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The Future of Dashboards, Data Apps, and AI with Plotly’s Chris Parmer
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In this week’s episode, I talk with Chris Parmer, co-founder of Plotly, about how the company is integrating AI into the next generation of data visualization and analytics tools. Chris walks me through the thinking behind Plotly Studio, their new AI-native environment where natural language prompts generate real, auditable code for charts, dashboards, and data apps. We discuss how this approach reduces bottlenecks for data teams, empowers non-technical users, and reshapes the role of the data visualization expert. We also dive into the limits of public dashboards, the rise of generative interfaces, and what a future of AI-driven exploratory analysis might look like. It’s a fascinating look at where data tools are heading and how analysts can stay ahead.
Keywords: Plotly, Plotly Studio, data visualization, AI tools, generative AI, dashboards, data apps, Python, code generation, data workflows, data analysis, natural language interfaces, data science, analytics, enterprise data security, data storytelling, Jon Schwabish, Chris Parmer
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Transcript
Introduction and Upcoming Series
00:00:13
Speaker
Welcome back to the Policy Viz Podcast. I'm your host, John Schwabisch. We are back. I've been a little bit sporadic with episodes after a big launch in September and October, but the federal government shut down here in the US made things a little bit wild for me. So I'm back. I've got a whole bunch of episodes to fill out the rest of this calendar year.
Interview with Chris Parmer, CEO of Plotly
00:00:34
Speaker
And we're going to start this last sprint with my interview with Chris Parmer, CEO of Plotly. ah You may know Plotly is one of the data visualization tools, sits on top of a bunch of other sorts of tools, including Python and R. And what Chris and I focus on in our interview, in our discussion, which I should say, we i did interview Chris several years ago on the show, so it's interesting to see the evolution of the tool.
00:01:00
Speaker
But what we focus on a lot in our conversation is the addition of AI or the inclusion, the implementation of AI into plot least new product plotly studio.
AI Integration in Plotly Studio
00:01:11
Speaker
And what's really interesting about it. And this is where I think AI is going to embed itself in data visualization tools is you load your tool to plotly studio, you query the AI, the large language model, and then it generates the graph for you. Well, it writes the code and then generates the graph for you. And so I think you probably have a little bit of that hallucination problem that we all are familiar with because Seemingly it's embedded within a box It gives you the code so that you can go in and change it and learn from it um And so it's all sort of right there in the package And so we spent a lot of time talking about that we talked about the evolution of plotly as a company And the staff over the last several years and just generally talk about what chris sees in the field of data visualization tools going forward
00:01:53
Speaker
So I'm back. The show's back after a couple weeks off. ah Don't forget, there's lots of episodes to go back to over the last few months. The last two episodes really focusing on mapping, ah tactile, physical maps, and a great new book, ah both out of Esri Press.
Target Industries and Accessibility
00:02:09
Speaker
I've spoken to BLS commissioners. I've spoken to other economists to talk about threats to the federal data system. Lots of stuff for you to listen to and take a look at when it comes to communicating your data as best and effectively as possible.
00:02:23
Speaker
All right, that's enough for me. Let's get over to the interview. Here's my talk with Chris Palmer, CEO of Plotly.
00:02:32
Speaker
Hey, Chris. Wow. Good to see you. It's been several years, I think, since we last talked. How are you? Great. Good to be back. Thanks for having me. Yeah. Yeah. yeah It's always good to have folks back on the show. It means ah either they're bored or I'm still around or a little bit of both. So Good to see you. So, I mean, let's get right into it. Like where do things stand at Plotly? Like, where are you guys? How many people are working there?
AI-Driven Code Generation
00:02:55
Speaker
Like, give us the lowdown on where things stand.
00:02:58
Speaker
Yeah, totally. So we ah we are over 100 people now. We're distributed ah ah little bit around the world. We've got some folks in Europe now, but mostly us s and Canada.
00:03:11
Speaker
um We still have a ah huge open source ah community behind our visualization technologies and our data application technologies. um and But most recently, we've gone full in on ai and we just based a new product called Plotly Studio, which is our AI native product for creating data visualization, doing data analytics, creating dashboards and data applications. And um and it builds on a lot of the technology that we've developed over the last 12 years of the company.
Natural Language and Visualization
00:03:45
Speaker
Okay, I want to spend most of our time talking about the the new tools, the new capabilities, the AI stuff. But let's just start at the beginning, though. For folks who are not familiar with Plotly, can you give them just like, you know, the quick little what it is and what they can do with it?
00:04:01
Speaker
Totally. Yeah. So we are a data visualization, data analytics company. um Our roots are in code-based data analytics and data visualization. So primarily most of our customers use programming languages like Python to do their data analytics. They primarily work in more technical industries like finance, ah policy, bioinformatics, energy, and data science.
00:04:30
Speaker
And and Primarily, they use our technologies when traditional BI tools like Tableau or Power BI don't cut it because they need to do more advanced analytics or more custom data visualization, um or they're primarily just doing their data analytics and code for a variety of other reasons.
Customization and Data Privacy
00:04:50
Speaker
um And now as the company evolves, we're kind of taking that same code-based foundation and architecture, but bringing it into the AI era where now the interface isn't necessarily just code, but natural language, which means that all these powerful and sophisticated capabilities that have been available for for over a decade that we've developed are now available to a much wider audience, which is exciting.
00:05:14
Speaker
like Okay, so ah AI, obviously that's all anyone's talking about these days. I am intrigued about how you all are thinking about and incorporating AI into the tool set.
00:05:28
Speaker
Is Plotly Studio right now, is that the kind of only place where AI is integrated or is that like the place where it's most being used? That's the main place and it's totally built sort of from this AI first viewpoint. So we're not bolting it on.
00:05:46
Speaker
really kind of went back to the foundations and thought, okay, if we were to build an AI first data visualization dashboard product, what would that look like? um And our approach is really about you know architecturally, the AI is generating the code to do data analytics, to do data visualization.
00:06:07
Speaker
And then our product runs that code and then displays the results to the user. And that's an important distinction to know about because we aren't asking the AI to do data analytics directly.
00:06:20
Speaker
We're asking the AI to generate code And then that code will do the data analytics and we will run it and then we'll display the results. So it's important because a lot of the issues that you might see or think about with doing something very precise, like data analytics with something very imprecise and prone to hallucinations with like AI um don't really happen as much in this architecture because the AI is generating the code and that code is is precise.
User Adoption and Benefits
00:06:47
Speaker
Right. So I can ask, I can go into Potlis Studio and say, Hey, I want to make a bar chart of the, you know, I'm loaded in the data. Here's the, I want to make this bar chart even more detailed, I assume than that have a conversation with it, you know, build this bar chart for me.
00:07:00
Speaker
It generates a bar chart and then it gives me the snippet of the code so that I can go in as these are sort of still control it. That's right. Yeah. So you have the full, you know verifiability, auditability.
00:07:11
Speaker
your ability to edit the code as well. You know, it's it's not necessarily like sort of a black box that you might have if you are using an AI for doing like image generation or something like that.
00:07:24
Speaker
There's, you know a lot that happens in the model that you might not have control over. It's a different approach. So if I bring, say, a big data set, and obviously I'm asking because I haven't tried it yet, so this is perfect. I get like the the tutorial right here.
00:07:39
Speaker
So if I bring in my big data set, Can I use Plotly Studio to essentially query or interrogate the data to determine what I eventually want to plot?
00:07:52
Speaker
Yeah. Yeah. we We're primarily focused on the output. So it works. you kind of have an idea in your mind's eye of the visualization that you want to create, you can ask very open-ended questions like, you know show me the top products over time you know by revenue.
00:08:10
Speaker
And you know in that statement, I'm not saying create a bar chart where the x-axis is this and the y-axis is this and aggregate it by that. I'm just ah giving an open-ended statement or open-ended question.
00:08:22
Speaker
And ah and we'll we'll make several attempts at generating code to hopefully answer that question. um But you know there can still be a gap between the type of question that you can ask and the type of answer that's even possible in the data.
00:08:36
Speaker
you know It's an important thing in our product philosophy is we're always going to be showing you the answer in a table or in a graph and something that is very rigorous without having the ai sort of hallucinate an answer uh you know whether it's that answer is in the data or not right and so if i want to create my bar chart in the style of don't know the financial times or the economist have like that sort of branded look yeah is like i can do that in chat gpt but i don't
00:09:07
Speaker
Personally, I don't really trust it right now. But like could I do that in Plotly students and say, hey, i want to make it here's my bar chart, but I want to style it like The Economist. Totally. Yeah. you know and It's going to vary on how much of like that brand style is in the model.
00:09:22
Speaker
Very common things like The Economist, or we had a user recently be like, make my app look like the matrix. you know It's all like that oh yeah ah green monospace fonts. and It does a pretty amazing job at it. yeah But the cool thing about it too is you can also be as precise as you want. um If you are like use these exact hex codes, it's going to use those exact hex
Dashboard Applications and Interactivity
00:09:44
Speaker
codes. It's not going to and come up with a different one. so And that's kind of actually one of the amazing things about working with AI.
00:09:51
Speaker
is that you can be as precise as you want. You can start with something open-ended, see what it shows you, the chart that it creates, look at it, reflect on it, and then become more and more and more precise over time until at the end, the sort of spec that you write to define your chart might be really, really detailed and control every single thing that you want.
00:10:12
Speaker
But you don't need to start there. And I think that's a really thing that feels really productive about it. You can start with something very loosely defined and then and then iterate after. Yeah, because it it is lowering the bar for folks who need to make a chart or a dashboard, but don't care necessarily about the branded colors that they have to use or the font that they have to use. They just know they have to use it.
00:10:33
Speaker
Exactly. Yeah. What happens with folks? And this is a question I keep getting asked about a lots of different tools that are, that are open source or cloud-based, but what happens with folks who have data that are secure, private administrative you know that they're you know that can't sort of go out in the world. like How have you tried to solve that challenge? Yeah. What we see among our customers is that almost all of the large customers now are running their own model in their own cloud infrastructure.
00:11:03
Speaker
So ah you know for us, our product works best with with Claude, Anthropics Claude model, and we run it ourselves in GCP and in an AWS. We've got it running multiple places.
00:11:17
Speaker
um So we don't even use Anthropics infrastructure directly. And so our customers will do the same thing. They'll run the model within their own infrastructure so that it doesn't leave the firewall. um And we see customers with with different...
00:11:31
Speaker
um In some cases using different models for different types of um analytics and data, depending on the privacy of it, like they'll use ah OpenAI or or Anthropics model for a certain level of data, but then other data that's maybe more sensitive they'll use llama or something like that but they're still all running within their organization and you know there's a lot of kind of interesting geopolitics around it some customers that are only you know only using non-western ones other ones that are only using western ones yeah if it's running within their own infrastructure like the data isn't leaking but there's some concern maybe about internal bias or something like that that's definitely way more on the like super high security high regulated side of the industry but those are those are things that some um folks are considering.
00:12:18
Speaker
Yeah. you know Our approach is just, well, we integrate. We've got an enterprise plan that if if that's if that's important to you, then that's configurable at within our enterprise plan.
00:12:28
Speaker
Right. So, so far, and I know it's only been out for a few weeks, but clearly you've been testing it for, I'm sure, a while. What are you, in early sort of stages now, what are you seeing as ah as the youth,
00:12:42
Speaker
either interesting or or just common use cases? Like, are people just kind of querying it to say, build me a bar chart, build me a line chart, let's try seven other things? Like, what are you sort of seeing as a use cases these days? Yeah, the big one is that in so many organizations,
00:12:57
Speaker
There's a pretty technical data scientist or data analyst that is just totally the bottleneck for the team or the organization. And we're seeing this adoption now where other, that that person's stakeholders can work a little bit more on their own.
00:13:15
Speaker
um just by working through natural language. So instead of sending the request ah you know by email, in you the stakeholders are already working in natural language. They have been forever, right? They send emails asking for the chart that they want.
00:13:30
Speaker
They can just do so directly in the tool. and that's And that's great. and and And we see a bunch of companies that have client-facing or customer-facing requirements and their customers are always wanting the charts and the dashboards to be slightly different.
00:13:46
Speaker
um And they're instead asking those customers or clients to use Poly Studio instead and ask their own questions. And instead, now the role of some of our customers and even the more the data scientists and data analysts is more around the data preparation side of things.
00:14:02
Speaker
my and getting all of that set up so that then everybody else can be self-service in the analytics downstream of that, which is really exciting. i mean, there's tons of bottlenecks there. it's It's really great for people to be much more empowered to work on their own.
00:14:18
Speaker
Yeah. But what do you see when that person who is not the data person is doing these queries using the AI, but they're probably not then looking at the code that's generated. Is there still, have you seen like, they can build they can build now the chart that they want, but are they still going back to that data person to say like,
00:14:39
Speaker
Hey, I've done this. Can you check? Or is it like there's enough faith at this point that they're just like, I just kind of need this bar chart for internal work. it's Totally. yeah Yeah. There is now a lot of faith in the code itself.
00:14:52
Speaker
And I think what happens more is there's more interrogation around the data in a way that there always has been, though. I don't think the dynamic is really different.
00:15:04
Speaker
and And in a way, it's it's almost helped because the stakeholders that are looking at these these charts often are the domain experts of the data anyway.
00:15:15
Speaker
So it's easy for them to spot check things that obviously wrong, or even to create the follow-up visualizations or tables that they can use to spot check their own results. So you know you create a bar chart that's an aggregation of a bunch of other data.
00:15:31
Speaker
And the way that you verify that isn't necessarily just by looking at the code to see if the Panda's syntax is correct. But the way you want to do it is to then like drill in and look at each one of those bars and take a sample of the data and kind of do all this manual spot checking work, which is something that often the stakeholders are pretty good at because they're the experts in the data anyway.
00:15:52
Speaker
And so we see a lot more kind of more importance around building tooling, around that spot checking, auditing the data kind of thing. And I think the more people that are looking at the data, the better that is.
00:16:06
Speaker
Yeah, yeah, for sure. um You mentioned the the dashboarding world. I'm curious um where Plotly sort of sits. I noticed on the on the main page of the website, there's a big focus on dashboarding.
00:16:20
Speaker
I'm curious where Plotly Plotly is with dashboarding and also how it intersects with the new AI tools. the Yeah. So what we look at, you know, if you're just working in a chat-based interface today, like using ChatGPT, and you ask a question, you'll get an answer that's an answer at a point in time.
00:16:38
Speaker
Right. you If you upload a data set and you ask you know some for a chart or something like that, that chart's not going to change. That answer isn't going to change over time. And so there's still this importance, in my view, that you are creating these artifacts that have eight that can have a long lifespan and that will actually update when the data changes.
00:16:58
Speaker
So if you're working in chat today, you might get an answer, but then you know you don't want to have that same 50 back and forth you know chat tomorrow to get that same answer. Do that every single day, right? And so that's our view is that if as a side product of doing data analysis with AI, you get this long lived dashboard that updates in real time with your data that you can always go back to without needing to go back to AI and have the same conversation over and over.
00:17:28
Speaker
That's really good.
Storytelling vs. Data Exploration
00:17:29
Speaker
So that's our view. We knew kind of AI is more of this tool to be creating these dashboards and the dashboards have a long life. Consumers of the dashboards often well we'll want to look at the same thing in the same format every every day, and the consistency of that is really nice.
00:17:46
Speaker
Of course, a lot of the downsides of dashboards still exist of like, you might want to view it in a slightly different way. And I think now AI enables those end viewers to customize their own in a much easier way.
00:17:59
Speaker
So I'll admit, I don't have much experience using Plotly and dashboards, and I'm curious ah This is really two part question. So the first part of the question is, are people using the dashboarding capabilities in Plotly, sort of, you know, historically public facing or internal facing sort of dashboards?
00:18:16
Speaker
And secondly, in your experience, do people actually use the public facing dashboards? I have more and more hesitation that when I, you know, create sort of this like data exploration tool and put it on the web that nobody really uses it.
00:18:30
Speaker
Like internals, think a different world. You and I are working on a project together. we need to explore the data together in real time. That's a different, I think, it ask. But public facing, I just feel like we just put a bunch of stuff out there and nobody looks at it.
00:18:44
Speaker
So that's kind of two-parter there. Totally. Yeah, I totally agree. I think where we've seen the most the most value, the most uptick is when people are building these dashboards that are really used in operational settings, right? And and we've we've kind of come up with different terminology like We call them data apps sometimes instead of dashboards to signal this more like this is something that's used in your day-to-day operations, right? It's really tweaked for the viewers that are using it that need to reference this data every day, whether they're monitoring like an electric grid and they're out in the field looking at the data.
00:19:17
Speaker
it's These dashboards are used kind of as reference materials or they're used you know, monitor trends over time and things like that. Or they have, you know input and output so you can actually upload your own data or update the data behind it because you're an end user within this organization that's modifying.
00:19:35
Speaker
That's definitely what we see. And I totally agree with you on the public side. It's very tempting to put up this sort of generic chart builder and say, oh yeah, well, anybody can just look at whatever they want.
00:19:46
Speaker
Yeah. It's actually often not true. Yeah, that's right. Yeah. and And I think about this a lot now with AI because yeah we've built our fair number of chart editors over time.
00:19:59
Speaker
And they need to become so sophisticated to be able to answer the broad range of analytical questions that you might have. And so it is now so much easier just to create the bespoke visualization that you want immediately without going through this you know generic chart editing interface.
00:20:20
Speaker
All example I've been exploring a lot on the public side is I look at San Francisco's 311 data, which is like the complaints line. So if you have or somebody blocking your driveway, you call this number. And the data is public, and it's awesome.
00:20:33
Speaker
And we have a new mayor in the city who's been great. And I'm kind of curious, like, OK, can we look at the data to see before and after? Has you know response time to city got better or not? Has it gotten better in certain areas, neighborhoods, or not?
00:20:50
Speaker
And there's, ah of course, an Explore This Data interface on the website. It's really tricky to to come up with a definitive answer um these types of questions. You're doing complex comparisons. You're doing computations to look at the diffs of different, you know the time range between open and closed date. You're comparing certain things against each other. like There's a lot of analytics that happens behind the end simple visualization. And I think that's the piece where speaking out loud, like that's the piece that we've kind of missed when we create a generic chart editor is that there's just so much analytics required before you get to the chart.
00:21:28
Speaker
And it's not like big data analytics. It's not like data pipeline stuff. it's It's sort of data prep. Like we haven't, I think as an industry, we haven't had a great terminology around it. It's this lightweight data analytics, data prep stuff.
00:21:42
Speaker
um And when we've built just a chart editor in the past, it hasn't always included that. And I think that's that's what what I love about Studio today is that it's generating the code and all of that code that you might need to do to prep and shape and analyze the data before you just put it directly into a bar chart is all part of the core experience, right?
00:22:01
Speaker
Yeah. And this has been bouncing around my head now for the last, i don't know, 10 months, basically, that There's a lot of public dashboards out there. A lot of them people make for fun or to show that they have technical chops. All that's good.
00:22:16
Speaker
There are a lot that you go, you know, in my sector, the nonprofit social policy, public policy sector, you see a lot of dashboards, you know, here's the job data and you can go explore for your state, your county or whatever. And I just, I feel like if your target audience is the sort of like regular person,
00:22:37
Speaker
Yeah. Right. Not another analyst, not another, you know, a social scientist, not another data vis enthusiast. If you're trying to reach that, that dad coming home from work, I don't think they want to explore dashboards. I think they just want to know the answer like right away.
00:22:55
Speaker
And so I kind of feel like what often happens is like the dashboard part is at the top. And then maybe there's a description at the bottom where I'm starting to feel like It should be flipped.
00:23:07
Speaker
You should tell a story and then say, okay, so here's the main, here's the bottom line.
Future of AI in Dashboards
00:23:12
Speaker
Here's the headline. Here's like three examples. If you really want to go find your zip code or your area, like, you know, go in and and dive in. And I know that that experience is going to be context dependent, right? If you, if that is important to you to know X, Y, Z, then you're going to spend more time. But I just think we just pour all this stuff out. And if this is expectation that people are going to dive and explore the data, I just don't think they're doing it.
00:23:36
Speaker
Yeah, I agree. And I think it it comes down to sort of the role of data viz. Yeah. cases, it is like a, it's a lookup table. As you mentioned, you are very interested in in the certain, you know the house price, average house price in your zip code.
00:23:52
Speaker
And great, like a data exploration tool will give you that. Other times you're interested in the broad range of like what's happening to housing prices and then in, in the city, but you're not even just interested in that. You're like, why is that happening? Right now?
00:24:05
Speaker
Right. Included in the data itself. No, cause that's just the data, but I want the understanding. I want the story around it, which is, you know, I, and this is, I mean, all of this is purely just a feeling and anecdotal, but I also kind of feel like when I look at the major newspapers, I read Washington Post and New York times, the guardian.
00:24:24
Speaker
um you know, l LA times, bunch of others, the number of those sorts of exploratory interactive dashboards seems to be far and few between these days. Yeah. Where it's more focusing on the storytelling than on the big data pieces or the big interactive exploratory pieces. Cause I, my guess would be, they have discovered that like, you don't get a lot of bang for your buck for that.
00:24:47
Speaker
And, people go to the Washington post to learn the story, the understand the context or the content rather than like, here's this, you know, 700 clicks and filters to get to this one number. Totally.
00:25:02
Speaker
Yeah. And I think the amount of like, sort of design effort that goes into creating something that's highly consumable is also super unappreciated. Yeah.
00:25:13
Speaker
And so, and the way you present your data for a particular answer is, is often so bespoke if you want to do it really good job at it.
00:25:23
Speaker
Yeah. I mean, I, I heard, I heard a number the other day that the Johns Hopkins COVID ah tracker costs $13 million dollars to build.
00:25:34
Speaker
which one could argue, whoa, that's a lot of money. But also, like, how many millions of people went to that dashboard every day? But that, of course, is also a a hopefully unique situation where people want to know for their own health what's happening around them. They don't really...
00:25:55
Speaker
You know, what's happening in the other, in another part of the world doesn't necessarily impact them directly. They want to know if they can send their kids to school, if they can go to their grocery store safely. Right. I think that's kind of ah a unique case. Whereas generally we're just putting this stuff out there, which does lead me to a question about if you see the future of dashboarding.
00:26:18
Speaker
okay I'll put it this way. You've described a plotly studio as. ah query the tool to generate a chart for you. um Do you see a future either in plotly or just generally speaking where the end user queries the, the data, I guess a data, an interactive piece. So we've called a dashboard where they query the dashboard using natural language instead of having to click all the buttons, they just ask the question.
00:26:46
Speaker
Yeah. I think, I think that will is, will happen but i don't think it will be on the dashboard itself. I think it will just be interacting with the data itself.
00:26:57
Speaker
And instead of the dashboard being this sort of fixed you will be almost, it'll be a generative UI. It'll be a generative dashboard where you will be asking questions and getting those, getting a unique chart that maybe hasn't been viewed before or originally created by the original author before.
00:27:19
Speaker
um And so as an author ah or an expert in the data, your primary job is to prepare the data in a way and maybe put in some of your own context of important things about the data so that then the consumers and the viewers um can have their own exploratory experience where they can ask the questions directly. So in the case of my you know SF public data one, I don't want to go to ah dashboard and ask for ah question because the the charts that are presented to me might not
00:27:58
Speaker
might not have the necessary controls or be presented in a way that can answer my question. I do want to go in and say, Hey, how has the response time for three on one cases changed in October of this year versus October of last year. And I would love it if the answer showed me sort of proof in the form of a data, right? Like in form of graphs, I don't want to just see, yeah oh, it dropped 5%.
00:28:25
Speaker
I'm not going to trust that 5% number because that, you know, that could, is that a median? Is that a mean? Is that the 90th percentile? How is it? Right. So instead AI is then showing you a set of charts to say, hey this is before, and this is after come to your own conclusion, but it's turning
Empowering Users and Enhancing Productivity
00:28:43
Speaker
it's right. That, that the original author might not have known about because in a data set like that, there's hundreds or thousands of permutations and types of questions that you can ask. so And I think that's the really unique thing about this new era of data visualization is that you can start to ask any question that you want and see this almost generative data visualization.
00:29:04
Speaker
and And it's hard as an author to know the types of questions that your users will want to ask unless you work in a really tight organizational setting. right But the way you frame it's really interesting because it in lots of ways changes what the job is or the task is of the data viz developer becomes less about the data viz, but more about facilitating or analyzing the data and then facilitating the a set of questions that a user might want to ask.
00:29:34
Speaker
That's right. Yeah. And I think there's a lot to, you know, to, for that person to create the initial stories, to see the viewer, to know the types of things that they can ask about. And I think that's a, that's a really tricky thing about AI today. I've heard it called like the jagged frontier where AI is remarkably great at certain things.
00:29:55
Speaker
Yeah. Terrible at other things. And unless you're an expert and have a lot of experience using it, you really know what is what, right? And I think for a user are coming into a dashboard, they might not know the types of questions that can be asked about the data.
00:30:10
Speaker
Seeing a set of stories that were already created by the data viz expert um that show how to structure your questions and your analytical queries, but also show you the realm of possibilities of what you can interrogate is a really important part.
00:30:23
Speaker
But then you you leave it up to the end user to kind of choose their own adventure. You need to create a bazillion charts for each HND user themselves. Yeah. And also putting it into a a finite space, I would guess, reduces those hallucinations, right? Like, yes you're not going to get, you know, if I if i went into ChatGPT or Cloud right now and asked for 311 data, it could pull from the New York City database rather than the San Francisco database. But if my Plotly, Plot Studio dashboard is
00:30:57
Speaker
populated with San Francisco data, then the hallucinations should be minimized. Exactly. Yeah. And there's a tremendous ah amount of work to sort of create uh, set up the environment so that an end user is ready to go. and ask those questions and that's so that's a lot of stuff we're thinking about within our product is these different roles where one person is just setting up the data, just setting up the environment, putting in additional context in about the data sets so that the end users can come in without needing to do all that work themselves and have and and have a productive experience.
00:31:35
Speaker
So, yeah, you know I think the there's these different roles. And I think The other approach that we take is that we want users to be able to view the answer as a chart or as a table and not just have the AI hallucinate a story. If you go into ChatGPT today and you say, tell you know have 311 cases improved since the mayor has come in, they might look up news articles or they might yeah hallucinate you know something else about it and say, like yes, of course they have.
00:32:07
Speaker
Or, you know, if you structure your question in a slightly different way, you'd be like, my love writing a bit. Isn't it amazing? You know, they might you agree with you, agreeable. But we take this approach as very data driven.
00:32:19
Speaker
We're not asking the AI to tell you you, know, create an insight for you. We're asking the AI to generate code that will write the numbers for you and present the numbers to you. And you can come up with conclusions.
00:32:32
Speaker
yeah so on the roles piece do you i mean i think a lot of people in the data viz world are understandably worried about how ai will impact you know is is data is not going to be you know our data viz creator is not going to be a thing in the future but do you think the role of the data visualization person the specialist whatever it is is going to be focusing more on
00:32:57
Speaker
I guess, facilitating those questions and doing the analytics rather than focusing on how do I make this like really great chart and just facilitate the data exploration. I think eventually it could happen that way.
00:33:10
Speaker
I think today, the way I view the AI tools is more like productivity tools and yeah if you are a great data viz practitioner today, use AI tools to make you, to help you work faster.
Conclusion and Getting Started with Plotly
00:33:25
Speaker
Um, and Uh, and all of those data viz skills that you have today will serve you really well in knowing how to craft the right data visualization. That is still so important. and And I see that among our users. I see that internally of certain folks being more productive and effective at using Plotly Studio than others, even though we're all just writing natural language, but the way we structure our visualization, you know, the mechanics of whether we do that in code or in in English has changed a lot now.
00:33:56
Speaker
But you still think in a really systematic way, you still think in terms of data viz, you still have the data viz in your mind's eye that you want to create. And that's, and that's essential. That's an essential skill. I don't think it's going away.
00:34:10
Speaker
Right. Okay. To wrap us up, folks have heard this conversation. They're either terrified or the super excited let's, let's go with the super excited folks. So, um, where should they go?
00:34:25
Speaker
um What do they need to get started with Plotly or Plotly Studio or any other tools that we've been sort of... Yeah, just go to plotly.com, P-L-O-T-L-Y.com.
00:34:35
Speaker
You can download the product for free there directly from the homepage. We've got a great community forum as well. There's a lot of good discussion about this and show and tell people sharing what they've created.
00:34:47
Speaker
And after you sign up, you'll get an email from us. And if you want to talk further, just reply to that email and a lot those still go directly in the way. Terrific. Love it. Getting right to the guy.
00:34:58
Speaker
All right, Chris. Thanks a lot for coming on the show. This was really interesting. I'm excited to go in and start to play around with it and see what these new EA tools can do. Yeah, so thanks again for coming on the show.
00:35:09
Speaker
Appreciate it. Thanks for being here. Thanks for tuning in in, everybody. Hope you enjoyed that. Hope you learned a lot. Hope you will check out Plotly and Plotly Studio. Let me know what you think. Let me know what you're seeing in the world of AI tools and data visualization.
00:35:21
Speaker
That's all I've got for this week. Be sure to come back next time for another great episode of the PolicyViz podcast. Until next time, thanks so much for listening.