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050 – Ways to Practice Creativity and Foster Innovation When You’re An Analytical Thinker

Experiencing Data with Brian T. O'Neill
050 - Ways to Practice Creativity and Foster Innovation When You’re An Analytical Thinker
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50 episodes! I can’t believe it. Since it’s somewhat of a milestone for the show, I decided to do another solo round of Experiencing Data, following the positive feedback that I’ve gotten from the last few episodes. Today, I want to help you think about ways to practice creativity when you and your organization are living in an analytical world, creating analytics for a living, and thinking logically and rationally. Why? Because creativity is what leads to innovation, and the sciences says a lot of decision making is not rational. This means we have to tap things besides logical reasoning and data to bring data products to our customers that they will love...and use. (Sorry!)

One of the biggest blockers to creativity is in the organ above your shoulders and between your ears. I frequently encounter highly talented technical professionals  who find creativity to be a foreign thing reserved for people like artists. They don’t think of themselves as being creative, and believe it is an innate talent instead of a skill. If you have ever said, “I don’t have a creative bone in my body,” then this episode is for you.

As with most technical concepts, practicing creativity is a skill most people can develop, and if you can inculcate a mix of thinking approaches into your data product and analytical solution development, you’re more likely to come up with innovative solutions that will delight your  customers. The first thing to realize though is that this isn’t going to be on the test. You can’t score a “92” or a “67” out of 100. There’s no right answer to look up online. When you’re ready to let go of all that, grab your headphones and jump in. I’ll even tell you a story to get going.

Links Referenced

Previous podcast with Steve Rader

Transcript

Brian: Welcome back to episode 50 of the Experiencing Data podcast. This is Brian T. O’Neill, your host. And my guest today is… me again. I’ve been rolling a couple solo episodes if you’ve been following along the last couple of weeks, and I got enough positive feedback that people seem to be enjoying these, so I’ll be doing them from time to time. We’re still going to have guests coming back, but for episode 50 here, I thought we’d keep it solo and share some thoughts around innovation and creativity for people working with data and analytics.

So, these things don’t always go together, and it kind of bums me out when I meet somebody that is very strong technically, very strong analytical thinker, great technical skills, and they tend to think of themselves as not being creative. My favorite quote, or my least favorite quote, was talking to a chief analytics officer recently, and he just said, “Brian, I don’t have a creative bone in my body.” And the main thing I wanted to talk to you today is about this, kind of, mental block here and what some of the tactical ways of actually practicing creative work and thinking about innovation from a series of steps, and activities, and behaviors that you can actually do instead of thinking about it as this black box thing that only resides in the minds of artists, and creators, and designers, and things like this.

So, I think a lot of you know that part of my mission is really to help people who think this way, and who are very gifted and talented with their work with technology and data and analytics and data science to tap into what human-centered design can do and how it can help you deliver indispensable products and services to your customers. And part of this, a lot of this is about the mental part of it: the way you approach the work, the way you think about your work in terms of problem-finding versus problem-solving; the role of empathy, which is really about putting ourselves in the service of others. And I really do mean that if we start to change the work from being a technical problem that’s staring you in the face, it’s kind of you versus it, and instead, thinking about, “My job is to enable somebody else.” Like, most of the software that we’re all working on is not for ourselves, it’s typically for somebody else, and so kind of getting that mind shift in place.

But anyhow, I digress. Let’s jump into this topic here around innovation and creativity. So, I hope that you guys don’t think of this as—how do I say this—I don’t want to be disparaging. I’m going to be throwing out stereotypes and generalities from the many conversations I have at conferences, and just phone calls, and research, and trying to talk to people my list, and clients, et cetera. So, these are very broad, sweeping generalizations that I’m going to make/ some of you may feel some similarity or this might resonate with you. Others, maybe not so much. But everybody’s unique. I’ve met plenty of skilled technologists who are also talented artists and musicians, especially around Boston. There’s a great great many of them. So, without further ado, let’s jump in a little bit here.

The first thing that came to mind when I was thinking about this was that to reframe this, as humans as a race, we haven’t been dealing with giant amounts of data and analytics that the world now puts out every single year; it’s multiplying like crazy. Not even 100 years, have we really been dealing with this kind of information. So, what does it mean to not deal with it? Well, it means we made decisions and we lived without having all the insights that we do today, which really reduces our guessing. That’s a lot of what we do is we inform better decision making with all this data.

We had to rely on trial and error, and our gut, and experimentation; we had to use other techniques. And one place I can see this getting in the way, just to give a very tactical example, was I had been consulting at a very well known online travel agency. We’ll call them an OTA. And this company was relentlessly using A/B testing on their website. And I have no problem with A/B testing; it has a role, and it’s a great technique for certain kinds of work.

But this company, a huge amount of the attention for the overall product strategy and design strategy was on one screen, was their hotel search screen. And this company was kind of gun shy when I arrived there, and they were looking at the work that needed to be done as kind of optimizing this one screen to basically get people to type dates in for their travel and get back pricing from all the different operators and hotels, et cetera, that provide these travel services, hotel rooms, and whatnot. And the vibe I got, when I walked in the doors, I could tell the joy in the work was gone for a lot because, in somewhat of a recent past apparently, there was a large scale redesign; it did not go well and the company lost all appetite to experiment beyond what they could immediately prove with data.

And what this translated into was that they were constantly running these very small-scale design changes on the site and testing each one and trying to isolate—you know, change the color, change the button, move it over, change this text, very tactical approaches to things, and optimizing. And, again, there’s a place for this with conversion. This is not new stuff. Many of you will probably know this if you’re doing any type of web analytics work.

But the point here that I want to get across is that this type of thinking where everything is analytically-minded, even when you’re doing design work like this, you’re focusing on a local maximum. You’re only going to get to the top of the peak that you can see right in front of you, but it’s not going to tell you what the next big idea is, what the next big problem is to solve; it’s just going to tell you of the choices that you throw at it, here’s which one performed better, and you can keep optimizing down. And on top of this, what can also happen—and this is what happened there—was that they started ruling out—any changes that failed once would be thrown out. So, what this means is if green background color on the button didn’t work, then they will never use green on any buttons, regardless of what the label of the text of the button was, surrounding content, all these other variables that may have something to do with how the conversion worked.

But if you threw that idea out, forget ever presenting that idea again. It was now a forbidden choice. So, this kind of approach where we’re thinking analytically all the time about everything, this is not how we do innovative work. This is not how we find out what the next big approach might be to solve the customers’ problem. It’s not even to help you see the problem really well; you’re just going to keep iterating and refining on what’s in front of you.

And I think part of this is the mindset—and I understand conversions matter, in this case, and it was a big part of it. And there’s shareholders and all the whatnot, I get all that stuff. But as Peter Drucker famously said, “Marketing and innovation. Those are the only two things that really matter, and everything else is a cost.” So, if you want to lead with the data work that you’re doing, and you want to be seen more as a center of excellence and innovation, if you want to find ways to leverage analytics and machine learning, to create better customer experiences, whether that’s an internal customer, or supplier, or employee, or whether you’re talking about the people that buy the products and services you offer, you need to wear a different hat to do this kind of work.

So, be aware of these local maximums, be aware of when the analytical mind is kind of jumping to what the next choice might need to be here. The exercise of creativity, which hopefully leads us to innovation, which is what a lot of companies say they’re looking for in their employees, it’s a different kind of work, and it’s a different kind of thinking. And I know this can be hard. These words get thrown around a ton, and I almost cringe talking about it just as much as some of you probably cringe a little bit at the idea of ‘craft,’ and ‘design,’ and some of these words that sounds so fluffy and hand-wavy: they’re so subjective and I understand that. I feel like ‘innovation’ gets batted around quite a bit.

Everybody says everything is innovative now and all of this, and it’s a little bit eye-roll-y for me, too. I think that one of the reasons I really like design, and I why I talked to you about design, and why I want you to apply human-centered design to your work is that it is a step-by-step process, it’s something anybody can learn how to do, and it’s a framework for innovative solutions. And it’s something you can repeat and do regularly as part of your daily work to get better outcomes.

And that’s what it’s all about. Nobody wants machine learning, nobody wants artificial intelligence, nobody wants analytics. What they want are the outcomes. They want the value that—they want the promise; what was the promise of these technologies? What was the service that they wanted to get? What was the outcome they wanted? It’s outcomes, not outputs. So, it’s a different kind of thinking.

And this stuff matters. And I’ll tell you something else too. A recent study—this is just from 2020—came out, and what it looked at was what were the sources of innovation in companies. And apparently, this study found that explaining the innovation output at companies is five to ten times more correlated to the ability of individual inventors at the company, as compared to all the characteristics of the company they’re actually working at, combined.

So, people and the way they work and think—you, that is. For those of you that are primarily employees, full-time employees at companies, you are the source of innovation, or you’re not. But you have a multiplier effect if you are seen as someone that’s doing innovative work. And if you’re a leader, I’m hoping that you’re fostering an environment to allow this type of work to happen, if you are in the product creation business. And again, whether you’re at a technology company selling commercial software products, or your data products are actually internal applications, services, models that are being deployed, I still call those data products, as many of you know by now, I think of them with a product mindset, even if we’re not making money from them because chances are, they’re there to save cost or, potentially, to improve the customer experience. There’s some value there.

And the ability to do this, and to be innovative here, a lot of the output at these companies is tied to the people doing the work. So, how do you get started with this stuff? How do we do innovation? How do we practice this stuff? I was thinking about this, and I was looking around to my own work that I’ve done with clients, and a lot of people associate design with innovation because they realize that design can change their customers’ experience, they understand how it can impact the business, they see what design-led companies like Apple—they understand that there’s something there even if they don’t know how to do it.

So, what I want to do is give you some practical stuff; take away the black turtlenecks, and the big reveals, and some of the hype that goes with this, and focus on the activities and the behaviors that you can practice on a regular basis. Things that you could put into work at your company today. So, let me jump into those. The first one is that—and again, I’m thinking about leaders here, and I’m assuming most of people listening here are leaders or perhaps you’re looking to be a leader in the future, and you’re in charge of the ship; you’re driving the ship whether it’s a product, or a whole suite of products, and entire department, and this is who I’m talking to. Because a lot of these things can’t happen without the proper management and leadership support; you need an environment to do this, and I know that’s really hard if you’re in one where it feels very closed-minded, this can be difficult.

But we can get into how—maybe in another episode—how an individual contributor might deal with some of these problems, but because my primary audience to serve are software leaders in data, and product, and technology, and engineering. So, the first one here is you have to create dedicated time to work on things that are not deadline-driven, and are not just about the latest fire drill. And I’ve seen this a lot, probably more so the fire drill stuff with the technology companies where they’re constantly chasing whatever the latest thing is. You know, a big customer wants to come in, and, “Well, we’ll only buy if we have this feature.” And the whole ship stops and everyone’s now working on this other thing.

And I get that. Sometimes that is the right thing to do. But if that’s the only environment you’re living in, forget about it. You’re not—the only way you’re going to come up with something innovative is probably by luck, and I want you to have repeatable processes and repeatable behaviors because we can’t rely on luck to get us to the next mountain top, so to speak. It’s just not reliable.

So, the next one here is beyond creating this dedicated time to work on these spaces, to work on innovation and to think about projects and products and ideas that are not deadline-driven and fire-drill-oriented is that you have to have the right people in the room in order to do this type of work. So, what does that mean? Well, we hear a lot about diversity. And I think diversity is really important here.

And I’m not just talking about racial diversity, gender diversity, although I will say right now, simply adding some women to your team—and I happen to know just from looking at my podcast guests, the leaders in the data products industry, machine learning, analytics, it’s a white male industry. And if you’re only surrounded with people like you, you’re not going to come up with stuff that’s as interesting and creative and innovative if everybody in the room looks like you. So—but moving beyond gender and race, we’re also talking about inviting outsiders, inviting customers, inviting resellers, inviting the sales team, the marketing team, subject matter experts.

Why do we do this? Well, when we’re thinking about the next level up, we need a volume of ideas; we need to get lots of different ideas, not jumping into one solution, implementing technology, and then hoping that when we throw it at the customer, it’s going to work. And it’s rare you see this happen because, speed, speed, speed, speed, speed. You hear it all the time.

But that assumes that the cycle time is really fast. So, I would say, it’s probably okay—you could probably get somewhere innovative—if you can rapidly iterate, learn, change, ship, get feedback. If that cycle is really fast, by all means, feel free to keep shipping code, getting feedback, changing the designing experience, and getting closer to what customers need. You can do it that way. That’s okay. Very, very rarely is that the case, though.

I find that, especially when you’re talking about the next big problem that you want to work on and thinking about six months, twelve months down the road, and not just to the next quarter and this type of thing, it’s too hard to do this kind of stuff. And you still need a volume of ideas. And why do you need a volume of ideas? Well, part of the reason having a bunch of different people with a volume of ideas, it’s not that one person’s idea is going to, necessarily, be the right one. It’s more about the fact that one idea might spawn someone else in the room coming up with an idea that’s a spin on that, and you get this snowball effect.

So, you get the, “Yes, and” kind of mentality. “Yes, we could do that. And what if we also did this?” “Yes, and what if we could also do that?” You need a breeding ground for that, and if we just come into the room with one or two ways of doing something, and we’re ready to jump into implementation, this is not how you do innovation.

This is—maybe you get something innovative once in a while, but again, that’s relying on luck. So, this divergent thinking—and this is something I practice and teach in my seminar, it’s a very common thing in the design thinking process, as it is sometimes called; I just call it design, but some people call it design thinking. But having a volume of ideas can really lead to creative ideas that no one individually in the room would have thought of, but the collective intelligence of the room can spawn some really interesting things. And we’re bringing all these different perspectives. You give a customer service rep in the room, they’re going to have a very different perspective on what, say, someone in product marketing or even product management might think, who might have—you know, maybe they’re relying on market research, and they’ve bought research surveys and some of this kind of stuff, to kind of think out, “What should we be doing with data in the future?”

And on the other end, you have someone that’s dealing with customers on a regular basis—if you’re talking about, like, a B2C company or something—and they’re hearing daily from the actual people who are the recipient of all this work that’s happening. And whether or not having a CSR in the room during your ideation work here is the right person, the point here is that their perspectives are really different. And having a salesperson there; let’s say you’re at a tech company and having a salesperson participate in some of this work, they also know what is difficult about communicating the value of the data and insights that we have? They are a super valuable source of intelligence for the team that is responsible for designing the experience that the customer is going to have. And so, CSRs, salespeople, marketers, product managers, designers, and of course, the technologists as well, especially with the knowledge of what’s possible—what do we need to do this kind of work?

But it’s really about wearing a different hat, and realizing that it’s time to take the implementation hat off and to think creatively, and to think about volume. So, if you want to practice this idea, the way I would think about it is when you get this group of people together to work on a particular customer or business problem, make one of your first activities about generating a volume of ideas and try to separate ideas from the person who came up with them. It’s not about who came up with it; it’s not about owning the idea; it’s about generating a volume of those ideas. So, there’s lots of different techniques for doing this, but the core of this is really at the quantity and not jumping into the quality of the idea, or jumping into an implementation plan too soon.

The next one is open innovation. And I’m not going to talk too much about this because we have a great episode on the show. Steve Rader from NASA came on the show to talk about open innovation. But this is the act of looking outside of your company for solutions to problems that perhaps you’ve worked on a lot and you haven’t gotten too far with them yet. But the idea here is, kind of, putting almost a for sale sign out, like, “Hey, we have this problem, it’s for sale. We need to invite people to come in here and tackle it.”

And this is something that’s almost happening right now, with—at the time I’m recording this, we’re still in the middle of COVID. Now they’re calling it the third wave, at least here in the United States. And so, you actually have all these different people, all these different companies out there working on vaccines, and PPE, and analytics, and insights, and all this kind of stuff. Imagine if your company had also put out that for sale sign and invited outsiders to come work on some of the problems that you have. A volume of ideas from people that maybe don’t even know so much about your particular industry, but may be able to approach a particular aspect or a particular part of the problem from a different perspective there.

And it may be such that they don’t necessarily solve it, but in the act of looking to outsiders to come and help you, you again may get an idea; it may plant a seed for what the actual path is that you should go on. But that seed might be something that never would have come out of your own team. And a lot of this is because we go native at some point. You get used to the way your company thinks, your team thinks, and this happens a lot.

Hiring, especially in data science and analytics teams, there’s so much hiring around technical skill sets—and that is of course, really important—but you’re going to end up being surrounded by people that tend to think the same way. And this is why I think teams that have product functions on them, they have a designer or user experience function on them with the data science and analytics, and the engineering pieces there are more likely to come up with better solutions. Because they’re all approaching the problem from different perspectives, and you really need to have that.

So, open innovation, check out the episode with Steve Rader from NASA on that was really fantastic. Great story in there about how do you remove the excess grease from potato chips in the factory where they actually produce potato chips; this was a problem, and this problem was actually outsourced, and it’s fascinating to hear the way that they came up—the technology that they came up with to actually reduce the fat, the grease that is on the chips at the time it’s put into the bag. So, check out that episode—really great. The next one here is improvisation, and empowering the group over the individual, and this kind of idea of teams. So, design is very much a team sport, and the best designers out there are not the ones living in the tools, setting type, choosing colors—and all this stuff matters; these fine details matter and I’m not saying they don’t, but really what the best designers are doing is they are facilitating the team; they’re facilitating the group coming up with a common framing of the problem, and helping the team understand how the experience is very much tied to what the value is that is going to come out the other end of the technology initiative. This is true whether it’s analytics, or advanced machine learning, whatever, AI, doesn’t matter.

So, what is this role of improvisation? So, I’m going to use an analogy here from jazz music. A bunch of that I’m also a professional musician, and I do play jazz as well. And one of the things about improvisation and jazz, it’s not just a total free-for-all. A lot of what it’s about is exercising your individual creativity within a group who is there to support you and to allow you to take those leaps, and to—when you’re taking a solo and the band is backing you up, you’re improvising there.

You’re trying out ideas live, in the moment, right now. And the team is not there to judge you. The team—the band—is there to support the soloist. So, there are loose structures here, but we’re experimenting and relying on the bandmates—our team—to support these expressions, these trials, these attempts.

We’re not there to sabotage each other. We’re not to say, “This is why I said we shouldn’t have done with this idea,” et cetera, et cetera. It’s not about that, that doesn’t help the customer. Infighting, and the politics, and all of this. If anything, we want to take a misstep or a mistake and turn it into something positive. We want to see, how can I riff on that? How can I take that idea—maybe that’s not the right way to go, but how could we come up with something better here?

And one of the things I like about this idea of improvisation in teams is the story about how there’s a very famous pianist named Herbie Hancock, which maybe some of you have heard of, and he was playing on stage with Miles Davis who is probably the most, maybe the most famous jazz trumpeter. He was alive in the ’50s and ’60s and ’70s, and Herbie was in his early point in the band with Miles, and he was comping, which means he’s playing chords on the piano, largely to support the soloist, which at the time was Miles. And Herbie played a quote, “wrong chord.” He did not play the right chord at the right time in the song.

And so instead of judging the chord as being wrong, what Miles did is he changed the notes he played while he was soloing. And by changing the notes in his solo, and adjusting them to the chord, instead of expecting the chord to adjust to him—even though we tend to think of, when the person is soloing in the ensemble, we’re there to support them, they’re not there to support us as much. That’s exactly what Miles did is he changed the notes to make Herbie’s wrong chord sound right. And I think there’s analogies here to how we do work together in groups. And it’s more about this idea of the risk-taking here and trying to support the act of risk-taking together because ultimately, if you’re not taking any risks, and you’re not ever taking some big swings, and missing, and the company culture is not supporting these occasional misses and thinks that every increment of work that is done is supposed to create value, you’re never going to come up with something innovative because by definition, you have to try stuff, and not everything is going to work.

And this also gets a lot of lip service, but I find a lot of companies aren’t willing to do this. A lot of them are so focused on the bottom line that they can’t see beyond taking a big risk and realizing, “You know what? We’re probably going to really make some people mad with this change we’re about to roll off, but you know what, this is the right leap to make now, and we need to start looking a mile ahead instead of just looking a few yards ahead and taking those risks.” And sometimes they’re going to be wrong, but sometimes they’re going to be right.

So, innovation doesn’t happen if we’re just doing incremental improvement work all the time; it’s not going to happen. So, get the team together, understanding that individuals in the team should be taking risks, and the team should be supporting those risks, and the team should be trying to take the ideas of the individuals and make them work towards the common good, towards the common problem we’re trying to solve with this data and this technology.

The next one is fostering and creating, and finding a company culture that allows for experimentation and failure. So, I kind of talked about this right now, but I want to call it out as something distinct. You need to think about this as what I call, reward the at-bats, and not all the base hits. How many times did the team get up and take swings? How many swings did we take at this problem space? And have we even figured out what the right problem is, right?

That’s another thing is, what game are we even playing? Have we clarified that on top of the how many at-bats has the team had? And there’s a difference, right. In baseball, we tend to reward the points scored, the base hits, and all of this. We’re not rewarding the number of at-bats because there’s going to be strikeouts.

Well, I think you actually do need to have strikeouts here, and the point of the strikeout isn’t to strike out. I mean, obviously, we would love to have more base hits, but the real point of it is, what did I learn from the pitches that were just thrown at me? What did I learn when I was at bat? What did the team learn when we went to bat, and swung, and we didn’t connect?

How fast are those learning cycles, the iterations that you’re doing? Are they fast? Are we taking new information in and putting it through our brain grinder and spitting out something better the next time we go up to the plate, or are we just swinging the same thing all the time? So, think about rewarding the number of at-bats. And I know you can’t always do this all the time on all projects; there’s not time to do this kind of work. But if you’re not ever taking the time to do this kind of work, you’re not going to come up with innovative solutions to things. You’re just going to be focused on that local maximum.

And the final one here is this is maybe a ‘no duh’ for people that listen to this show, but practicing human-centered design, or design thinking, or just user experience design. For our purposes, they’re all the same thing, and having this product mindset—this is the other piece—and when I say ‘that product mindset,’ I’m talking to people doing applied data science, analytics work, and custom application work within non-software companies. But having the product mindset, as if, you know what? We’re going to roll out this artificial intelligence, this model, it’s going to change some of our operations, it could touch a whole bunch of different departments, and thinking about that as a product, as if you could sell that to another company that would have this problem.

How much would you focus on the customer experience? How much would you care about whether the model did its job? How much would you think about the outcomes? That’s the product mindset piece. The design piece, of course, is practicing these techniques.

And there are step by step things you can do to implement design in your group, even if you don’t have designers. And I’m not trying to turn everybody into a designer, but I actually think you already are. If you’re determining the solutions and the experiences that your customers are going to deal with, you are a designer. You just might not be a good one because your designs are byproducts of your technology work that you’re doing. So, the question is, do you want to get better at that intentionally? Right? That’s the keyword is, do you want to intentionally change the design instead of it kind of being a byproduct of the technology work that you’re doing.

So, innovation can spawn from simply applying a human-centered design process to the work that you and your teams are doing. Designers are accelerants for this, they are experts at this, they help make this process go faster, they help you take more of these swings, and take these risks, and they help you develop a faster and deeper empathy for customers; they will pull you into this customer space, this mentality and thinking relentlessly about who is supposed to receive the value here? And again, customers here may mean a paying customer or it could be an internal stakeholder. A report, or a dashboard, or some artifact, some output, those are still customers or users here. I’m using these terms broadly.

So, if you don’t know how to do that, a lot of you know I have a self-guided video course on my website, as well as an instructor-led seminar that I teach twice a year, those are great places to learn. There’s tons of free resources on how to practice these techniques. Of course, hiring staff, or bringing expertise are other ways to accelerate this process. But the point here is, you don’t have to be a title designer to do this work. There’s a lot of this work that is skill-based, it can be taught to other people, and ultimately, I think that my personal mission with this work is I want to see design happening in these places because today’s data leaders, and data science and analytics leaders, more and more—especially as AI gets adopted—we, or you all, are going to have a big impact on the society and the world that we live in.

And so I want to infect that work with design. I want you to practice design. As you do the amazing technical work that you do, I want you to use this as a way to make sure that you’re serving the audience that you intended to serve, ethically, with trust, with user experience in mind, with usability in mind, with accessibility in mind. I want you to be able to do that, and there are ways to do that. So, again, you can get a demo of my course, or seminar, or go to any other place. The difference with my work is that I focused it for data practitioners, technical product managers, and people that tend to think like we do: they live in this world of data and trying to create new and innovative solutions with data. So, those are my activities. Those are the behaviors I want you to go out and practice.

This was episode 50. I wanted to give you something both actionable, but also kind of high-level and strategic in terms of the mindset here. And whatever you do, be aware of those local maximums. Don’t try to be the individual hero. Remember that design is a team sport. It’s not art. We’re not here for self-expression. Design is about serving others, it’s about empathy, and it’s about having an impact. It’s about creating an outcome, not just creating an output.

Why does that matter? Because nobody wants data, analytics, machine learning, artificial intelligence, software. That’s not what they really want. They want to feel something, they want to change something. In our case, they usually want decision support, decision intelligence, actionable insights. Those are the outcomes; “Help me make a decision.” That’s the game we’re playing. So, go out, take a bunch of swings, I hope you hit some base hits. Hope you hit some home runs. Good luck. Thanks.

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