About this episode
Imagine planting seeds and not watering them. Or putting water on the stove but not turning on the gas. Sounds silly, right?
Now take your organizations data and think about not leveraging it as an asset. It’s like taking the initial step of planting something in the garden and deciding not to provide nourishment.
Join Tim, Juan, and special guest Jane Urban from Takeda to understand the difference between a data asset and a data product and why companies still struggle finding the real value.
Speaker 1: This is Catalog and Cocktails, presented by data.world.
Tim Gasper: Hello everyone, welcome, welcome, welcome to Catalog and Cocktails. It's your honest, no BS, non- salesy conversation about enterprise data management with tasty beverages in hand, brought to you by data.world. I'm Tim Gasper, longtime data nerd and product guy, joined by Juan.
Juan Sequeda: Hey, hello everybody, I'm Juan Sequeda, I'm the principal scientist here at data.world. And as always, it is a pleasure to go spend middle of the week, end of the day, go chat about data, have that honest, no BS chat about data. And today I am super excited to introduce our guest, who is Jane Urban, who's the VP for global commercial and medical data at Takeda, and who's had so much experience in healthcare and pharma data, and spent time in consulting. We're going to have so much fun time. In our prep session I was like, " I cannot believe everything that you have done and what you're planning to go do." This is going to be so cool. Jane, how are you doing?
Tim Gasper: Welcome, Jane.
Jane Urban: I'm really good. Thank you, thank you for bringing me on, I really appreciate the opportunity to be here and chat with you today. It's going to be fun.
Juan Sequeda: Fantastic. So let's kick it off. What are we drinking and what are we toasting for today?
Jane Urban: Yeah, so I have the Nippon Japanese cocktail for the Japanese theme, I suppose, which is Japanese whiskey, vermouth, and ginger. And I would say I'm toasting the opportunities of the new year. We're about to hit Lunar New Year actually, this weekend. So happy New Year, is what I would toast.
Tim Gasper: Nice.
Juan Sequeda: Love that. Nice.
Tim Gasper: Happy New Year.
Jane Urban: Happy New Year.
Juan Sequeda: So I prepared the drink for both of us. It's what I'm going to call a bittersweet vodka soda. It's a vodka called Grateful Vodka, and I mixed some agave with some bitters in it. That's a sweet and bitter.
Jane Urban: Nice.
Juan Sequeda: And just some soda, it's actually pretty good. And I'm toasting that we're at the office. We are all fancy dressed up today because we had our headshots.
Tim Gasper: Picture day.
Juan Sequeda: Picture day.
Jane Urban: Picture day, yeah. Luckily I didn't have picture day, so I was not as dressed up, but that's awesome.
Juan Sequeda: No, so it's funny seeing everybody in the office, it's just full of people, I guess, who came because we're taking our pictures. That's why I don't have my t- shirt today. It feels weird to be ...
Tim Gasper: Yeah, I've done it a few times, but you're usually in your honest, no BS shirt.
Juan Sequeda: Cheers, cheers.
Tim Gasper: Yeah, it's great to have you.
Jane Urban: Thanks again. Yes, cheers.
Juan Sequeda: So we've got our warmup question of the day. 18 days in, what new year's resolution have you stuck to so far?
Jane Urban: I would say one thing I've been really trying to be good about is I have a one- sentence journal that I do in the evening, just one sentence about the day. And I've actually made it pretty far. I'm a little more than 18 days, I've done it a little bit in the fall as well, but I really like that as an evening practice, one sentence about your day.
Juan Sequeda: It's a great idea.
Tim Gasper: That's awesome. I love it.
Jane Urban: Yeah, it's easy to do compared to a full journal entry or something.
Juan Sequeda: I like that one.
Tim Gasper: I'll have to consider that, a good way to cap the day, right?
Jane Urban: Yeah, they do make one- sentence journals, so it's a thing.
Juan Sequeda: Good. Keeping it concise. How about you, Tim?
Tim Gasper: You know what, I've been doing a good job of keeping up with my Duolingo.
Jane Urban: Nice.
Tim Gasper: I'm trying to pump up my Spanish and I'm trying to learn Korean. I'm half Korean, but I don't actually know how to speak Korean, so I'm trying to remedy that.
Jane Urban: That's very cool.
Juan Sequeda: 18 days in, good.
Tim Gasper: 18 days, actually a little longer than that. Kind of sort of year, right?
Juan Sequeda: Yeah.
Tim Gasper: How about you? What have you got going on?
Juan Sequeda: So we have something with my wife and I, let's make sure we hug for one minute every day.
Jane Urban: Oh wow, I love that.
Juan Sequeda: You know what, we should sit down and do 10 pushups together every day. Three days in we're like, " Hey, we haven't even done... Yeah, it's not going to happen."
Jane Urban: Yeah, it doesn't sound as fun as a hug.
Juan Sequeda: I've got a bunch of travel starting up soon, so we'll see how that's going to work. But anyways, that's ours.
Jane Urban: You can do a virtual hug, like a video.
Tim Gasper: Yeah, you should, yeah.
Juan Sequeda: Yeah, exactly. All right, let's kick it off. So honest, no BS. What does it really mean to leverage data as an asset, and how is this related to data as a product as different?
Jane Urban: Yeah, so one of my things I say repeatedly in my role is that we have to leverage our data as an asset. And what I mean by that is, you have to care for it like you would any other asset. So you're doing maintenance on it, you're making sure that it's kept up with the latest whatever's available for it. You also have an element of value that you place on it and this notion of an asset on a balance sheet, if you will. And I think that is something that has been sometimes missing in the way people think about data. They think about it more like a byproduct or in some cases like a necessary evil of doing business. And I've been really trying to switch that mindset in Takeda to say that it's truly something of value. Compared to a product, I think it's a bigger concept. The asset being a larger thing versus a product is typically, in my mind anyway, more specific to a problem you're trying to solve for a given customer just like any other non- data product would be. So it's a little different, a little broader, I think maybe a little bit easier to adapt than data products, which is something that takes a little bit more maturity maybe in understanding the value of data. So if you start with it being an asset, at least then it makes sense that you would make products out of your asset. So hopefully that makes sense.
Juan Sequeda: Okay. This is interesting, because actually talking to people, they use it the other way around. I think everybody, first of all, the data as a product definition or data product is something that is not well defined stuff. Let's go into some examples.
Jane Urban: Sure.
Juan Sequeda: Can you give an example of what is an asset and what would be a product, and what is the value and how are you defining what value is and measuring it?
Jane Urban: Sure, sure. So in pharmaceuticals specifically, I think one of our biggest assets is the data we generate on our clinical trials and the work that we're doing to collect information about how our drugs perform in patient populations, both as we're trying to get the approval, and then after the product's in the market we're also trying to measure how it's doing, what the real world testing of that product looks like. And so product for most pharma companies is a drug, the idea of a drug, a molecule or a compound that you're going to sell. So it's an interesting place to be talking about data as a product as well. And so I think the way you think about data as a product versus an asset versus other terminology as the notion of that problem solving nature I think has been something that resonates with folks in pharma. Because we don't make data. We're not a software company, we're not a data company, we're a pharmaceutical company, we make pharmaceuticals. So how do you bridge that mindset around the chemical makeup of a molecule being the thing we make to, how do we make data products? So that's where I think we've been trying to really anchor people on the notion of different kinds of solutions that can be using data to solve a problem that a customer has. So that resonates a little more clearly with people when we talk about product ownership of a data solution. And I try to avoid the word platform, because that in my mind is a different sort of thing altogether. And so there's a bit of that settling in on the terminology that we work on for sure.
Tim Gasper: Yeah, aligning on those terms can make a big difference to make sure we're all speaking the same language. Tell us about value. What does it mean for data to have value, or a data asset or a data product to have value?
Juan Sequeda: And to add to that, I think one of the themes that we're seeing a lot in this year is the whole understanding the business need, making sure that whatever work we're doing is actually providing that value and that value is directly related to ROI. This is the theme of 2023. We're looking at layoffs and looking at everything that's going on. If you cannot show how you're making money, saving money as direct as possible, you're in trouble. So I want to hear this from you. How are you getting at the value of producing it?
Jane Urban: Yeah, I think we do a little of both ends. So there's certain places where having better data can help us make better decisions, and so that hopefully leads to maybe a higher ROI. So that's one way to look at it. So there's that upside of more revenue in on the investment and the same investment, for example. I think there's also an element of data helping us to be faster and maybe more efficient so that we're saving resources, both between using digital and ways of communicating with customers using virtual platforms, for example, rather than physically going and seeing a customer, things like that. So there's an element of data in digital that can save money. So I think we put on both levers. But it is definitely a maturity that we're not, at least I think we're still working on figuring out how to truly tie investment in infrastructure and technology and data to the bottom line of the business performance. It's not easy, it's just a lot of steps in between, especially when the thing you make is not data. But I have to say, this more recent story with Southwest Airlines jumps out to me as an example, which I've been using a lot actually, around not investing in infrastructure and not putting money toward caring for your data and your technology can create some pretty catastrophic problems for you. So it does help explain to folks maybe when they wonder, " Why does this matter? Why can't we just stop working on this altogether for, say, a decade?" And then you can see what that might look like. So that helps in some ways to help people understand the downside risk of not doing it. It may be more powerful in this case than investing is right now. But you're right, there's definitely a lot more pressure to justify and to create some value story when it comes to using data and technology and things like that.
Juan Sequeda: In your role, and what we've discussed before was, you started out at Takeda in the smaller group in the US and you're growing globally. You have been able to go and then show so much value that you're now leading the global team for data. So how has that transition been from something smaller to something growing? And again, what are the lessons that we can be telling people about, " This is how you should be showing value here to your executives"?
Jane Urban: Yeah, I think the first thing is really understanding either your customer or your executive or both, their problems. So a lot of the conversations I have and the ones I'm having even now in my new more global focus role is to really pick apart, what are the big business problems that you're trying to solve? Not specific to data or digital or technology, but just generally speaking, " What worries you, what keeps you up at night?" And those questions I think really help hone in on where data, digital technology, whatever it is, can play a role in fixing things, making things better. If it's about making better decisions, faster decisions, more accurate decisions, anything of that sort of nature, then you can start to talk about where investing in data and investing in, say, speed of data moving around or different kinds of technology like API versus batch or something like that. But it's important you anchor it back to the problem. So folks especially in the executive space who maybe haven't heard of APIs, they just want to know you can do things better and faster. And so I think if you can emphasize it and tell that story really effectively, that can unlock a lot of the value because people now get it. So I tend to use a lot of metaphors, a lot of stories that are hopefully frameworks that people can then latch onto and say, " Well, I know what that is. So now what you're saying makes more sense to me." So talk a little bit about pipes and water, for example. You're trying to flow the data quickly, when I have even fewer pipes it means it's going to move faster, things like that. I also use a lot of food metaphors, I guess that's my interest, around mise en place or prepping data. That concept of, how do you get data ready so that when you need it it's ready to go? Things like that to help people, I think...
Juan Sequeda: Let's freeze from live on these metaphors.
Jane Urban: Yes.
Juan Sequeda: One of the last episodes we did last year was, our metaphor was about jumping into the pool. And it was talking about governance and jumping into the pool.
Tim Gasper: The governance person, the lifeguard, and things like that.
Juan Sequeda: Then you have lifeguards, right? Because it's like... Then you have the shallow end, you have the deep end, and we have lanes. It was a really good metaphor, right?
Jane Urban: That's cool, I like that one.
Juan Sequeda: Let's dive into a lot of the metaphors you're using,'cause I think this is the storytelling that people need to be able to go have these elements to be able to explain it. Because we'll just go back to, " Oh, well, we need this API and this platform to do that."
Tim Gasper: Good communication around data and the value of data requires us to make it simple and make it relatable. So yeah, curious about your food metaphor, for example, what's resonated there? How have you articulated that?
Jane Urban: Yeah, I've got a whole bunch of different food metaphors. So mise en place is one, if you're really into food and food prep that's a good one to talk about. How do you prep your data and clean it up and cut it up into pieces that are more manageable? Things like that, that's intuitive. I think of mirepoix, which is carrots and celery and onion all cut up and put together. That's data I can analyze. So that's one way we talk about how to prep data. So you're going from really raw things that can't be used in your dish to something that you can use. The other one we talk a lot about in the team I just came from, and I'm sure I'll use it in this new team too, is around the notion of data governance and data strategy being really helpful as the farm end of data. What are the things that we need to grow to be able to have the data we need to make the dishes we want to make? And so this notion of farmers who are then bringing those raw ingredients to the chefs. But one of the things I've said a few times actually in other forms is the idea that the best way to do this is people who have done analytics, who have used data to actually answer questions. So the chefs are better if they've already been a chef when they go back to becoming a farmer. So the idea of former chefs turned farmers I think makes you a more effective farmer, because you know why this particular ingredient is so important and the high quality nature of it is so critical to getting the right answer. So it's that notion of quality and preparedness and planning and hopefully getting ahead of the urgency that you tend to see in a kitchen when you don't have the ingredients you need to make the food you're trying to make, is helpful for people to understand why we're putting the investment in up front to get better data, to get higher quality data, and to monitor it, to keep track of it and take care of it and all those kinds of metaphorical things. Watering your data, people talk about hydrating data, I know that's a metaphor I've heard before too. So I think the food thing for me is easy to, I can use lots of different pieces of that puzzle to tell the story, and I think most people have some sense of farm to table or whatever, they have that idea. So it tends to work really well for just pulling people in and giving them something they can play with. And then you know you've got it when they start using the metaphor back at you and trying to come up with different sous chefs and head chefs and whatever, they're like, " Okay, we're good, we're making this happen."
Tim Gasper: People are getting it, right?
Jane Urban: Yeah. Yeah.
Juan Sequeda: Another metaphor I've worked with is thinking about the manufacturing process. This is something I chat with my buddy Muhammad Asser, is you have the job shop process and then you have your continuous flow.
Jane Urban: Yes.
Juan Sequeda: So you're going to go develop the Toyota Corolla, that's just a very cookie- cutter, we know how to go do that. That just goes through the flow. But then you're going to have this nice high- end vehicle which is just coming out. We don't know, we don't have a lot of quantity of these things, it's going through some innovation. So you have to have something, very specific approach to that, and guess what? It's not probably not going to have all the sophistication of all the checks and balances you have for the continuous flow because that stuff needs to just go out and out. And what happens today, I feel, is that we want to be able to, " We have to have all this stack, all these things, all these data tools and all these 40 different tools," but we're doing something that is probably very innovative that we just need to move fast on this stuff. Where for the things that need to go out without a problem, we're not investing in it. So I think we need to really understand, are we doing something that's continuous flow that just everybody expects it to work, or are we doing something that is really innovative or needs to be very quick, we don't know what the value is, let's go test this out, and that's something specialized? And there needs to be a transition too. The moment we start saying, " Hey, people are buying this stuff, maybe we need to go figure out how to make this process cheaper, faster." Okay, let's take this job shop manufacturing process and turn into a continuous flow process. I think that's another analogy I like.
Jane Urban: I like that one, especially as you trans transition from the data science, analytical space into something that's maybe more engineered and repeatable. And then you have that nice, it flows actually with the way we talk about the roles in the data world. So that makes a lot of sense. One thing I would tweak on that or at least add to it is, I try to make people understand that when we talk about setting up some sort of process that runs daily or weekly or monthly for a report that you update every week or something like that, which has that continuous, repeatable thing, the one piece that I often find that folks who haven't really worked with data don't understand is that data's very chaos oriented, it tends to break. So I actually just recently experienced this with one of my teams, where they had had a steward to manage their data and they were getting to a point where they had fewer and fewer issues and the steward had less to do. So they're feeling really good. " Okay, we got it." And then they got rid of the steward, and within a few weeks things started to fall apart and there was a sudden panic. And that's when I heard about it, like, " Oh, we got rid of our steward because our data was really high quality and we don't need a steward any more." And that's where I have to remind people that you're just never quite done. Even in those processes that are running really nicely, every so often there's some weird thing where the week ends on a Thursday and that somehow wasn't, I don't know, we didn't anticipate that. And so now everything's broken. And that's the things that I think people forget because they haven't lived through enough years maybe of data chaos. So keeping in mind that even the manufacturing plant, it's not completely unstaffed. There's still somebody there just in case something weird happens who can change something or turn it off, fix the thing, turn it back on again, whatever that looks like. So just keeping people aware of the unfortunate never- ending need to care for your processes is important too.
Tim Gasper: I feel like that relates to a conversation that we had at DGIQ, when we had a panel with Shannon and Anthony. And one of the things that we talked about was the fact that it can be really chaotic in this sort of environment. And it's easy sometimes to think of things like governance as project based and be like, " Oh, we've accomplished compliance and we're done," or, " We rolled out our stewardship program and we're done." And one of the big things that they pushed on that panel was to really think of governance more like a function, more like HR, for example. Do you ever think, " Oh, well now that we ..."
Tim Gasper: Yeah,
Tim Gasper: we are done with HR.
Jane Urban: We've hired everyone we need, we're never going to hire another person. That's going to be totally... Yeah, it's interesting, Takeda has, I think, taken a really bold bet here with this function, data, digital, and technology which I'm part of now. It is meant to be something like HR, like finance, like ethics and compliance. These are things that endure. They never really stop doing what they're doing. There's always more that is needed, that's helpful, that creates value. And so as we're trying to reposition data, digital, technology, there is a bit of that trying to raise the awareness that you're never really done with these data, project is a word we're trying to bring away from the nomenclature. Because if you think about building some sort of a process or a solution in technology, it's always having iterations. There's always going to be a new release, a modification of some sort or other. So you're never really done where you can be like, " Put it on a shelf, it's complete." And so that's important I think to keep in mind, that the same way finance is never done and HR is never done. There are cycles and different parts of the year, say, but it's always there. And I think that's what DD& T, or data, digital, and technology is meant to also be like that. And I think that's an evolution from maybe where we were with IT, where it was kind of project based, like, " We're going to build a new something and then be done and release it and it's done." Now I think we're saying no, this is a never ending adventure that we go on when we try to use these things for solving problems.
Juan Sequeda: So I agree that this is never done, but we still need to start showing progress, start showing value in its value. Again, I want to continue on, let's keep brainstorming on the metaphors and stuff, but how are we describing, " This is the value that was done"? How should we be measuring this? Every so often you have a cycle, and what is the outcome? How do you know that you have been solving that problem? And in particular the pharma world is very unique. So I'd love to get your perspective from a global general perspective, but also from the pharma perspective.
Jane Urban: Yeah, it does, it varies. It depends a lot on what you're talking about and what you're doing. Some examples that spring to mind for me, though, I think we've done a few projects over the years where we've added new insights or new data, whether it's a dashboard or report or we've sent out some kind of new data based information to our teams, especially in the field. For example, I distinctly remember, this was probably 2016, 2017, we released a new list of physicians targeted and ranked by how engaged they are with the same therapeutic area, how many patients they have that they're treating, things like that. And that information was helpful to our field teams to know who to talk to and why to talk to them, and a little bit more about what was going on in those clinicians' practices. And so that kind of insight from data, we happened to put the date when we released this new information to the field into one of our forecasting models. We just added it as a point in time in the forecast. And after we built out the whole forecast for the year and looked back at that moment in time, there was an actual pivot in the sales of the product within a few days of when that new information had been released. And other things were happening at other times, but that was really closely aligned to that updated information. And so we ultimately had to conclude that by giving the field people better data with better ability to talk to the right people at the right time, we did better as a company. It's a rare moment that you get that lucky that you just happen to put it into a model and it shows up as being a really strong driver of a little bit of an unusual situation in our sales, and it couldn't be correlated to anything else that was going on. And so it was really exciting, because for me that was the story I needed to say, " Look, when we have better information, we make better decisions and we do better as a company." So those are some ways we get a little bit lucky, I suppose, rather than necessarily planning for it. But that helped us, I think, as an analytics team to really start to change the opinion of what data can bring to the team. So that for me was a really cool moment and probably gained me a little bit of room to take some other risks and do some other things. Because people were like, " Wow, that was really helpful." So that's part of it, I think too, is having that ability to notice the impact that you're making and measure it when you can. And we were measuring overall all the different tactical things we were doing. So at the time we were doing different kinds of live interactions. We also had TV ads, we had stuff on the web, banner ads and digital advertising and things like that. And it was pretty clear that this was something that actually drove some of the ROI. The other thing was that the data and digital stuff was a lot cheaper to do. That also helps. So you can say, " For every dollar you're putting into these things, you're getting a lot more dollars back because they're not as expensive." So I think those kinds of things can really help tell that story and put it with numbers behind it, rather than just because it's cool or because everybody else is doing it. Which is a reason, but it may not be as compelling as, " Because it helps us to do better from a revenue perspective," which is usually pretty compelling. So that was one of my earlier wins that I think bought me a lot of space to continue to build and grow because it was pretty legit.
Tim Gasper: Yeah. Collecting those outcomes and those accomplishments end up being a key way to advocate for the team and show the value. One thing that I think is interesting, so I want to tie this outcomes conversation back to the beginning when we started to talk about assets and data products.
Jane Urban: Yes.
Tim Gasper: Are you finding that through your organization and in general, where is the value creating activity? Is it happening in taking the data chaos and identifying and managing the data assets? Is it happening in data assets to data products? Is it totally unrelated to that, it's a second plane on this? Curious on your thoughts on this.
Jane Urban: Yeah, I think there's a little bit of value in each step. It's hard to break that down. So for that example, that's an investment of, in the case of our space we actually have a lot of data in pharmaceuticals. So we have prescription data, we have claims data, we have all these different sources for activities that hospitals and medical practices do. So we pay a bunch of money for that. So there's an element of investment in that data, and then there's work to clean it up, to filter it, to get the right diagnosis codes or whatever to be able to do the analysis you want to do. And then there's the element of how you serve it up and where you put it and how you show it to people so that they can take action on it. So each of those, I would say like food, you have to pick it out of the ground, you have to clean it up, you have to chop it up, you have to serve it up and put it on a plate with a little garnish. All those steps have some value to them. It's hard, I would say, at least from what I've seen so far, it's difficult to break it down by each of those steps and say, " Here's the relative value of each step," because they're all needed to get to the end. The dish wouldn't work if you didn't do all those things, you know what I mean?
Tim Gasper: Right, yeah.
Jane Urban: But I do think you can start to see how much you're spending, so at least you have the I in the ROI pretty clearly. So if you look at, how many people does it take to clean up the data, how many people do the analytics work, and how much does the data itself cost? You can start to see the relative investment and then you can say, " Well, if this is the full return and the full investment, I have a sense and then I know as a percentage what I spent." So there's some places you can look at that. But I would say that each of those components is really important, being able to clean it up, being able to serve it up. All those things are what get you to the actual outcome you're looking for, which is hopefully better experience or more revenue or whatever the measure is.
Tim Gasper: Right.
Juan Sequeda: Which goes back to something you added earlier when we started off saying, " Hey, data is an asset, asset is larger than a product, it's something you care for, it has value, and it's on a balance sheet."
Jane Urban: Yes.
Juan Sequeda: So technically you should be able to say that this asset that I have here cost X and I can tell you where that X came from and calculate the time, the people...
Jane Urban: Yeah, so I think that's where you think about it in a profit and loss measurement sheet. It's a liability in some cases depending on how you look at it. But it's also an asset, right? Because it's creating value over time, which is I think how you try to think about an asset.
Juan Sequeda: At Takeda do you actually have data assets on your balance sheet?
Jane Urban: We don't have it in that exact financial treatment, but we do have the cost of what we're spending on data in our measurement of how our analytics team is spending and what they're doing. So I won't say it's quite like that, but it is an interesting thing to play around with, I think, over time as we get more mature. I would see the possibility of, especially if you end up building something that somehow has value in its own right, I think one of the things that's happening in the healthcare space that's an emerging trend is the notion of digital healthcare products. So actual, whether it's sensors or algorithms that help predict disease outcomes, if there's something where you put in some lab results and some family history and a bunch of other inputs, you can decide from that calculation what's the risk of disease progression, for example. Those are data products that actually create value for patients. And you could in theory get them perhaps approved through some sort of regulatory body and reimbursed by your insurance company and things like that. So there's not as much of this in the US, it's still kind of emerging. I know we have some examples that we're working on in China and Japan, so different countries are in different places with this. But there is this notion, I think, that eventually we will use digital healthcare solutions.
Juan Sequeda: This is fascinating.
Jane Urban: So that's pretty exciting, that idea.
Juan Sequeda: Super exciting, because now you're saying there is this product being used that can actually be charged to insurance companies, so they have to go pay for it. And this product is literally all based on data that was done. So I think you have that external factor which is forcing you to put a price on this stuff.
Jane Urban: Yes. I think that's what's going to change the way that data, digital, technology sits in the organization. Because before it was back office, byproduct, whatever you want to call it, and now we're thinking of it actually as a potential revenue generator in its own right. Now, this is early days, it's going to take some time. But I think we're heading in that direction. Because think about the amount of data you collect in your Apple Watch. There's so much happening. We're learning so much about how you have a continuous monitoring of your heart rate while you're sleeping, while you're awake, all these things. Slowly I think we're going to figure out how to tap into all of it. Right now we're not really using all that data, it's just sitting in an app. But eventually that's going to become a way that we can really measure how people's health is improving or not. And Apple's trying down this path for sure, and others are also looking at it. I think the first place is really going to be more with diagnostics and things like that, where you're saying between this picture maybe of some kind of image of someone's body plus some other information, maybe a lab result, whatever, we can start to build models and algorithms. You look at what ChatGPT is doing with different inputs. I think there are now a version of that with imaging, for example. So slowly there's going to be a bigger, I think there's a trend, I would say, in a macro sense toward digital health as a product that becomes reimbursable in its own right from just a drug molecule. So it's kind of exciting times for DD& T in the healthcare space for sure.
Tim Gasper: Oh that's cool.
Juan Sequeda: One of the things, my 2023 prediction is that, more than a prediction, it's like an ask for the data community, is start talking directly about ROI, like, " This is what you need to do." And I've already been seeing some conversations going on. People are like, " Yeah, you know what my answer is? Data monetization." And then they're going to go jump all the way to the further extreme saying, " Oh, we're going to go sell our data." And then they're just going to go off and not... They're not even economists in this stuff and they're talking about it. I think we're jumping here, but you've described so many different things in the middle. Eventually this is an asset and, I don't know, here's a table of data that can do a lot of that stuff. I could go sell that, and people are doing that. There's stuff like data marketplace and stuff like that. But doesn't mean that all your data, that's the only way to show ROI here.
Tim Gasper: Not the only way to prove value from the data and turn that liability into an asset. That's not the only way.
Juan Sequeda: No.
Jane Urban: There's a lot of different pathways, yeah.
Juan Sequeda: There's so much, but I think we're just starting to go scratch the surface on this. And this conversation has just opened my mind. It's like, " Oh wow, we've got to start thinking about coming..." Again, I think the definition of the asset with the product as a solution, you start thinking about the solution. So you're selling that solution, which, by the way, uses a lot of the data as a product in there.
Tim Gasper: Yeah.
Jane Urban: Yeah. When I think you look at some of the really tech companies, it's a nice flywheel effect, where they're not only selling a product that has data used to create it, but it itself is collecting more data, which then helps them figure out what the next product should be. And so you end up actually using the data product to make more data products. So we're not quite there yet in healthcare, I don't think, but certainly in some places that's well on its way to becoming a thing.
Tim Gasper: Yeah, exactly. Well, just before we get to our next question, I want to say that this episode was brought to you by data.world, the data catalog for the data mesh. It's a whole new paradigm for data empowerment, learn more at data.world. And the next question that I want to ask you, Jane, is around, the pharma space is not necessarily known for being super fast and super agile. It has a process and that process can take a long time, and all aspects of the business get pulled into that. So how do you deal with trying to be agile in that environment, and how do you bring a data product or data value to fruition fast? Is there a culture clash that you have to deal with?
Jane Urban: Yeah, one of the things I joke about is, if you have a bullet train of innovative companies and you've got Google and Amazon and Apple and these companies at the front of the train, pharma's not even on the train. We're in one of those little push cart things that you're pumping up and down, trying to keep up with the back end of the train. We are just not the innovative. So I will say, though, I just saw chart the other day. I think it was...
Tim Gasper: But with millions of dollars in that thing.
Jane Urban: Right. But I think it was Gartner, the one maybe glimmer hope for us, which is a sad one, is that one of the places that's actually behind pharma is the automotive industry because they've really been resistant to using data. You think about Tesla as an outlier, but for the most part the automotive, it's all decentralized and you have all these little franchise auto companies that sell you your car. That model is really tough from a data collection perspective. And I still get emails from many dealers ago trying to see if I want to buy a car. So I think there's hope for us. We're not the absolute slowest to grow, although if you get somebody on here who's in the automotive industry and they come after me, I apologize in advance. But no, I think where healthcare is right now, it's definitely a lot of talk. I think there's a lot of interest, there's a lot of disruption happening. Certainly Takeda is no exception here. We're trying to build and get rid of the IT notion and move more toward using data and digital and technology together to create value. The talking points are much closer to where a lot of companies have been for years, so that's a good sign. But there is a reality of, the products that we make, the drugs that we make are decades in the making. They're not fast- moving things. It is really unprecedented when you think about the way that the COVID vaccines were accelerated through the regulatory process. However, people forget that Moderna, Pfizer, those guys have been making that kind of vaccine platform for a decade. They didn't just turn it March of 2020.
Tim Gasper: It wasn't start from scratch.
Jane Urban: No. So I think even that, as impressive as the regulatory process ended up being to get the approvals, it still was many years prior to the actual approval process that allowed for them to move as quickly as they did. So yeah, I think that's the context in which we operate, is that we're releasing new products in years, not months or weeks or even days. So that has a bit of a backdrop to how we think about the time scale of what we're doing. However, I think we have opportunity to have iterative smaller impact faster, because the regulatory pieces for the medical device space are a little different from, say, the ethical molecule space. There's other ways we can help patients without it being regulatorily reviewed and approved. Certainly things like these different algorithms that can help patients to better understand what their disease looks like and how it's progressing. We also, I think, have a really cool opportunity when we partner with different patient advocacy groups and help them better understand their patient populations. So there's other ways that we can help using data faster than just developing a net new molecule to solve a medical problem. And that's, I think, where we want to play in terms of how we get better impact, better traction, better value with the data we have. And that can be done in more of a weeks to month time scale rather than years. But moving from waterfall types of development projects to agile is slow. And certainly I think the hardest part is, if it's not done with a critical mass of people, it's really tough for those who are trying to do it. So if you have two or three people who are trying to work in an agile way and they have their standups and their ceremonies and they're doing their thing, and then they're asked to do the rest of their job in the not agile way, they struggle. And so we see that happening with people who try to bring agile to a team and then the rest of the organization is still not there yet. It's a tough haul. And so there's no surprise that a lot of the data and technology folks turn over quickly. That's been the pattern. Chief data officers don't last very long. And some of that is, when you're trying to embed in a company that's never done anything in that way, if it's not a software company, for example, then you do have that, I think, uphill battle. The middle ground for me, though, has been starting smaller and trying to solve smaller problems first, and then showing that value and showing that you can fix something and make something easier for someone and having that quick win, I guess classic consultant speak. But if you can do some of those things and show people that their data can be useful to them in a way that they hadn't thought of before, you get more and more leeway to start solving bigger and bigger problems and then you can move things forward. But I think unfortunately a lot of people who join healthcare from outside of healthcare are very frustrated at first. It's like you land in molasses and you're trying to run and you just can't quite make as much progress as you'd like to make, compared to other spaces where the turnaround and product development is much faster.
Tim Gasper: Sounds like you have to think about and plan your approach, pick your battles, and really find out where the quick wins or the easier wins are possible, and save the longer- term battles for the longer term because it might be a while.
Jane Urban: Yeah. The other thing, I think, is you have advocates and people who are excited, I tend to be very people oriented. So are there people who are geeky in their own right and they like this stuff and they're willing to dialogue with you and engage with you? Because those are the folks you can start with to see if there's something you can do to help them, and then get them excited and then they can bring more people to the party. And that doesn't always necessarily mean it's the most impactful or the highest value right out the gate, but it's about getting something to work and to stick and to have people willing to go along on that journey with you. Because to me, the behaviors of the people tend to be the thing that slow down technological innovation much more so than the technology. Writing the code is not usually the problem. It's usually people being resistant to change, not wanting new process to come into their world, or maybe just afraid because they don't know and they don't like to be in a place where they're uncomfortable and they don't know. So it's really orienting toward, how are the people feeling who are going to have to deal with this change, maybe more so than the tech and the platform. I'm probably unusual that way'cause I'm not a tech person, I'm a people person when it comes to these things.
Juan Sequeda: That's what is missing more in the data world, is that it's not just about the tech, it's about the people and the process and what you're exactly saying. One of our buddies here, Stuart Kerber, I love his saying, is you all find those crazy bunch, those astronauts, right?
Jane Urban: Yes.
Juan Sequeda: You think back in the 1950s people were like, " Yes, I want to go to the moon"? No, you're crazy to want to go to the moon. You want to go find those crazy people who say, " Yes, I want to go to the moon." And those are the folks that are going to be your allies, are going to be the evangelists who are going to be supporting and cheerleading, and you get that trust with them.
Tim Gasper: And help you break the mold.
Juan Sequeda: Yeah.
Jane Urban: Right, exactly.
Juan Sequeda: From a leadership and executive in the pharma space, how is this evolving? Because you can't always just be the slow little, in the back, right?
Jane Urban: Yeah, no. I think this is probably true across the board, this is not unique to pharma, that a lot of the executive layer is starting to really try to figure this out and embrace it. And we're no exception. I think our CEO, Christophe Weber, has been very vocal. He was actually at JP Morgan and mentioned data, digital, and technology as one of his priorities and becoming a digital biopharmaceutical company as one of his priorities. So clearly for Takeda there's this great... And that's a boost for us, because you have a voice from the top saying this is important and we want to invest in this and we want to make it happen. I do think, though, that there's a natural fear of the unknown or fear of looking like you don't know what you're talking about that we have to navigate. And so a big part of managing our executive team is to help them have that safe space to ask those" dumb questions" so that we can get them comfortable. So I think early days with some of the work I did in the US, it was about just getting a whiteboard and drawing a picture of how this stuff works and letting all those questions come out and why and how and all that, so that you can get people feeling like... They're dangerous, they know enough to be able to speak to infrastructure with data. And even just the idea of batch versus API, you can explain that pretty simply enough. And then people are like, " Oh yeah, yeah, we should run it in API." So you help people feel good. And the metaphors also too, but I think sometimes for people they want to know, " Unpack this jargon for me so I understand it a little better, so that I can feel comfortable when the techie people come and talk at me so I don't feel lost." I think that's part of the journey we're going on across the board, because there's a whole generation of folks who are not digital native who are still trying to lead, and then the digital native generation's coming in and it's a clash. It's a little bit of a tension between folks for whom this is super intuitive and they know exactly what to do without any instruction, to people who are struggling a bit with how to use those different new technology. Look at the way we all had to figure out Zoom and Teams and Slack and all these things to be able to communicate with each other during the pandemic. For some folks that was a really tough transition and they struggled. And so that's the same group that's going to struggle with moving toward a digital future, whatever that looks like.
Juan Sequeda: And one of the things, in this growth you were in this position, you were starting building the data team in the US and going globally. I would love for you to, time flies, we've got to wrap up soon here, is, share us your lessons learned, the good, bad, and the ugly of having these teams starting small and growing to a global level. Because people are like, "I have a great team," but yeah, you have a certain handful of people. No, this is a different scale. You're going through amazing scale of growth of data globally. Please share us the good, the bad, and the ugly.
Jane Urban: I look back, and some things are, like I said, better to be lucky than good. So I will say some context around the first build that I did back in 2016, 2017 was for a drug called Entyvio, which has become a blockbuster, meaning it has more than$ 1 billion in revenue. It's a huge, huge asset for us as a drug. And at the time it was growing incredibly fast, but it was still very small and there was a lot of infrastructure to build out. So some of this would have happened despite me. So I don't want to make it seem like it's all on me that I grew these teams up because of the nature of the business. That said, I do think starting with the problems, the biggest problems, the hardest problems, especially the hardest ones, really understanding what those are and starting to frame up, " What's it going to take to solve those hard, hard problems and how long do we think it'll take?" In some cases it might be a year or more to really wrangle it to the ground. And so one of the things I did early on was to get a scale and a scope of what all is going wrong and then look for a mix. Honestly the extremes was probably the best things to tackle. The things are going to take the absolute longest, that are going to be the hardest, you've got to start on those because that's going to take the longest time. And so if you don't get started, you'll never get it moving. And then maybe there's a few things that you can do with the remaining capacity you have. Let's say you have half your time spent on that big, hairy, audacious program that you have to do. The other half is about things that are quicker so that you can start to showcase that, " This is going to work, trust me, trust this process, trust this team." And then ideally what you're doing then is creating demand for the services you can render so people want more resources to go your way. So that's one thing I think I definitely learned by just having that mix. So you can't go all in on the long- term stuff and not show anything, and if you only do quick things and you never start the long things, a year in you've solved a bunch of little problems, but there's nothing meaningful there and people start to wonder, " You've been here all year and you haven't solved this big problem at all. What's going to happen?" So if you can do that mix, I think that's one thing to think about. But you have to know the list to start with the list, is, what are all the things? Does that help? I don't know if that ...
Tim Gasper: Yeah, that does. So my background is in product management, and this actually reminds me of good product management, where if only you focus on the big rocks and the big strategic things, then you don't see progress until six months from now or 12 months from now and things like that. Yes, you can prototypes and MVPs and things like that, but it's bigger, it's more ambitious. But then there's the small wins that you can get along the way that can have a big impact. They're visible, you can evangelize them, and there's a smart mix that you have to do in order to really show the value in an ongoing way that's optimal.
Jane Urban: Yeah, and it always depends a little bit on the situation. But I think if you can do a little of that big tentpole project that's going to raise everybody's spirits and hope in the long run, and then a few littler things that make a difference in the short run, even if it is just helping answer a really specific question that someone has, I find that sometimes can make this really cool moment happen for someone. Then they're like, " I like you. We can trust you." It can be something small, but that can help, I think, to get people to... Because they're buying, essentially, you in the beginning until you have anything to show for yourself. So that's a big, I think, takeaway, is just making sure you're thinking about it in that context because that's where you are at that point in the early...
Tim Gasper: The honeymoon period only lasts so long.
Jane Urban: Right, so you've got to come up with something. Exactly,
Tim Gasper: Yeah. No, that's cool.
Jane Urban: I'm still kind of in it'cause I'm three months into this role, so I'm like, " What else can I come up with?"
Tim Gasper: Oh, nice, you're like, "Okay, ... here." No, that's funny. Being a data leader is complicated. It's got things going on there.
Jane Urban: Well, it's the chaos, right? I think that's what it is. You're leading something that's naturally... But one of the things I always reassure my teams is, we have so much job security, this is not going away. So don't stress, you're always going to have more problems to solve. You'll never run out, so don't worry.
Tim Gasper: You all are important, and data really drives value, and especially when we can articulate that value.
Jane Urban: Totally, totally.
Juan Sequeda: Well, this has been a fantastic conversation. In our takeaways I'm going to highlight a couple of the big nuggets that we've had here, but let's move on to our lighting round, which is presented by data.world. So we've got four questions, yes or no answers or you can give some context. I'll kick it off first. So is an approach and focus to developing data assets more important than developing data products?
Jane Urban: Man, that's a tough one. In my mind, the way my framework works, you have to have assets to be able to create the products. So yes, I think you have to start with assets and make sure you have what you need, the raw ingredients, in order to create the dishes. Yeah.
Juan Sequeda: I like how the assets are these raw ingredients and you're going to go build a bunch of stuff with it. And I think this is how we can start framing it. The products need to be tied to some particular business outcome that at the end is either going to save money or make money, one of those two things.
Jane Urban: One of those two things, yes, totally.
Tim Gasper: I think that's interesting advice. There's more to unpack there in the future, because I think people are so excited about data products and they're just like, "How do I jump to data products?"
Jane Urban: Yeah. And I worry that it's overused, though. I think it's almost like people are using it as a crutch for whatever they're doing. " Oh, it's a product so it's going to be better'cause it's a product." And it's like, you're still just using software or something. It's just not as different as you thought it was.
Juan Sequeda: I think we can spend multiple episodes just honest, no BS, give me your definition of a data product and we'll be all over the place.
Jane Urban: Oh, yeah.
Tim Gasper: We did a sequence of just asking people one- minute rant on data mesh. We could always do a similar montage on data product.
Juan Sequeda: That's actually probably a good idea.
Jane Urban: Data mesh is similar, right? People say, " Well, isn't it just data governance" It's something else, right? I think data product can just be ...
Tim Gasper: The answer is, " No, no, no, no, you don't understand it."
Jane Urban: I didn't mean to step on that.
Juan Sequeda: We need all these blog posts and all these things, right?
Jane Urban: Yes.
Juan Sequeda: All right, you go, next question.
Tim Gasper: All right, all right, next question. All right. So is it the responsibility of a data leader to prove their data team's value?
Jane Urban: Yeah, I think so. I think that's part of the role as the leader. Obviously the team has to do things too, but I think the leader's the one who's going to help be that mouthpiece. That's why I'm talking about, I'm the cheering squad for my team. And the storyteller, which is where the value becomes clear to the end user. So yeah, I think that's a part of the role, and it's necessary because often the folks who are really working in the weeds on the details and making things happen, they don't want anything to do with talking about what they're doing. And so they would much prefer somebody else do that value proposition and story. So yeah, I think so, I think that's a part of it. And that's a different skill, which is why it's really tricky for folks when they've been so good at doing data science or data engineering or whatever technical thing to transition into leading a team. Because it's a totally different thing they need to do to be successful in that role. So that probably speaks to why sometimes new data leaders struggle, because they need to do something very different. They're not a super programmer, they're a storyteller, which is a different skill.
Tim Gasper: That's a really good observation.
Juan Sequeda: Also a good conversation I've been having and just reading a lot about data leaders, should they have a technical background or maybe they shouldn't have a technical background? Or that's not their primary background.
Jane Urban: Yeah, that's another one I think that's an interesting, I don't think there's a right or wrong there. But in my mind I like that I have some experience doing some of the tedious things because it gives me that context. So having programmed a little bit early days in SaaS and SQL and using data to answer questions myself I think gives me some more confidence that what I'm asking somebody to do is either awesome or really hard or is going to be terrible. You have a sense of what you're signing someone else up for and you have that credibility.
Tim Gasper: I like your food analogy when you said that the chefs made the best farmers.
Jane Urban: Yeah,'cause do, I think you need to have some chef experience, some in the kitchen, really in the trench kind of experience to be really... But you don't need to. I've seen some really great data leaders who are much more about the storytelling and, " You tell me what I need to know and then I'll make it sing," kind of. " I'll tell that story."
Juan Sequeda: It could be an interesting episode. Let's have one.
Jane Urban: I think so too.
Juan Sequeda: About just technical background and non- technical background. All right.
Tim Gasper: This reminds me of an episode that we had a while ago, a long time ago, of different types of CDOs. They have different styles.
Juan Sequeda: Yeah, we had that with Muhammad Asser, who was about, " Oh, are you the entrepreneur one? Are you the one about protection"? All that stuff. I think that that's a good one.
Tim Gasper: Yeah.
Jane Urban: That's a really good one.
Juan Sequeda: Next question is, is the people aspect of data the biggest missing piece?
Jane Urban: I think so. That's an easy yes for me.
Juan Sequeda: I was hoping you would say yes, because that's the one we need to emphasize most.
Jane Urban: Exactly. It would be weird if I was like, " No, people don't really matter that much."
Juan Sequeda: Actually, no, I think both.
Jane Urban: I feel like it's one of the things I have found myself having to remind people the most. And it comes with a phrase that I often say to our really techie folks, which is, you have to finish your homework before you go and play video games. And what I mean by that is, you have to fix the things that people... The important basic stuff that people really want to get done is going to have to get out of the way before anyone will be interested in your video game, cool technical thing that you want to do. And that's really hard when people, " But this video game though, it's so cool." And you're like, " I love it, I get it. I actually need finance data," or whatever.
Juan Sequeda: Cool compared to useful.
Jane Urban: Yeah. And sometimes they come together, but sometimes they don't. And so you just have to make sure that there's enough of that emphasis. And I think that's a bit of the struggle that really people who are very tech... I love technology too, but I think if you let that take over, you run the risk of a lot of cool stuff, but it's not tied to the goals of the business or the problems that are causing problems. And then how do you justify it? Eventually someone's going to catch up and be like, " Why do we have all of these Oculuses? What are we doing?"
Tim Gasper: Yeah, you can't have a healthy body if you're only eating candy.
Jane Urban: Exactly. It's why you can't have dessert all the time. Same idea. Yes, totally.
Juan Sequeda: All right, last question to you.
Tim Gasper: All right, last question. This one's actually a little more just industry specific. So is pharma industry experience, or let me be more specific, is pharma industry expertise critical to pharma data work?
Jane Urban: That's a good one. I guess I would say, if I had to pick yes/ no, I would say no.
Tim Gasper: Okay.
Jane Urban: I think you can teach people a lot. I think it's important that people get that context, and we've had to do a lot of that work with giving people some kind of onboarding of what is a pharmaceutical company and some of the terminology, of course all the acronyms, all that kind of stuff. But my hunch is that's the same any time you cross industries. And consultants do that all the time. If you look at the big consulting firms, they oscillate across industries and I think they can figure it out. So I think you can teach a lot of this. I think that's kind of controversial because I think there is a bit of, " How can this person lead if they haven't run a pharmaceutical team before?" kind of stuff. But I've seen people coming in from very different spaces and doing quite well in the data world, because the data part is really a skill that is important. And knowing best practice and how to document your code or how to put together your different kinds of catalogs like you guys manage, it's helpful if you have a general, there is a righter way to do things and a wronger way. There's not one right way, but I think it's helpful if you have a background. So we've hired some folks from Amazon, we've had people come from agriculture, people from other industries that are not at all healthcare specific, and they've done quite well. So I think you can teach that part of it if that's what you're looking for. And you can get people who have done some pretty innovative things. Again, with the whole train at the back end thing, if you can find some people from an Amazon or Google or Apple or something that come, we just got somebody from Meta, and that's been really interesting to hear their war stories of being in Meta. So there is something there too, I think, for us that benefits us if we bring some outside in thinking for sure.
Tim Gasper: So yeah, the outside in thinking. And if you had to choose between somebody who's great at data and not so much at pharma versus great at pharma, not so much at data, when you are working with the former, you can teach proficiency in the pharma.
Jane Urban: Right. No, I will say I've seen people make a pivot in their career and I've had people come to me and say, " I love this data stuff. It's something I'm really passionate about. I was a sales rep or I was in a totally different department at a pharma company and I want to make ..." And it can be done. So it's not that you couldn't do it, but I think that's a different sort of journey that you're going on and you probably won't immediately jump in and start writing code. Obviously that's literally having to learn another language. So I think you can do both, but I find myself more biased toward actually looking for the data skills over the industry experience.
Juan Sequeda: Well, we have gone through so much and hey, it's TTT. Tim, take us away with takeaways, kick us off.
Tim Gasper: Time for takeaways.
Jane Urban: Time for takeaways.
Tim Gasper: Amazing conversation. I think we started off especially around this idea of data assets versus data products and the importance of treating your data or thinking of your data as an asset, and that being pretty fundamental on the journey towards data products. And I think that's an interesting perspective, because people tend to use these words in all sorts of different ways. Feel like it makes a lot of sense to me to think of this as a funnel. I've really latched onto your use of the word chaos. There's like the data chaos that's at the top of the funnel.
Jane Urban: Yes,
Tim Gasper: And then the data products, and then there's data value all along the way. And I think that's a really great way to think of it. And you mentioned this idea of, you want to care for your data assets, you want to really think about the data value and think of it almost like a balance sheet. Maybe not literally a balance sheet, because a lot of companies aren't necessarily literally putting their data on as liabilities on the balance sheet. But if you think that way, then that really gets you to think about it in the right way. Assets are larger than a product. And in general, you also mentioned some of the terminology in pharma, that in pharma a product is usually a drug. So that actually adds some interesting dynamics about the words that we choose and how we talk about these different things within data. And when you talked about what is data value, you mentioned it's better data, better decisions, more accurate decisions, and then higher ROI that's coming from those data assets and data products that can result in being faster, being more efficient, saving resources. Both the data side and the digital side can have a big impact on this, you mentioned. And you really have to focus on the problems, the problems that you're solving, and that really connects to the data value. So talk to the executives, talk to the key people in the organization, ask them what keeps them up at night, what makes them worried. And use stories, use anecdotes to make it so that you can have a common language to talk about their data problems and talk about how data can solve their problems. And if it helps to talk about pipes and water, if it helps to talk about food metaphors like mirepoix and things like that, if it helps to talk about supply chains, use what helps you have a productive conversation. And these metaphors, I think you mentioned several of them, food being a favorite, as being really powerful and helpful to do that. And before I hand it to Juan, you also mentioned that data governance, data management, it's never done. You agree that, hey, it's a function, don't think of it in terms of projects, think of it more in terms of programs and processes. Amazing stuff. So much more, but Juan, what about you?
Juan Sequeda: So for me one of the top nuggets here was, collect the outcomes. So I always say, talk about data catalogs, about cataloging data, and this is more about cataloging data, it's about cataloging data and knowledge. And the knowledge also is, what are the decisions that are being made? What are the outcomes from those decisions? And you want to go basically trace it all back and saying, " Hey, generate these case studies, how we're actually improving." And then at the end you may find some correlations saying, " Hey, this outcome, it's correlated with this data work." You see some correlation. Then from a qualitative point of view, maybe you can actually point to, it was actually that causation right there. And once you do that, this is my interpretation, it's like you start generating that trust, you gain that accountability, and then actually you have the availability now to take some risks.
Jane Urban: It's almost like an extreme in lineage, Juan, I think that's what it's like. It's like you take lineage even further forward than just a report, but all the way to, what did you do with it? Yeah, yeah. I like that.
Juan Sequeda: But business lineage here, that was a big aha moment for me here. And we talk about, what are the connections between the data assets and the data product, data values? This other big great nugget is, it's not just about, " Oh, we're going to go sell the data." It is, no, we're generating these products which are these services that happen to be all filled with data. And at the end in this particular space, there's some other forcing function which is actually again forcing you, in your case it's an insurance that you need to go pay for this, they're going to go pay for it, you need to put a price on that stuff. And I think there's this trend that it doesn't have to be always just the regulatory point of view. And I think when talking about the pharma industry itself is, yeah, pharma is slow, but hey, automotive is a little bit slower than that, so it might not be that bad. How to start is, you need a critical mass of people to go start small, show value, quick wins. Who are those early evangelists, those champions? I think that's something we constantly hear, but that's something we really need to focus on, and help others feel comfortable with that conversation about tech. On scaling teams, start with the problems, understand, make a list of them. Understand what are the hardest problems. And another great nugget for me was, have this entire spectrum of really hard problems and really easy problems and start at the extreme. You need to tackle the hard problems because you know those are going to take a while, but you also want to tackle the easy problems'cause you want to show some value quickly. If you only focus on easy problems, you're going to show quick wins but not show the big picture. If you only select the hard problems, it's going to take a while, they're not going to show value, people are going to be questioning what you're doing. And then finally, finish your homework before you can play video games. How did we do? Anything that we missed on takeaways?
Jane Urban: No, I thought that was fabulous. It's really made me sound kind of smart. Thanks.
Juan Sequeda: We're just repeating what you said here.
Tim Gasper: That was all you.
Jane Urban: No, I appreciate it, that's really cool.
Juan Sequeda: So throw it back to you. Three questions. What's your advice about data, about life, whatever? Open question. Second, who should we invite next? And third, what are the resources that you follow? People, blogs, conferences, books, so forth.
Jane Urban: Sure. Okay. So I think my advice probably is not surprising, it's about starting with the people even though you're in a data space. And one thing I think is really important as a leader in data is, I call it, especially with my thing here, you set the weather as a leader. So you decide the energy level, the optimism, the way that everyone's going to going to feel. And so keep in mind how you show up, the energy you bring, the way you engage is going to really be part of how you succeed or fail. I think sometimes people forget that their own energy as a leader is what starts to generate momentum for the team. So I'm often relentlessly positive even when I'm a little unsure we're going to figure it out. I'm like, " We're doing it, this is happening," and it tends to work out that way. So that's one thing I would say I've found to be very helpful over the years. In terms of the next person I think you should bring on, I'm a huge, huge fan of Israel Abraham. He's over at Mass Mutual. I interviewed him actually recently as part of a data program I was in Massachusetts doing. And he has built this incredible juggernaut of a data infrastructure at Mass Mutual and he's just a lovely, wonderful human being as well. So I would recommend him highly as another person to talk to.
Tim Gasper: Awesome.
Jane Urban: And then in terms of things I follow and read, from leadership perspective, Simon Sinek's one of my favorites. I tend to repeat things he says on a regular basis. I really have to plug in the Data Storytellers podcast as another podcast. I was actually even listening to that one and I was part of that one, that notion of storytelling. I think what's nice about it is it's different stories that people have told to get things to happen in their organization.
Juan Sequeda: That's how I found you.
Jane Urban: That's how you found me, that's right.
Juan Sequeda: I'm like, " Wait, I want to meet Jane."
Jane Urban: Yeah, that one I would say, every time I listen to one of those you get this really interesting kind of vignette. It's usually about 15, 20- minute story that someone's telling about what they've done with data. So that's a really nice one. And then I think in terms of conferences or things to read, I'm a big fan of, actually Evanta has a data conference that they do every year that I really like. The format is smaller, you get to really talk to each other more.
Juan Sequeda: I like that. As a vendor we can also go to these things, but it's not salesy at all.
Jane Urban: No.
Juan Sequeda: It's almost like this podcast, it's honest.
Jane Urban: No BS honest thing going on. I think it's really nice, and I love that they do have those round table breakouts so you actually do talk to each other rather than just somebody talking at you. So yeah, those are all good resources for data.
Juan Sequeda: Well, this has been a fantastic conversation. Before we wrap up and say thank you, a quick couple of things. Next week we have Anna Abramova from SQL DBM. We talk about data modeling, which is one of the other big topics right now. And second, in two weeks, or 10 days actually, I'm going to be at Data Day Texas here in Austin. It's on January 28th, on Saturday. Get a 20% discount, just put in my name, Juan Sequeda, to get a 20% discount. It has an amazing roster of guests all packed in one day. And even just a bunch of former guests from Catalog and Cocktail. So Shamak Dagani is going to be there, Joe Rice, Bill Inman, who was our guest last week, Chad Sanderson, Yanz Osmond, Dave McComb, that's going to be a phenomenal conference. I'm going to be giving a talk on, " Show Me the Money," talk about data ROI. That's my talk over there. So just use my name, Juan Sequeda, to get 20% discount for that. And with that, Jane, thank you. Thank you so much. As always, thanks to data.world, who lets us do this, the enterprise data catalog. Thanks for supporting us. Jane, thank you, thank you, thank you. You have a good one.
Jane Urban: Cheers.
Juan Sequeda: Cheers.
Tim Gasper: Cheers.
Speaker 1: This is Catalog and Cocktails. A special thanks to data.world for supporting the show, Karli Burghoff for producing, John Moyens and Brian Jacob for the show music. And thank you to the entire Catalog and Cocktails fan base. Don't forget to subscribe, rate, and review wherever you listen to your podcasts.