On this episode, Steve Hamm sits down with Francois Ajenstat, Chief Product Officer at Tableau and a leader in the data analytics industry. They discuss the ins and outs of data visualization, how Francois approaches data integration at Tableau, and the future of artificial intelligence.
In this episode, Steve Hamm sits down with Francois Ajenstat, Chief Product Officer at Tableau, and a leader in the data analytics industry. They discuss the ins and outs of data visualization, how Francois approaches data integration at Tableau, and the future of artificial intelligence.
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[00:00:00] Steve Hamm: So Francois, you were at Microsoft for a decade. You were in databases, office products, business intelligence products. Why did you make the big leap to Tableau.
[00:00:11] Francois Ajenstat: Well, I've been in the data space for a long time now, actually, even before Microsoft, I was a Cognos and, I just have data in my blood. I get excited about the potential that organizations have with data. About 10 years ago, , almost this year. , I got to learn a little bit more about what Tableau was trying to do and it really changed, , how people work with data.
[00:00:35] And I got really excited about, being part of the revolution and, you know, changing. Who's able to work with data, how it's being done, and democratize analytics. And that was the reason I joined Microsoft, was to democratize technology. And I had this exciting opportunity to join an incredible team with incredible technology to really change the [00:01:00] market and change the world.
[00:01:01] And, it's been almost 10 years and it's been exciting every single day.
[00:01:05] Steve Hamm: Yeah, yeah. No, Microsoft had Vizio. How does that compare to Tableau?
[00:01:11] Francois Ajesntat: Well, Microsoft had a lot of technology. , it had analysis services, pivot tables, , access, if you will, SQL server reporting services, integration services. They acquired Vizio, but Vizio was really more of a, tool to visualize processes, whether processes are actual business processes, org charts, et cetera, is more a visualization tool , for our business process.
[00:01:38] While Tablo was using visualization technology to help people understand data. So it's not really a visualization tool. It's used to using visualization techniques to bring data to life and help people think with data.
[00:01:53] Steve Hamm: Yeah. Yeah. So visualization is one of the ways that people understand the data, but that, but, but [00:02:00] Tableau's products and technologies are much deeper than that.
[00:02:03] Francois Ajesntat: Oh, that's correct. Much, much broader than that. Right. We, again, some people think of Tableau as a visualization tool and yes, it visualizes data, but we really think it's a visual analytics solution, right? To help people ask questions of data, right? Go deeper in their data, explore it any way they want.
[00:02:23]Steve Hamm: So what is your job as chief product officer?
[00:02:27]Francois Ajesntat: my job is multifold. I like to say that I am responsible for ensuring that we build the best possible products for our customers. So I do product planning, product strategy, and work very closely with customers to understand what they need to see and, you know, help ensure that Tableau builds the right product.
[00:02:46] That's for our customers.
[00:02:48]Steve Hamm: Now we talked about visualization for a moment there. You said Tableau is much deeper than visualization, but I do want to talk about that for a minute because it seems like that is a very [00:03:00] important part of data analytics. Why is visualization so important.
[00:03:05] Francois Ajesntat: [00:03:05] Well, first, it's important to consider that our eyes, right? Vision is one of our most important senses, right? We use vision every single day, and it's how we understand the world and, you know, prior to visualization technology coming into. Right? Really the mainstream in the BI world, most data was tabular.
[00:03:29] Most people were looking at data in rows and columns, whether it's in a traditional report or an Excel, and it was really hard for people to understand what that data means and it's a slow, rigorous, painful process. And so what Tableau did was flip the equation around, use visualization technology to help people understand what the data means, think with data, and really make it easier for people to understand the meaning of the data and enable them to .
[00:03:56] Explore that data without limits, and that [00:04:00] was really a different approach to the problem. And it changes how people work with their data. It makes data more accessible, it makes it more approachable, it makes it more understandable, and as a result of that, it drives the engagement of the users, which is what we all want to do.
[00:04:18] Steve Hamm: [00:04:18] Yeah. Yeah. Now I want to reflect back Edward Tufty, who was a professor at Yale university just a few blocks from where I'm talking to you from. He popular. Yeah. He is the legend of this. He popularized the craft of a visual representation of data, and he was harshly critical of some of the computer programs.
[00:04:39] The way they use visualization. And one one's very simple, but very powerful example was PowerPoint. , so there are different theories about how to use visualization, how to use it most powerfully, most clearly. What are the design principles that underlie Tableau's approach to visualization?
[00:04:58]Francois Ajesntat: [00:04:58] , well, that's actually a [00:05:00] really interesting question because, you know, when you think about Tufty, , he really was focused on helping people. . Visualize data in the right ways, right? What are the right ways to really help people understand the meaning of data? And there's a lot of abuses of visualization technologies around, but you know, for, for us, from a design perspective, , it's really about knowing and respecting our users, providing freedom, enabling them to have incremental interaction.
[00:05:32] But most importantly, immediate feedback so they can keep thinking with their data. It's about making everything direct, safe and simple. And when you do it in that context, it really helps people think with their data in a very natural way. , and so we applied tough T's techniques automatically in the product.
[00:05:51] We have best practices in the product so that people are really, you know, staying in the flow of their analysis rather than having to [00:06:00] understand how the software works.
[00:06:02]Steve Hamm: [00:06:02] I think it might be helpful if you would walk the listener through the experience of using this QL and this is your core product.
[00:06:10] I understand. pick a real world scenario and walk us through it.
[00:06:15] Francois Ajesntat: [00:06:15] Yeah. So this QL was the original innovation that basically spawn Tablo. , it is, it stands for visual query language. And it's everything that happens behind the scenes. When people interact with Tableau, you never actually write this QL. It's not a language you have to learn. It's automatic in the background, but it's really, really fundamental.
[00:06:40] So when you're using Tableau and you say you drag and drop. Anything into the canvas is what we call the main screen. So let's say I want to look at sales over time. Well, this QL converts that into the appropriate SQL statement or the [00:07:00] appropriate database language query language that then goes out to the database and gets those results and then draws the pixels on the screen.
[00:07:08] So never in Tableau do you say, I would like to create a pie chart or a line graph. You just say, I'd like to see this kind of data, and then Vizquel converts that appropriately into the database query and into the visualization results. And that's really what makes the experience of Tableau much more natural to use.
[00:07:29] It enables people to, , to think with their data, , because we keep people in their flow of analysis rather than the traditional approach, which is really about constructing charts.
[00:07:40]Steve Hamm: [00:07:40] Can you give a scenario for us.
[00:07:42] Francois Ajesntat: [00:07:42] Yeah. So a classic example, , you know, that probably most people have gone through is, , you're an Excel and you say, I'd like to create a chart. So you're literally go into a chart maker and you choose the chart type that you want and what data applies to it. , and then when your chart is [00:08:00] created, it's there, it's fixed.
[00:08:01] But if you want to keep interacting with that. You have to go back to modify the chart, and it's kind of this back and forth approach. This QL basically throws that whole paradigm out the window. You just continue to interact and, , we automatically will visualize the data in the right way. Oh, you're looking at.
[00:08:21] You know, , sales over time. We'll make that a line chart. You can always change it if you want, but we'll do that automatically. You want to look at the geographic distribution of your supply chain. Boom. We visualize that on a map. You're looking at the correlation between sales and profit. That's automatically a scatterplot.
[00:08:40] And so the user doesn't say how they want to visualize it. They just say, I'd like to see this data and the software takes care of the rest.
[00:08:48] Steve Hamm: [00:08:48] Yeah. Well that is really cool.
[00:08:50] Francois Ajesntat: [00:08:50] It's a magical experience. It's
[00:08:51] Steve Hamm: [00:08:51] Yeah. It sounds like it is. It's really, I can't wait. I want to use Tableau.
[00:08:56] Francois Ajesntat: [00:08:56] Every day.
[00:08:57] Steve Hamm: [00:08:57] Yeah. Now your products can [00:09:00] be used on data stored on premises or in the cloud. What trends are you seeing in this regard? Are your customers moving their data to the cloud in a big way?
[00:09:10] Francois Ajesntat: [00:09:10] Oh, in a huge way. , you know, we believe in choice and flexibility. People have data everywhere. You know, we don't live in a world where there's only one source of data. There is data everywhere on premises and the cloud and applications and files. And we want to really make it easy to visualize and analyze all of that data no matter where it is.
[00:09:32] But one of the big trends we've been seeing over the last few years, that really guy, it's where analytics is deployed, is really around data gravity. People are putting their analytics closest to where the data is. So if I have a, you know, Oracle database on premises, I'll probably put my analytics on premises.
[00:09:54] But increasingly we're seeing that gravity shift to the cloud, right? With snowflake [00:10:00] in particular, more and more of the core data that people need is in the store and this is in the cloud. And so we're seeing a tremendous shift of this on-prem data to the cloud, and as a result, driving more and more of the analytics processing in the cloud.
[00:10:15] And that's been really, really exciting to see how quickly that's accelerated. Now, there's still a lot of data on premises, , but the, the numbers speak for themselves. The growth rates of cloud data is unlike any other data source that we see out there.
[00:10:31] Steve Hamm: [00:10:31] yeah, yeah. . So Tableau accesses data from on premises databases or from the cloud, from cloud databases or data lakes. , what, , I mean, is it, does it work better in the cloud or is it just kind of the same experience just from a different source?
[00:10:49] Francois Ajesntat: [00:10:49] From a user perspective, it's a, it's the same experience, right? Because we don't want users to really have to think about where the data stored. We want them to [00:11:00] think with their data, but there's some very, very important attributes of these cloud data sources that make the experience. Dramatically better.
[00:11:10] The first is performance. If you want to think with data, you have to be able to stay in that flow of analysis and every time people have to wait for results to come back. They just stopped their analysis and they walk away and we're seeing that many of these cloud databases are fast. They've got the performance, they can auto-scale, , their, their backend, right.
[00:11:33] With the separation of computers, cute and storage. So that, yeah. You have this incredible performance, no matter how much data you're storing. Secondarily is you don't have to make compromises when you're in the cloud. You don't have to limit how much data you're storing or you know, what you're storing because of the constraints of on-prem technology.
[00:11:56] So we're seeing that in the cloud. People not only get better [00:12:00] performance, but they're also analyzing bigger data volumes as a result. , and that enables them to make better decisions faster.
[00:12:09] Steve Hamm: [00:12:09] Yeah. Cool. Now Tableau and stuff like became business partners. Relatively early on, and I think at both companies existence. Can you explain how you hooked up and how you work together and what that does for customers?
[00:12:23] Francois Ajesntat: [00:12:23] Well, I don't really want to talk about how we hooked up. That's a little personal.
[00:12:27] Steve Hamm: [00:12:27] That's sexy.
[00:12:27] Francois Ajesntat: [00:12:27] That's a little sexy, but, but it was a, , an amazing meeting that we had. the attributes I mentioned earlier, right? Performance and scale are so critical for Tableau, and when we met the snowflake team in the early, early days, I think there was probably less than 40 people in the company at that time.
[00:12:49] And they were telling us what they were doing, how they were really democratizing database technology to make it. Accessible to everyone, , and [00:13:00] reducing, removing all the constraints that typical databases, , had, we were just hooked from the get go. Yes. You're telling us, wait, you're going to make storing data easier.
[00:13:11] You're going to make querying data faster. We're in. And so we started with a focus on customers and started working with some key customers together and really launched our connector. And since then, the partnership has flourished. Our customers have grown in success, and really the adoption has skyrocketed.
[00:13:35] Steve Hamm: [00:13:35] How does the connector work?
[00:13:37] Francois Ajesntat: [00:13:37] Well. So the connector we have is an optimized connector for snowflake, right? It's really tuned and designed to get the most out of the snowflake backend. It is a live connector, so when we connect to snowflake. We don't replicate the data. We use the full power of the snowflake database, and every time you drag [00:14:00] and drop, we're acquiring snowflake and getting results dynamically.
[00:14:04] It's fast. It's interactive, , and enables limitless exploration. , but really, you know, we're, we're leveraging SQL technology, but it's optimized for security, for performance, for capabilities.
[00:14:19] Steve Hamm: [00:14:19] Yeah. You know, I love scenarios. You can already tell that from my question so far. Can you give an example of how customers using Tableau and snowflake together can get great results very quickly?
[00:14:32] Francois Ajesntat: [00:14:32] Oh boy, there are so many of those and so many industries. . One of the ones I saw early was really around IOT data and customer data. All right? We're collecting more and more customer data than ever before. It's coming in from a number of different sources all over the world. Whether it's. Snowflake or Google analytics or, , your, , website interactions, just data coming everywhere [00:15:00] and you don't know how much they, that that's going to be, , how quickly it's going to come in.
[00:15:04], and so we're seeing people really take that customer data and to get their single source of the truth. , and loading that into snowflake, getting real time results in, , and not having to wait for the typical, you know, ETL and, and, and database, , set up that was required in the old days. They're able to get that data to their users as quickly as possible.
[00:15:27], and we're seeing that and yeah. Pretty much every single industry. , another example is what we've seen at these days with COBIT. , you know, there's data flowing in about cases all over the world coming in from all these, , various places, , being able to start up a snowflake instance in minutes, starting to flow data into it in a day and getting it out to users the next day.
[00:15:53] That has enabled public sector agencies, , organizations that need access to this data. [00:16:00] But it's enabled everyone to really have one place to quickly react to changing events as quickly as possible and getting it out to the key constituents really, really fast.
[00:16:13] Steve Hamm: [00:16:13] Yeah. So this is Anthony Fowchee, our hero using Tableau. Do you know?
[00:16:18] Francois Ajesntat: [00:16:18] I cannot comment, , whether or not they're using Tableau, unfortunately,
[00:16:23] Steve Hamm: [00:16:23] Okay.
[00:16:25] Francois Ajesntat: [00:16:25] but they're definitely using data, right? If, if this crisis has shown us one thing for sure is that data is so vital to. All of our responses. You know, the public sector response, the commercial response, , and speed and agility is everything these days.
[00:16:43] If you don't have those two attributes, you're going to fail. And snowflake and Tableau really make that, , , winning combination.
[00:16:52] Steve Hamm: [00:16:52] Yeah. It's really interesting. You know the whole concept of computer models, people became accustomed to that around hurricanes, and they [00:17:00] understand that models Berry and the re and predictions very now they're getting used to hearing about models in connection with all of the aspects of the spread of covert.
[00:17:11] And it's almost like. You know, the world is getting a master class in computer modeling and prediction. And it's almost, you know, but you know, machine learning is really becoming and a social part of how people understand what's going on in the world. So is that, I mean, I would think a guy who's interested in data analytics all his life, that that would be very encouraging for you.
[00:17:35]Francois Ajesntat: [00:17:35] , it's been, , amazing to see how data has really been in the forefront. Every day we see the charts every day. We see the models with different aerobars being shown. , but every day, whether you're a citizen looking at the impact of data in your. County and your state. If you're a policymaker, if you are a company trying to figure out how to get back [00:18:00] to work, right?
[00:18:01] Data is that lifeblood that helps people make good decisions. , but it's, it's shown the good, right? The importance of data. But it's also shown the bad. A lot of people have used that data in, in poor ways to visualize data in ways that may create more harm. So not only has data come to the forefront, but it's also brought up other aspects such as ethics of data and privacy that we all have to be thinking about, um, in our organizations and, and, and need to be thoughtful about that.
[00:18:36] Steve Hamm: [00:18:36] Yeah, you can cherry pick data to make just about any argument, I guess. And if you visualize that, it makes the argument even even stronger. So
[00:18:44] Francois Ajesntat: [00:18:44] Quite possibly, yes.
[00:18:46] Steve Hamm: [00:18:46] There clearly are challenges that society and businesses have to face. The, , you know, we, we've talked about vis QL a bit, but I know that you have another technology, Tableau, the truly [00:19:00] important live query engine.
[00:19:02] What does that do? How does that fit in? How does that fit with snowflake?
[00:19:07] Francois Ajesntat: [00:19:07] Well, the live query engine is, is fundamental to Tableau, right? A lot of different BI technologies take different approaches. Some, , force you to replicate the data into their technology. So you have to move all the data. I take the time, , in order to get a great experience. . What Tableau has done is created what we call a hybrid data engine where we can query dynamically alive data stored in snowflake and in other data sources, , where there's no data replication.
[00:19:39] You leverage all the security in the backend, all the compute of the backend, all the stories of the back end. So it's really analytics without limits. , we do have this other technology we call hyper. It's part of our hybrid strategy that we use for a slow databases. So if you're connecting to, you know, an old Oracle database [00:20:00] that, you know, is not performance, , we can help make it perform it with hyper.
[00:20:04] But we much prefer that that data moves into a fast performance, scalable, a database like snowflake so that we don't have to replicate data. We have one place to secure, govern, and manage all of our data.
[00:20:20] Steve Hamm: [00:20:20] Right. Cool. Now, I know that Tableau was purchased last year by Salesforce, and I'm interested in learning about what has happened since then. How are the two organizations fit in together, kind of organizationally, strategically, and what impact does that have on the way, , you know, Tableau and Salesforce CRM products work together, and also what implications does it have for snowflake users?
[00:20:50] Francois Ajesntat: [00:20:50] Well, it's been a really exciting years since, , Salesforce acquired Tableau, and we're still in the early days, but you know, [00:21:00] fundamentally. The reason that Tablo fits so well with Salesforce is that Salesforce has been driving digital transformations with customers and at the heart of every single day, digital transformation is data.
[00:21:16] Every process generates data. And they needed the best, easiest to use tool to understand and get insights from that data. And that's really what Tablo helps unlock within the Salesforce. As far as platform is, that brings powerful analytics that can help customers, right? Get a complete view of their customers so that they can drive better engagements, they can deliver better services, they can engage with them in better ways.
[00:21:50] And so really the, the two companies are partnering together to help companies drive digital transformations forward and enable them to, . [00:22:00] Analyze all of their data, whether it's stored in Salesforce or outside of Salesforce. And that's actually been something really interesting because, you know, a Tableau D works with any data, not just Salesforce data.
[00:22:11] And we work on premises, not just in the cloud. , and that's really, really important. , but something that Salesforce has been realizing over a number of years with the acquisition of MuleSoft a few years ago, where they realized that customers need to have a much broader purview of. All of the data that's coming from all of their systems and bringing that all together to get a complete view of their customers.
[00:22:34] Now, Tableau continues to be an independent business unit within the Salesforce family. , so we have the same team, the same mission, and with Salesforce, we have the opportunity to dramatically accelerate the amount of innovation and the customers that we can help. , and that's just been a really, really, , encouraging and fantasy.
[00:23:01] [00:23:00] Steve Hamm: [00:23:01] No. We talked before about the connector that Tableau has with snowflake, and I imagine Tableau had the same kind of connectors with the Salesforce CRM products, but are you doing, are you doing more deep integration with Salesforce at this point?
[00:23:17]Francois Ajesntat: [00:23:17] , we are, I mean, we are working with Salesforce in the same way that we're working with any other data source, , from the standpoint that we want to help people visualize all their data, whether it's stored in Salesforce or outside of Salesforce. So we're going to continue to enrich all of our customer, , all of our connectors to all data sources.
[00:23:36], but Salesforce has this breadth of technologies from. Einstein to MuleSoft to Quip, really, , an incredible, , set of capabilities that they can bring to bear for customers. And so we, we now have access to the full kitchen cabinet and we're trying to figure out, you know, what are the right innovations to bring to our customers that will deliver the most value to them in the easiest way [00:24:00] possible?
[00:24:01] Steve Hamm: [00:24:01] And does that, does that entail increased integration? Excuse me. Does that entail increased integration between Tableau and Salesforce CRM, or could you be more specific about that?
[00:24:12] Francois Ajesntat: [00:24:12] Of course. I mean, we are looking to unlock value out of every Salesforce cloud, whether we're talking about sales, cloud, service, cloud marketing, cloud or commerce cloud, and enable any customer to quickly connect to that data and combine it together so you can get that complete view. Really the direction we're going on is how do we help our customers get a single source of truth.
[00:24:37] So that they can see the complete picture of their customers. It's a multiyear journey. , and we are working across every single cloud and across all of our technology partners to bring that vision to life.
[00:24:50] Steve Hamm: [00:24:50] Yeah. Now in February , Salesforce and snowflake analysis strategic Alliance. Do you have a view into that from Tableau is, are you kind of [00:25:00] the middleman between it, or how does that, how does the relationship between those two work and how does Tableau fit in?
[00:25:07] Francois Ajesntat: [00:25:07] So we've had the privilege of working with snowflake for a long time and coming into Salesforce, we're definitely a key part of this strategic Alliance between Salesforce and snowflake. Ultimately, what we are trying to do is to make it as easy as possible for Salesforce customers to get their data into snowflake today.
[00:25:29] That's complicated. It requires a lot of effort for our customers to get the Salesforce data, , out and pull it out and load it into snowflake. And what we want to do is to make it as easy to get a Salesforce data. It's a snowflake, so it could be fast. It can be stored, it could be combined with other sources really, really easily, , and used with tools like Tableau or quite frankly, any other.
[00:25:57], analytic solution or other solution [00:26:00] that requires access that data.
[00:26:03] Steve Hamm: [00:26:03] Oh, help me understand the data that's in those Salesforce CRM products, those clouds. Does it have to be extracted from those. From those software applications and pulled into snowflake , and are there therefore, , copies of it or are you able to do this in a way that it's still just one, you know, one version of the truth?
[00:26:29] Francois Ajesntat: [00:26:29] Well, we are extracting the data out of the Salesforce clouds and bringing that into snowflake so that snowflake becomes that. Analytical data source that can be used for a multitude of purposes. You know, generally speaking, it's never a good idea to go query a transactional database, , which really Salesforce is transactional in nature with opportunities and transactions coming in every single day.
[00:26:56] And so bring it into a system that's [00:27:00] optimized for analytics that's optimized for queries, , is really critical for our customers. And that's what we are working on. So by having that data flow out of Salesforce into snowflake in an easier to use manner will mean that more customers will be able to get more value out of the data that they're collecting in Salesforce and then use that in a strategic way in their organization.
[00:27:26] Steve Hamm: [00:27:26] Yeah. So once again, give me a scenario of how a snowflake customer, who's also a Tableau customer and a Salesforce customer, what new things are they going to be able to do as a result of all of these integrations and relationships that they couldn't do before or they couldn't do as well before?
[00:27:47] Francois Ajesntat: [00:27:47] So I'll go back to the keywords, speed and agility. Speed of chili is everything these days. If you want it to do, you know, connect to snowflake, [00:28:00] Tableau and Salesforce together. You could do that today, but it's going to take you time. It's going to be complicated. It's going to be expensive. It's going to require a lot of effort to connect the systems together, move the data over, get it ready for analysis, build the data models, then get visualized.
[00:28:20] And so what we're trying to do is, again, focus on speed and agility. How do we reduce that time to value? How do we get that data into the hands of people that need it as quickly as possible? So it's not that you couldn't do it before, but the effort was so high that either a few number of companies could do it or the time to value with soul long.
[00:28:44] That maybe, you know, the opportunities in front of them, , were missed because they couldn't react as quickly as possible. And so if you think about opportunities coming through, , service requests coming through, , you know, all those things, you want to get it out to be [00:29:00] analyzed and managed by snowflake as quickly as possible.
[00:29:03] So, take an example in these COBIT times. . Some organizations or some agencies have been using service cloud to manage all of the incoming requests that they're getting from their customers. Think of a, I'm looking for access to a loan. , I'm looking for, , rescheduling an appointment or cancel an order.
[00:29:25] All of these requests are coming in through a ticketing system like service cloud. Well, it's coming in in a different pattern than it ever did before. It's probably coming in at a faster clip than it ever did before in the history. Well, how do you respond to that as a company? How do you change and adapt your resources, right?
[00:29:44] Your inventory, your customer service, risk personnel as a result of that? Well, if you're not able to get it out to be analyzed as quickly as it's coming in. Well, you're not gonna be able to respond to [00:30:00] the challenges and opportunities ahead. So getting that time right, reduce, getting the value of that data into the hands of as many people as possible by making snowflake.
[00:30:13] That place where all questions can be answered is the difference between success and failure. , and we're seeing that every single day and companies everywhere.
[00:30:29] Steve Hamm: [00:30:29] You seem to be really passionate about the user experience, and I'm just wondering, I mean, obviously you're a, an executive pretty high up in the company. How do you keep in touch with what the user needs, what they want, what their problems are? I mean, do you just read a bunch of report feedback reports or, or do you actually get out there and, and, and talk to them and, and that kind of thing?
[00:30:53] Francois Ajesntat: [00:30:53] I think passionate is kind. We're obsessed with the user experience. We're obsessed with our [00:31:00] customers, and that customer obsession is what drives us every day. How can we keep making technology that helps our customers be more successful? Uh, and that permeates our culture. That permeates our values. .
[00:31:15] You know, it is one of our top values as a company is customer success. And so we just inject, right? Our developers are people with, with opportunities to listen to customers. Everything from what you might expect, like telemetry, , to understand how people use the software, to, , one on one conversations that will.
[00:31:40] We have with our customers that give us valuable input. We drive large beta programs so we can get our cust or our technology in the hands of our customers as quickly as possible. , but one of the things that Tablo has that differently, and I think snowflake does the same thing too, is that we have an incredible community.
[00:32:00] [00:32:00] You know, a lot of people describe our customers as fans, but our community is probably one of the biggest assets we have that's not on our, on our books, but it's one that changes how we interact. Mmm. With. With ourselves, with our customers, and make sure that we build incredible capabilities. , so our community gives us really powerful feedback that we listened to, right?
[00:32:29] Every feature that we build, we ask the questions, who is this for? What's the customer tell us? Who in the community has asked for this? What are the use cases? , and then we continue to refine and innovate so that we can deliver that solution as. Delightfully, yeah. As powerfully and as quickly as possible for our customers.
[00:32:53]Steve Hamm: [00:32:53] We've talked about how Tableau and snowflake work together on behalf of their joint [00:33:00] customers. How does Tablo use snowflake for its own needs for its own data analytics and data management?
[00:33:07]Francois Ajesntat: [00:33:07] while. We're a snowflake customer too. We use snowflake for everything, and it is our single source of truth at Tableau. Prior to this, we were using an on prem SQL server database, and that database was built and architected.
[00:33:26] Over a decade ago when snowflake did not exist and it was starting to show its age, we were starting to have limits on performance. We had to constantly massage it and tune it to get the performance that we needed, especially as our company grew, as the amount of data that we wanted to analyze grew. , and.
[00:33:48] The needs across the organization grew as well. And so we just couldn't keep up, keep up with our old approach. , and so we evaluated pretty much every single [00:34:00] technology, , database technology on the market. And we landed on snowflake for a few reasons. Number one was performance. Could we get the performance that we needed for the amount of analytical usage we're going to put on it?
[00:34:14] Second was. Could this technology enable future growth? We're 5,000 employees strong today. But what's going to happen when we're at 10,000 employees? What, what's going to happen? When do we have, , you know, 10 X more products and 10 X more customers? Can the technology keep up? And then third is how do we focus on our business rather than infrastructure?
[00:34:38] We then want to be in the business of managing databases in the old ways. We want it to focus on the business of Tableau. And let. Our database provider essentially ensure that we have the right stories and performance. And so when you take those key characteristics, snowflake met that list [00:35:00] perfectly, and it is now our standard.
[00:35:02] I use it every day. I asked lots of difficult, different questions about everything from sales to, , telemetry data, and I go to one place, snowflake to find that out.
[00:35:15]Steve Hamm: [00:35:15] hey, so the last question we want to ask you to be a bit of a visionary.
[00:35:19] Francois Ajesntat: [00:35:19] Oh boy.
[00:35:20] Steve Hamm: [00:35:20] Yes. Look, out ahead. Look beyond this carbon crisis though, that's kind of difficult. I know. How do you see data and specifically data analytics and data visualization, the kind of stuff that you do, how is it going to impact business and society in powerful new ways?
[00:35:40] Francois Ajesntat: [00:35:40] And that's a really, really good question. And you know, we see a future where data and analytics is as pervasive as Excel is today. And you might think of that and say. Well, is that really a future that [00:36:00] will ever be true? Well, if you go back in time, maybe 20, 25 years ago, , you know, Excel was not pervasive.
[00:36:07] We didn't have a PC on every desk. , we thought that tools like Excel were for the finance department. They were for the select few that it needed to crunch numbers. , and today we have. Yeah, a tool like Excel on our desk every day, and we use it for a wide variety of use cases. , so we have this vision that, you know, data and analytics becomes pervasive.
[00:36:33] It's part of the fabric of society. It's part of the fabric of organizations. But we're far from there. I think there's a lot of things that we have to do as a society, , to get there. It starts first and foremost with data literacy. You know, most people aren't literate in data. We're not teaching it in schools yet, and so we have to get this kind of curriculum of, you know, how do you think [00:37:00] with data, what is some basic math and statistics?
[00:37:03] It just has to be ingrained in the curriculum. Of our schools all over the world. Second is, as much as we have advanced technology and we're very proud that Tableau's the number one easiest to use analytics product in the market, it's still too hard to use for too many. Right? We need a 10 to a hundred X improvement and ease of use of analytics.
[00:37:30] And that's going to come in the, in multiple forms. , some of it might be natural language, , artificial intelligence or just user experience, paradigm changes that will help people think with data in a much more natural way. But the third, and I think just as critical element is. That data is complex for many people.
[00:37:55] And I'm not just saying, you know, looking at data is complex, but data is complex. [00:38:00] It comes in different shapes and sizes, and it comes in from different places. , and in order to. Be able to analyze that data. You have to be able to store it. You have to be able to connect to it and combine it all together.
[00:38:17] And so I think that we need a step function, , improvement as well in how data is managed. At the enterprise scale. And I use enterprise not to no qualify a company type, but really at scale, , we have to make storing, managing, and scaling that data so much easier. And if all these things start coming together, we have.
[00:38:41] Yeah. People that are literate in data. We have the technology that's easy to use and we have availability of data. Now we're going to be used to be able to use it in every parts of our lives, whether it's personally, how you know you might. Look at yourself and you know, people do a lot of, , [00:39:00] a quantification of themselves, you know, their health metrics, et cetera, whether that is looking at every single business process.
[00:39:07] As, you know, companies digitize and, you know, the online world and this postcode world is definitely going to change, , and probably become the prominent, predominant way that people, , work and live and interact. , and so how do we. Adapt to those new patterns and make data. We bring that to the forefront rather than being in the background because the more that people are able to utilize that data as quickly as possible, the better it's going to be.
[00:39:35] And so I think every business process, right, every person, every company will become data companies. And I think every company will be transformed through this crisis, , as we move to the next normal. But every company, , has an opportunity to innovate and differentiate by utilizing data as the fundamental element that will [00:40:00] make that happen.
[00:40:01] Steve Hamm: [00:40:01] Yeah. That's kind of inspiring Francois,
[00:40:04] Francois Ajesntat: [00:40:04] Oh, thank you. I'm, I'm excited.
[00:40:07] Steve Hamm: [00:40:07] I guess so. You know, it's
[00:40:09] Francois Ajesntat: [00:40:09] a lot, but I think we can get there.
[00:40:11] Steve Hamm: [00:40:11] Yeah. You know, ever since the early days of the PDA, early 1990s and you know, the personal digital assistant, but whether it was a device and then later it really became a vision of a, of a software application. I've always just loved the idea of having personal digital assistants for individuals that help them.
[00:40:34] You know, live their personal lives and their professional lives more successfully and satisfactorily, and, and, , you know, and with more fun too. And, , so I just love the idea of having PDAs that people, and I guess you call them question, answering machines these days with AI that people really have always available to them and that [00:41:00] they're.
[00:41:00] You know, I think often the interface will be voiced, but often the feedback will be, might be voice or it might be visual representations of data and things like that. And so do you have a take on that? I mean, do you, do you think that there'll be a time in the future when individuals have that kind of powerful, you know, companion digital companion with them all the time?
[00:41:25] Francois Ajesntat: [00:41:25] I definitely think that that's where we're headed. And you know, there's two, two kinds of things. There's the question and answering machines that will be automated, right? They'll just happen automatically without a human, right. , many. Business processes will be digitized and we'll use analytics technology to improve them automatically, but then there's the human powered ones where you have a, you want to be able to answer questions at the speed of thought and whether that's having it in my pocket every day where I can know exactly how my business is doing.
[00:42:00] [00:41:59] Or whether the system is proactively telling me the things I should be looking at and guiding me to saying, Hey, there's a problem here in the Midwest. You might want to go look at that right now. , and so that the technology will become much more of an assisted okay. Technology to help people not only get the answers they need, but tell them when they should engage with their data.
[00:42:25] Steve Hamm: [00:42:25] Yeah, so it's really, I like the word assistive and I think that also the idea of kind of the human and the machine kind of collaborating for the benefit of the, of the human and their, and their organizations or their, whatever agenda they have is another important thing as well. So, first of all, thanks so much for your time today.
[00:42:50] I mean, you, you explained certainly what Tableau does very clearly how it interacts with snowflake. , you really, I think, summoned up an [00:43:00] image of the power of data analytics, data visualization that I think. Anybody who listens to this podcast is going to go away saying, let me get my hands on that capability.
[00:43:12] So it's been really, it's been really educational. It's been really fascinating. Thank you so much for your time.
[00:43:17] Francois Ajesntat: [00:43:17] Well, it's been my pleasure and I look forward to, , our growing partnership together between snowflake, Salesforce, and Tableau. There's so much opportunity and, , I'm glad that we're able to share this and continue the conversation together.