understand what the user wants to see in the data.
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Lynn Pausic: taking kind of all those layers and putting them together. Now, things that we could start to do is do exciting things like data driven prototypes. We could do things that are just vision mockups. But the better we understand the data, we could actually get into validating things with users and
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Lynn Pausic: derisking a lot of development time further down the road by doing data-driven prototypes.
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Lynn Pausic: And we're going to look at a couple of examples here.
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Lynn Pausic: Ab.
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Lynn Pausic: also
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Lynn Pausic: thinking about. Now, if we again continue the layering.
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Lynn Pausic: we have to think about AI, we're in a a new world, as of probably the last couple of years ago. AI, it all forms of it, you know, machine learning, deep learning. And now generative. AI nlp, some of these things have been around longer than others. Some been around a long time, and now they're just hitting critical mass. But it is going to impact
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Lynn Pausic: the way that users want to work productivity, and how we as product people and ux people think about
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Lynn Pausic: incorporating and supporting users. When we have an AI, a more AI driven experience, whether that's driving analytics or driving a generative experience? Altogether. And in my experience, some of what we're seeing out there is. It seems like people think there's a lot of daylight between data science work
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Lynn Pausic: and engineering work being done, operations Ops being done to be able to drive these AI driven experiences. So the daylight between Ux and the experience and data science and engineering isn't as big as people think. And in fact, we need each other a whole lot. So to really be able to utilize all these wonderful models.
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Lynn Pausic: and different types of AI. We have to work really closely together.
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Lynn Pausic: And the the screen. Here, for example. You could see a a few things. This is a good example of a generative. Ai where the users, interacting in a more conversational format. Right? But a domain expert is expecting to probably get responses back that are within a certain range right of outcomes. And if we start to get too far off the reservation
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Lynn Pausic: with generative AI, you're gonna lose the user right? If they don't trust it, they're not gonna use it, whether it's generative AI or other types of AI. And so we have some subtleties here, and that that smaller screenshot on the bottom. So saying, well, show me in this different context this out, Cap, explain why and how we got to this outcome. And yet you can get into
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Lynn Pausic: we're not gonna dive deep today, but all the fancyness of Rag and Craig architectures and so forth, to be able to reveal more source. Detailed information to help ensure trustworthiness to the user but for today, let's just talk about the experience a little bit more.
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Lynn Pausic: and what we have here to be able to surface cues to the user to guide them of what they can. In this case, maybe ask an AI agent or an Nlp agent, or what we can
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Lynn Pausic: provide in the experience to explain why this was the answer, and the root cause. Those are handshakes between
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Lynn Pausic: user experience and data science and making sure that not only is the data science accurate, but the models are also designed in a way that we can now get that outcome and have certain representation in the user experience to help guide the user more. So it's a very tight coupling, a very tight handshake. To be able to
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Lynn Pausic: have AI driven user experiences. So the takeaway here for any Ux people or product managers. Listening is if you're not already snuggled up to your data science team and your engineering team, you need to get in there and do that because it's
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Lynn Pausic: it really enriches the experience, and you'll be more aligned to be able to deliver the type of trustworthy and valuable experience. What, in short, what we're saying here is not just AI driven experiences. But you know, all user experience, particularly whenever you're dealing with complex domains, domain expert users and massive data sets in many cases and
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Lynn Pausic: models. Now, it takes a village. It's always taking a village, and more now than ever it takes a village. So you have to have the product focus and understanding the strategy and the goal posts and the market. You have to have the technology and the data so scalable technology, data that can be utilized whether it's for AI
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Lynn Pausic: or deterministic experiences. And then the user experience. And they all have to be working in sync together and helping each other right? There's a lot of overlap in what we're doing to be able to bring these
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Lynn Pausic: data driven solutions to market. The other thing that's imperative.
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Lynn Pausic: And for is empathy. And for those design thinking folks out there, you're well familiar with empathy for those of you that aren't. Think about it like this. If you have a mindset other than your user, if you are not the user, you're not the Sme, and that really understands the users. Often we as engineers, Ux people, product managers, we have to shift our mindset right to really put ourselves in the shoes of the user and understand how they think right.
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Lynn Pausic: most engineers, even ux folks.
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Lynn Pausic: product managers, we don't automatically think like our end users. So we have to do some work to be able to put ourselves in their shoes right? And we'll be better off for it right? The more empathy that we can have, the more we understand their vantage point. Whether you're new X design or your developer and architect, a data scientist. We can all contribute to the user experience and how we think about it.
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Lynn Pausic: And another way of thinking about, you know, a design led or pro sorry product picking the way we think about it is design led product development. So it's not something that happens upfront. It's not something that happens that empathy doesn't just happen when you're in the room with a customer, a user, it permeates all of the work that you're doing to
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Lynn Pausic: conceive of a product strategize about a product, design, a product, implement it and commercialize it and get it into the market right.
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Lynn Pausic: All all through. We have to be thinking like our users. We have to understand the problems that they're interested in solving the problems that their business and your business are interested in solving right and 0 in on that.
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Lynn Pausic: And this is our flavor of design-led product development probably looks fairly familiar. There are a lot of things here that are also synonymous with general product thinking. But the cool thing about how we approach is often with our customers. We are coming in at any point of this life cycle. So whether it's a new opportunity, or it's something that's been in the market for a while and maybe user adoption, let's say, isn't what
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Lynn Pausic: was hoped for with a particular product. You can get in here, and you can start at any point and try to understand how to go forward right? So that might be taking a new opportunity, or an existing product that needs to be modernized and reimagining it right and getting it in front of your market and getting that feedback and turning the crank together internally with that village with Smes
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Lynn Pausic: right and doing that rapid prototyping. Maybe that vision prototyping, maybe even derisking a little bit, doing some data driven prototyping to make sure that the experience that you're conceiving of can actually be supported by the data that is available or when it will become available doing your market validation all the way through, then executing and implementing and getting it into the market.
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Lynn Pausic: And then we're getting that market feedback. And we're analyzing that feedback. And we're adapting right spotting new opportunities that are around. We go.
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Lynn Pausic: So here's a real concrete example. And as you listen to this, some of you may be chuckling on what product thinking can do and design led thinking. And we have a little saying at Expero called, Don't build the beautiful wrong thing. AKA,
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Lynn Pausic: understand the actual problem that really needs to be solved. So imagine that you have a senior portfolio manager and a capital markets managing a deep portfolio of different financial instruments and products. And that customer comes and says, Hey.
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Lynn Pausic: I need a faster grid.
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Lynn Pausic: Okay? Well, it'd be very easy for developer to go and look at the potential performance issues of improving that particular grid. Right? But if we're putting our design thinking hats on right? We want to put ourselves in the user's shoes, like, Okay, well, why do you think you need a faster grid?
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Lynn Pausic: Well, it's too slow, and it takes too many clicks.
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Lynn Pausic: Okay, well, with all of those clicks. What are you trying to accomplish? Oh, well, I want to be able to pivot and sort and filter and do all kinds of things. Oh, okay, okay. Got you. So I could see why you might have a performance issue. Then, if you want to do all those kinds of things. So then tell us, what's your goal
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Lynn Pausic: in pivoting and sorting, etc? Oh, well, what I really wanna understand is across this deep portfolio, where the changes are, where the changes happening, let's say, in rating, for example,
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Lynn Pausic: or value. And I want to understand that so I can rebalance my portfolio.
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Lynn Pausic: Okay? So now, by just going down the simple set of in this case, 3 wise instead of 5 wise, maybe. We've gotten to the root cause of the problem. And the more complex things are right. And dealing with these domain experts who right away, wanna kind of jump to a solution. You have to be able to dig in. And when we're putting our
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Lynn Pausic: design lead hat on and really thinking about the user. And well, why are you doing this? Well, then, why are you doing that? That helps us reveal what the real problem is, and the great news about that is now we can deliver a much higher value solution. We could have gone in and just given them a faster way to pivot. But a better solution is to say, Hey.
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Lynn Pausic: we could give you a faster pivot. But also.
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Lynn Pausic: if you really just want to understand where you need, where there is a ratings change in this really deep portfolio.
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Lynn Pausic: We could highlight that for you. Oh, okay, well, now that solves 2 problems, you solve my performance problems. And now, at a glance. I can understand where there might be a change in this case and a rating across a very deep portfolio, and maybe I want to be able to do that on a mobile. Maybe I want to see it as a data visualization. Maybe I just have a little visual indicator for whatever I'm in my tabular view of my portfolio. So now we've provided a lot of value
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Lynn Pausic: across something that has a massive amount of data for domain expert end user just by getting in there and and putting our design led hat on for us. This is kind of our high level process, and we go through. I'm sure there's probably nothing. There's nothing rocket science about it. But I I wanted to use it as a context.
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Lynn Pausic: to say like, let's take a little peek inside of what happens during kind of a design sprint, particularly whenever we're thinking about. Well, how do we get feedback? And how do we validate things. So whenever you're dealing with large amounts of data and complex data and these data intensive products, and particularly with expert users, sometimes you need to leverage either
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Lynn Pausic: high fidelity, mock-ups of your experience or data driven prototypes were possible which we touched on a little bit ago.
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Lynn Pausic: Whatever you're going to choose as your your weapon in the arsenal for expert users. With these these very data, rich
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Lynn Pausic: products. It has to be realistic. Otherwise, you stand the chance of a user. Assuming that a certain calculation or analytic or AI driven item is, gonna be there and it's not so you gotta get pretty detailed. The other thing is
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Lynn Pausic: it's really important to quantify your user experience. So sometimes user experience can feel a little subjective right? Particularly if you're the pushing pixel stage. And you're driving more from the look and feel down as opposed from the data up and and intersecting that with the problems you're trying to solve.
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Lynn Pausic: So what we want to do here is quantify that. And there are lots of ways to do that, and it doesn't need to involve a large number of customers or end users. Right? We're not looking for statistically significant data, right? This has been around a long time.
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Lynn Pausic: It's important to be able to quantify, and over time doing simple things like having a rating scale whether you're doing a you know, a a moderated session, a crowd source a
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Lynn Pausic: you know, it could be a a user driven, heuristic evaluation, whatever it is, being able to quantify. That is super important, because now, over time, we start to have actual data that we can look at to say, Oh, you know what this feature, when features are rated
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Lynn Pausic: 4 or better in terms of their value. When we're testing them.
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Lynn Pausic: they tend to do well in the market, but when they're they're 3 or less, they don't tend to do well in the market. Okay? Fantastic. Well, now, we have quantified something about the user experience rather than the just. You know, what does it look like.
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Lynn Pausic: And so if you think about where your product is and its maturity level, these are some different things across the continuum here of product maturity early on, you're probably going to do more market sensing because you're looking for big pictures and maybe trying to identify at the very top of our design-led thinking
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Lynn Pausic: where there are opportunities in the market, and how to really take advantage of those opportunities and what those problems are, and how you're gonna resolve them. With with the experience.
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Lynn Pausic: All the way through, you know, starting to early on, get into that more moderated feedback which can be super important. Before you start doing automated. You know, you're talking about a website or something. We could serve up version A and version B, easily fine. You might jump to something that's a little more automated. When you're dealing with
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Lynn Pausic: complex products that have large data sets and expert users, you're often going to want to spend more. If it's a new product, a modernized product, you're a startup. You're really trying to understand that audience. And what's valuable to them, and how you're going to prioritize. Let's say you know your roadmap and what you're going to put in the market. And when so getting in there and actually still having real live people do that work in a moderated way can be really valuable
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Lynn Pausic: cause. You can not only understand whether they completed a task or didn't, or found a piece of functionality to be valuable or not valuable. You can get in there, and you can ask the why, and you can dig in just like we talked about a couple of minutes ago with the 5 wise. Well, why? Why? Why? To get to the real problem, and how to improve your product. So moderated all the way through all the things the great things that we can do now in an automated fashion.
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Lynn Pausic: And all the analytics that we can
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Lynn Pausic: put into products and analyze. And in fact, there's
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Lynn Pausic: a litany of things that you could do across the product life cycle. So those either been doing this a long time? I'm sure you're familiar with most of these. If you haven't, there's a little map for you of across the top there is a product, life cycle, and down the left side are all of the many different tactics that you can take to at any point in the product lifecycle. Start to improve your usability, your trust.
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Lynn Pausic: your adoption in the market. There's there's a different weapon in the arsenal for all of that.
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Lynn Pausic: And then finally to wrap up. Today, I'm just gonna kind of walk through here. Some of the different things. That we talked about? So some of the guiding principles when we're talking about these data, intensive experiences and domain expert users, understanding the business problem. And where in your product, you're gonna impact
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Lynn Pausic: solving those business problems and user goals, the most. And we talked about that foundational element, right? So really understanding the audience and the problems
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Lynn Pausic: and the data right? And the tech stack that you have available and how that's going to work for you.
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Lynn Pausic: Course, understanding your audience having that design led thinking hat on and empathy and putting yourself in the user shoes, identifying and defining the actual problems. Super important. Ask our 5 wise and be curious. It's not just about the why, but also ask questions. Right? So you know, what do you need to see as a user to be able to
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Lynn Pausic: leverage an analytic and make a decision. What would convey trust? If you're surfacing an AI outcome, you know, ask questions. And our our goal is to always measure and create something that's valuable. To the users. Think of things holistically. Make friends with your data, scientists and your engineers. We should be working together hand in glove, and of course develop a strategy.
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Lynn Pausic: On how you're gonna approach. You know your user experience. And you put all these together, and you have a whole lot of great
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Lynn Pausic: different principles and weapons in the arsenal that you can yield as you go out into the market with your data-driven and data, intensive products and expert users. Thank you very much. It was great chatting and love to hear from you. If you have any other questions, that's how you can reach us. Thank you.
