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Noah IliinskyWhen you’re drowning in data, it’s good to have an information designer like Noah Iliinsky looking out for you.
Noah takes a human-centered approach to a data visualization, connecting people with information tucked away in massive datasets.
His purpose-driven process is designed to help users quickly extract the insights they need from a flood of information.
We talked about:
- his new role as Principal UX Architect at Collibra, a data-governance company
- how he accidentally wrote a masters thesis about bringing user centered design perspective to diagram design
- his work with content professionals over the years: mostly technical writers and UX writers
- his four-pillars process for teaching data visualization (and other design concepts):
- Purpose (the why)
- Content (the what)
- Structure (the how)
- Formatting (everything else)
- how he helps users connect to technology with his interface work
- his goal to help users stop using the tools and artifacts he creates as quickly as possible, so that that they can get on with their lives
- how “shallow” recommendations can be superior to full-blown guided experiences
- how he uses UX research in his work, whether he’s doing it himself on small projects or working with a team
- how starting with purpose keeps his information design projects on track
- the importance of providing context in data visualizations
- his assertion that in UX design, “If you can’t draw the map, you don’t understand it well enough to really build a good solution.”
Noah’s bio
Noah Iliinsky has spent more than a decade researching, writing, and speaking about effective approaches and best practices for designing data visualizations. He is the co-author of Designing Data Visualizations, and the technical editor of, and a contributor to, Beautiful Visualization, both published by O’Reilly Media. He has a master’s in Technical Communication from the University of Washington, and a bachelor’s in Physics from Reed College. He loves cats, bicycles, and baking.
Connect with Noah online
- ComplexDiagrams.com website
Resources mentioned in this podcast
Video
Here’s the video version of our conversation:
Podcast intro transcript
We live in a world that is inundated with data. Human-centered designers like Noah Iliinsky turn that sea of data into usable information, helping us discover useful knowledge hiding in those huge datasets. Noah takes a purpose-driven and content-focused approach to guide his process of discovering and revealing the insights in our data. One of the most powerful techniques in his toolkit is data visualization. These visualizations can highlight important information and show connections that other displays might miss.
Interview transcript
Larry:
Hi everyone, welcome to episode number 89 of the Content Strategy Insights podcast. I’m really happy today to have with me my friend, Noah Iliinsky. I know him best as a data visualization guru, but it turns out that he’s much more than that. Right now, he currently holds a position of Principal UX Architect at an outfit called Collibra. So welcome Noah, tell the folks a little bit more about yourself and your work at Collibra.
Noah:
Hi, Larry. Thanks for having me. I’ve only just started at Collibra, I’m pretty excited to be there. They are a company that does what they call data governance or data management. So you might be a large institution, a bank, or a university or some other large company that’s got lots and lots of data from lots and lots of sources. It might be customer transactions, it might be financial records, any number of these other multitude of data sources. And what Collibra does is, it is a catalog of all your data sources that allow someone to come and say, “I need requests on all the transactions we had of October this year from all of our different systems.” The other thing that Collibra does fundamentally is, it tracks things like privacy and permission and whatnot, so that’s built into the system.
Noah:
So I am, like I said, brand new there, but I will be working on UX for a number of their systems. And in terms of about me, let’s see, short version is I got a physics degree as an undergraduate. I wrote code for the first dot-com bubble. I eventually went to grad school at the University of Washington here in Seattle where I live, in the program that is now human centered design engineering. It was still on its way to becoming a design program at the time back when it was called technical communication. And so I had to some degree make up my own master’s degree on the way there was some, but not as much as I was looking for there.
Noah:
And I accidentally wrote a master’s thesis about an 80 page master’s thesis about bringing a user centered design perspective to diagram design. And of course that approach is applicable also to data visualizations and applicable to a lot of design contexts, including all different types of communication design. And so I call myself an information designer in that, this comprises data viz, the visualization being one flavor of artifact or output. But similarly, the work I do also involves things that we think of as the traditional information architecture skillset where it’s, how do we navigate people around a product or a website? How do we group feature this together so people can find them. That’s the navigation version of information architect. I’m not the library science build your ontology version of information architect, that’s its own subspecialty that I am. I am not an expert at so…
Larry:
No, I think often of that Jesse James Garret’s layers of UX design and how different there’s little looping connections between the layers and different labels for the activities that people do in each layer. And you exemplify that with everything you just said. Oh yeah, I want to talk… Most people in my world, I don’t know if it’s most but many people in the content strategy world, they come from a word background, we’re all writers and editors and those kinds of folks. And we’ve worked a lot with folks like you over the years, but I think it seems like maybe it’s… Do you see more of that? I guess do you work, tell me about your relationship with word people and-
Noah:
Yeah. So the two main job titles that I have worked with historically are people who are technical writers, who are very specifically writing how to documentation for complex online applications that I’m working on. So I spent 4 years at Amazon Web Services and worked on some very complex things there. And so we would work pretty closely with the technical writers in terms of making sure the right stuff was documented helping them figure that out. Although generally the technical writers I’ve worked with have been incredibly sharp and fairly independent and pretty able to figure stuff out on their own.
Noah:
And then the other job title, which I’ve worked with a little bit less, but there’s definitely people who are specifically work as UX writers, or UX content writers, where there is a standard voice, a standard tone, maybe specifics of vocabulary that we use for technical terms, and these UX writers are responsible for making sure that all of the copy on the page that anybody sees in terms of the controls, and the labels and whatever are both consistent, and compliant with the definitions that we use and consistent in terms of the voice, and the tense, and the mood and all these things. So those are the word people I usually end up working with.
Larry:
You’ve done a lot in our world, I didn’t… I’m learning something new, and something you said earlier, especially when you’re talking about working with tech writers, one of the things you’ve talked about, I’ve seen you to give a couple of Ignite Talks on this subject, and I can’t remember which one it was, but you talked about this notion of design the thing right, but make sure you’re designing the right thing. And you’ve alluded to some of that, was that some of your work with the technical writers you worked with?
Noah:
No, I mean, that was… Okay, here’s the background of that. I did not attend and did not see, but somehow listened to the keynote from South by Southwest, I think in 2006. And it was Dan Gilbert who wrote a lot about happiness, right? And I shamelessly stole that from him, he was talking about how to be happy guaranteed, all you have to do is two things. You have to know the likelihood that a thing will make you happy, and know the likelihood of that thing coming to pass. And if you know those two things you can guarantee you’d make the right choices and always be happy. So that really stuck with me. And I riffed on that, this is… You saw my guaranteed successful design talk and all you have to do to be guaranteed successful with design is, design the right thing and then design it right.
Noah:
So humans are bad at all of these things. We’re bad at knowing what’s going to make us happy. We’re terrible at judging the odds of how likely something is. Similarly, in the tech industry, we do a lot of building the wrong thing because we haven’t asked the question why enough times to understand what it is we’re building and who we’re building it for?
Larry:
Well, that’s another similarity you have with every content strategist I’ve met. You’re constantly hacking the human being and trying to figure out what do I need to do to get my point across or to address their need-
Noah:
That’s right.
Larry:
Yeah. And do you have, I don’t know, a master template or something for that in your work, like an approach I guess, to your work, because I know you’re… And I love that you’re very relentlessly user centered. That’s the foundation. Tell me how that unfolds.
Noah:
So the process that I came up with for teaching people how to be data viz, but actually works fine for other design and works great for communication, everything else. And Larry, we can post the link, this is my four pillars process. Step number one is always purpose. And the purpose is who is my customer? What problem are they trying to solve? How are they going to consume it, right? Is it mobile? Is on the desktop on big screen? Is this printed out on a piece of paper? Is it an interactive or is it a static PDF? Do I get colored? Do I get black and white? What are my accessibility concerns? What are the language concerns? What’s the vocabulary, every consideration, right? Who is this for? What problem are we solving for them? That’s always step one.
Noah:
And until you have that, you can’t really make any other decisions usefully. And for me, this is the answer to the paralysis that I get with a blank page, when someone says, “Oh, just design whatever.” It’s like well, okay, but who’s this for? And is it mobile? Is it desktop? Or is it printed on paper? And so you have to define this purpose first that encompasses all that. That’s all step one. That’s the hard part. The rest is easy and I’m not joking. I’m not saying that in a flippant way at all, that’s really the hard work, and that’s usually the piece that is most missing. And because it is so foundational, if you get that wrong or don’t do that at all, everything else downstream, it’s a classic garbage in garbage out situation, right?
Noah:
Once you’ve defined your purpose, step two is content. It’s literally of the entire universe of content that I have available to me that I might choose to represent to my user or my customer, because literally your user… It’s a customer of a knowledge product, right? I’m trying to give them a knowledge product so they’re satisfied. So I probably don’t want to give them everything. I probably just want to give them the parts that are relevant to them. I want to give them answers that they can actually use in their work. So how do I choose what content matters? Well, you have to go back to purpose. You have to know who they are and the problems that they’re solving and what are they going to do with the data you give them whatever.
Noah:
Step three is structure and in a data visualization context structure means what graph type do we use. And there are some tools and some approaches where someone goes and picks a really cool graph type, but they haven’t really considered the data. They haven’t considered the purpose, particularly for data visualization. There are very, very strong capabilities in the brain, and the eye, and the cognitive psychology of how we perceive placement in color and shape. And they have… Because of how these are perceived, they can very much support the communication that you’re intending and the data type that they’re meant to represent, or they can make it very, very challenging to understand the data that you’re trying to represent. And so that’s a low hanging fruit when it comes to data viz, but honestly, it’s true in any communication, whether we’re talking about textual writing that starts very broad and then gets more details as you go into it or starts with the answer and then gives the backstory behind it.
Noah:
The reason we write outlines, the reason we think about the flow of a piece of writing is we want our structure to be compelling, to the support of the story that we’re trying to tell to either give the bottom line first, so if people stop reading first that they get the takeaway regardless, or to build up the story, so by the time we get to the punchline, all the foundational work is there, and people understand it and find a compelling, there’s all different ways that we tell stories, but when done well, we have an intentionality behind it, and we care a lot about the structure.
Noah:
Step four is formatting. And again in data visualization, that’s typography, interactivity, grid lines, annotation, everything beyond the course, where does it belong on the page in other communication that also can of course be topography, it can be linguistic flourishes. It can be all the other things that make the content that I’m communicating, compelling, that make it interesting, that draw attention to the points that matter and lots of things. So that four points purpose first, then content, then structure, finally formatting is a pretty generally applicable approach to designing data viz, interfaces, written communication of all kinds. Yeah, that’s what I do.
Larry:
I’d say the way you just laid that I’m currently retooling from a content strategy generalist to a content modeling specialist, and I might just steal your approach. It’s actually very close to what I’ve been working out as I reflect and study, and anyhow-
Noah:
And I’ll just put one more thing in. I’m really good at reinventing wheels, and then years later finding out that I’ve intentionally or not borrowed from a lot of other places. So I’m obviously not the only one to say you need to understand your customer and your user before you do these other things. I came through this from a data viz perspective, but there’s a lot of design processes that are very similar in terms of the broad thinking, understanding the customer, figure out what’s relevant, structure your approach, and then implement it right. That’s not just me, I don’t want to take credit for the whole history of-
Larry:
Exactly, but it’s clear that you’ve applied it in your own… You’ve given your own flavor to it and your implementation of it, and appreciate that. And you know, this reminds me of another thing that you talked about in one of your talks and it’s… I’ve been currently geeking out on the… I’m studying knowledge graphs and graph representations and ontology and stuff like that. But that’s got me thinking going back to that, that old pyramid of data, information, knowledge, and wisdom. It’s like Maslow’s hierarchy of data to, I don’t know… but you did one of your other, again, I can’t remember which Ignite Talk it was because… But you did a talk once where you talked about the working from… Instead of focusing on the raw data that you’re working with or just the basic information about it, get up to solution and action and that level, and you’re ascending. And I guess the similarity I see there’s this ascending up procedure. Tell me a little bit about that part of your approach that the sense-making I guess-
Noah:
Yeah. So I don’t know, probably 15 years ago, I think I got this from Jared Spool, but I’m not sure. I saw the notion of like you have your database or your technologies over here, and your customers over there, and there’s a gap in between them, right? And your interface is somewhere in between those two extremes. And you’re going to do your work to move your interface somewhere away from the technology towards the user. And then the user has to do the rest of it to get from wherever your interface is to what they need. And that’s hard work. That’s expensive work, whether I do it, or my customer does it, somebody has to do it. And so if I can invest to move closer to them, there’s less work that each of them has to do. And that scales every customer for the rest or forever gets the benefit of that work that I do.
Noah:
Similarly, there’s a lot of knowledge products that have a very… Is sort of a first order approach when they said you have all this data, we’re going to make it available to everybody, and then everybody can find whatever they want, which is a little bit like saying, “Welcome to the restaurant. We have all the ingredients, we have all the mixers and ovens and you can make yourself anything you want.” And that’s fine. And sometimes you want to cook, but sometimes you just want someone to give you a hot meal. Sometimes it’s somewhere in between, right? You want the cake mix with the mac and cheese in a box and it’s half done for you, you’ve got to do a few steps, but the upshot is that if we zoom out a little bit… When I’m creating a knowledge product, whether it’s an artifact or a tool, my ultimate goal is not… I don’t want my users to have a long time on task, I want them to complete their task and go onto the next thing in their life, right?
Noah:
My success is based on the user getting what they need from the tools that I built and getting on with their life. So the closer I can get them to their action, or the answer or whatever it is that they need to take the next step of their process, the more successful I become, because it means they’re spending less time on my tool and more time doing the next thing down the road.
Noah:
This leads to this hierarchy that you mentioned. And I’ve seen different versions. This is not mine, again this has been around forever. So you start with raw data, maybe put in a database or a spreadsheet, and you’ve got some information, you can go to get row totals maybe if you want, and now you can say, “We’ve got some answers there.” Some people talk about this being knowledge or wisdom. Wisdom might be the knowledge to take the right action that you need. This is all great. And if we can give people not all of the answers in the world, but just the answer they’re looking for today, that’s a win.
Noah:
They spend less effort, less time looking for the right answer, or they’ve got the one and they can just go do it. And at the top of that stack that you saw what we can link to the slide deck, this is from my again, from my guaranteed success-
Larry:
Yeah, I’ll put that in the show notes, yeah.
Noah:
Yeah. It’s in the slide deck, but if you can get them to a solution, then they don’t have to take any action at all, right? And so this is … subscription services. We know you shop for paper towels. We know that every six weeks you buy a four pack of paper towels. Here it is. It’s on the homepage. These are things I used to order before, click the subscribe button and you never have to shop for paper towels again, they just show up at your house, right? This of course applies in all sorts of knowledge contexts as well, right? We know that every month you come and you get the monthly totals for sales, what if we auto-generate that report for you? What if we highlight the rows for you that you always go search for any way of the retail outlet that had the most and the least sale?
Noah:
What if we highlight the ones that had the largest change positive, and the largest change negative, both in terms of dollars and percent, if we know what answers the people are looking for, the answers are easy. Computers are really good at doing the math. They just have to know which one you want, right? So again, this goes to make the user do less work, get them closer to the answers of the actions that they want.
Larry:
Yeah. You know one way that, that dynamic you just described is manifesting in the content strategy world these days is a lot of concern about personalization and recommendation engines, and building content systems that support that. Have you done any work in that… Like you just alluded to we know you order these paper towels every six weeks, have you done that work or?
Noah:
I have not. I’ve done… So the only recommendation engine that I worked on was actually when I was at Amazon Web Services, working on QuickSight, which is the data viz tool. And I was the first designer, and first product manager on that. And we built a graph recommendation feature called Autograph. It is a signature ha ha, that’s a pun, signature feature of my tool. But what it does is as you start to select the fields that you are interested in using, it recommends graph types that are compatible with the data types you’ve picked. So it’s a very shallow analysis. It’s only two or three clicks and only two or three data points, but the goal there is at least to get people headed down the right path, as opposed to for example, there’s plenty of data viz tools where you go browsing in this beautiful wonderland of graph library, and there’s hundreds of bizarre, esoteric, really interesting, cool data viz types that are completely useless.
Noah:
And you pick the… My favorite example is the spiky pyramids with the stripes on them. And then you try to shoe-horn your data into it and try to make it meaningful, and it can’t be, right? Because that’s not a valid really data type that’s ever going to be useful or a valid graph type. And it’s never going to be useful because it doesn’t work well cognitively for humans. Instead of this, we tried to set people up where they instead of purpose, they started to tell us just the tiniest bit about their purpose by literally touching fields and saying, “I’m going to graph this field and that field and that field.” And based on that, we could say here’s an appropriate graph type. It might not be the exact one perfect for you, but it’s appropriate. It’s not wrong at least.
Noah:
My joke since I designed that is, 5 or 10 years from now, it’s all going to be machine learning anyway, you’re not going to need a data viz expert to build this recommendation engine. And I think we’re pretty, pretty close to that being true at this point, that was in 2015, I designed that. So my guess is that, in the next year to 5 years, most of these data viz tools are going to have recommendation engines that are pretty shallow. They don’t need to know a lot about you as soon as they can see the flavors of data that you’re talking about. And I’m not talking about sales data or personal data, I’m saying, Is it a geography data type? Is it a date data type? Is it a category data type? Is it numeric, right? That’s enough to make a pretty good recommendation on a graph. So that’s the only recommendation work that I’ve done.
Larry:
Got you. Well, you reminded me of who said that? The designing for the scent of information, I can’t remember who talked about that-
Noah:
I know the term, I don’t know the author.
Larry:
Nielsen Norman group, maybe one of those guys, anyhow, but it’s that idea of like you just said, “Okay, you’re going to tell me a little bit, and I’m going to help you along the way.” That’s an emerging role. Hey, let me ask you another thing, you’ve mentioned in that specific example, and you mentioned earlier a couple of things about helping people get the tasks, accomplish the tasks they want to do, or just get the answers they need that implies some research. Do you do your own research, work with researchers? How does that usually unfold?
Noah:
It depends on the context. Some of the time I’ve been lucky enough on again, on some of these products, mostly when I was in Amazon Web Services having a research team and we’d get a researcher assigned and they may be part of this larger project, but certainly moved from all the freelancing I’ve done from the smaller projects, it’s literally doing it myself, talking to end users, trying to understand their needs, putting a paper prototype in front of them and just walking them through the process or a very simple, clickable wire frame, which you can do and you can do in keynote. Most of the drawing tools, now you can put it at hotspot, that’ll take you to the next page. So I’ve done a little bit of that, but I don’t call myself a researcher in that regard because all the professional researchers are much, much better at it than I am.
Larry:
Yeah, no, and it’s interesting. And I always… I haven’t talked a huge amount to folks at Amazon, but I always feel like, You’d think in a giant, massive enterprise like that, that you just have everything at your disposal, but maybe not so much.
Noah:
Yes and no. Yeah. It depends on the size of the project, right? It depends on whether somebody, I don’t know what level the director level wants to pay for a researcher, either from the central core or hire one full-time. But certainly there was plenty of stuff that the product managers usually are pretty smart and they know their customers and their use cases pretty well. The good ones do. And you start with a lot of hunches and then you go talk to people. Either I go talk to people or the product manager goes and talks to people so…
Larry:
Yep. No, I think that I’ve done enough… I had enough conversations around that, that it’s just completely bespoke everywhere. Everybody is in their own way. And with whatever resources they have and yeah. Wait, there was something else I was going to ask you about that just came up, give me a second, it’ll come back to me. Oh, I know. When you’re talking about that ascending that pyramid or whatever, up to the more valuable stuff, Are there… How do you know when you’ve got… You mentioned an example, if we’re going from data to information and how you… And then adding up a column, that gives you a little bit of maybe even close to knowledge or something like that. Are there, I don’t know benchmarks, or how do you know you’re on track, I guess is what I’m getting at.
Noah:
It goes back to purpose, right? You have to know your customer and what they care about. There was I don’t know, a report that went out every week maybe at Amazon Web Services, in the group that I was in. And it was, for every database product we have, how many active users and how much money did we make, and it would be up and down 2% maybe, right? Week over week, if we were lucky, right? Don’t quote me on that number, I’m just making it up. But low, low, single digit percentage plus a decimal changes.
Noah:
And if you’re a director or a VP let’s say, you know that 2.5% is a really good week, or month or whatever. And 1.8 it’s on the low side, but you just know that because you look at it every week because it’s your business because that’s what you care about. But if somebody is brand new and they say, “Okay, 2.2, 2.4%, is that good? Is that bad?” I don’t know, there’s no context provided there, but if you wanted to, you could take that another step and say, “Here’s how this deviation, this week over week delta in a percentage or a total number of dollars, here’s how it compares to the average or here’s how it compares to the change a year ago, so we’re going to give you some context, because I don’t really care if that number is 2% or 2.5%, I just care if that’s better or worse than an average week.” Right?
Noah:
And you just have to know the context, you just have to know the customer and what do they care about. Now, if I’m a sales person, it might be that every week that I don’t hit my average, I don’t get a bonus. And so that number, the specific number might be really important to me, right? Or it might be that if I’m in retail or some other transaction, I have a number in my mind, this means we’re profitable, or we’re not profitable. This means we’re making money or we’re going into savings, right. But all that’s contextual that you’ve got to get from the ultimate consumer of this.
Noah:
And the ultimate consumer in a lot of cases is someone who is going to make choices, is going to make decisions based on this, right? We’re going to close that 7-Eleven because they’ve been losing money for the last four years, and we just don’t think they’re ever going to get profitable again, or we’re going to shut down on our printer business because we can’t make any money on printers, or we’re going to invest in this neighborhood, or invest in this product because they’re doing really well, and we think with a little more investment, we can ramp up our results in some meaningful way, or here’s the cheapest flight, and yeah, it means I got a 4-hour layover in Chicago, but there’s a great restaurant in O’Hare that I’m going to go and get a great sandwich on the way I don’t mind having 4 hours in Chicago and it saves me $200 on my ticket, who knows?
Larry:
Got it, and I’m inferring from the way you just said that, and also the fact that you were at Amazon, that those metrics you were referring to have customer needs built in as much as your business objectives, is that true? Or how do you balance those I guess? Yeah.
Noah:
Sure. I mean, the ethos, certainly the Amazon Web Services was, more users is better, right? And we might spend years developing a product and sinking money into it before it becomes profitable. But ultimately if we can build the tool that our customers want to use, then they’ll use it and eventually we’ll make money on it. So the foundation there really was make it easy for people to use the tools, make tools that people want to use. I mean, that was web services, right? I was not in the retail side and that’s a whole different philosophy, a whole different business model, but yes, ultimately, apparently it very much was about solving problems for customers, whether it’s a new data viz type or whatever, that new data viz tool.
Larry:
Got it, all right cool. Wait, no, I can’t believe this. We’re already coming up on time. These things always go amazing. Like I can think of 20 more questions, but let’s start to wrap it up, but before we start to wrap up, is there anything last, anything that’s come up in the conversation or that’s just on your mind about UX design or the data visualization that you want to make sure you share before we…
Noah:
Yeah. I mean, I guess, all right. So here’s my strategy pitch to make you a better certainly UX designer. And this is also in my Guaranteed Successful Design talk. By the way, there’s a 5-minute version of that talk, and there’s a 45-minute version of that talk. So the same link has both of them, slide decks recordings everything. For UX design, you have to draw the map, the process flow, whether it’s a step-by-step that the user takes, or whether it’s a web navigation map, or an architecture map. If you can’t draw the map, you don’t understand it well enough to build a good interface. Anybody can build any interface online, but if you want a good interface. And the analogy that I use is, we’re going to, we’re going to build a kitchen for this house this year. And next year, we’re going to build a living room.
Noah:
And, the five-year plan is we’re going to get some bedrooms in here and a bathroom someday and… People have built houses that way. I grew up in a house that was a little bit like that started being built in the 30s and had significant work done on it, in the decades after that, but you don’t get a great tool that has been built organically without intention. And that big picture, even if you’re not going to build it all this year, that big picture of how do all the pieces fit together, how is it user navigate from here to there? When we add new features and new options, how is that going to integrate with what already exists? What’s the difference between a first time user and returning user in their experience. If you can’t draw the map, you don’t understand it well enough to really build a good solution.
Noah:
I’m sure there’s people out there taking offense at this right now, but I’m going to stand by it that probably there’s inefficiencies, sticking points, the things that might make sense to the experts, but newbies are going to get confused by because they weren’t there when it was built and they don’t understand how it got that way, so that’s my pitch for draw out everything. And it doesn’t have to be fancy. I’m talking whiteboard fidelity, right? But…
Larry:
I wouldn’t expect any other approach from a data vis guy-
Noah:
Right. That’s me as a diagram thinker, not me as a visual analyst, except my brain loves all these pieces, but they’re not 100% overlap. The Venn diagram of those is not 100% circle.
Larry:
Cool. Well, thanks. Hey, one last thing, Noah, what’s the best place for people to stay in touch with you on social media or?
Noah:
Sure. I go through phases of being active and not so active on Twitter, but that’s a great place. My Twitter tends to be data viz and UX, bicycles and climate change. Lot of politics, a lot of US politics for the last 4-5 years and cute animals when I remember to do that. So I’m happy to be called out for not having enough cute animals, but I do tend to post data viz and UX content as I see it. So yeah, Twitter and my Twitter handle is just @noahi, N-O-A-H-I. And then my very neglected blog is ComplexDiagrams.com. It’s less of a blog and more of an archive of some things I’ve written: slide decks for conference talks. And we’ll put the link for my four pillars design approach, and my guaranteed successful design talk up there. And those are both pretty self-contained one-off pieces of content that people can view at their, leisure. And I’ve got slide decks and recordings and all that. So there’s good… Consume whatever version you like.
Larry:
Excellent. Well, thanks so much Noah. I really enjoyed the conversation. Fun to catch up with you.
Noah:
Absolutely. Thanks so much for having me on, this was great.
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