Transcript
Jess Carter [00:00:01]:
The power of data is undeniable and unharnessed. It's nothing but chaos.
Speaker 1[00:00:06]:
The amount of data, it was crazy.
Speaker 2 [00:00:08]:
Can I trust it?
Speaker 3 [00:00:09]:
You will waste money.
Speaker 4 [00:00:11]:
Held together with duct tape.
Speaker 5 [00:00:12]
Doomed to failure.
Jess Carter [00:00:13]:
This season, we're solving problems in real time to reveal the art of the possible. Making data your ally, using it to lead with confidence and clarity, helping communities and people thrive. This is Data Driven Leadership, a show by Resultant.
Hey, guys, it's Jess. You're going to hear from Jorge Sancha in just a minute and he is going to talk a bit about his company, but also about what it's like to have real-time or near real-time data at your fingertips for data driven decisions. Now, I'm not going to project that everyone who listens to this has streaming data at their business, but I do think that there's a question that'll make this episode really valuable to you, which is this do I ever wish I had data faster so that I could make better decisions more quickly and see better outcomes? I think if your answer to that question is yes, whatever that data is, whatever you wish you could have more readily available more quickly. I think Jorge does an incredible job sort of explaining how he tries to get that vision to fruition with his accounts. And so it's a really interesting conversation and I think he does a great job explaining how does the business see this as a business problem, this need for speed and accuracy of data to make good decisions? And then what does it look like for a tech team who's thinking about this? And how do they also help convey the potential value proposition to business leadership when they're looking at their budget or ways to be more effective or efficient? So I thought this was a really interesting conversation that's got me thinking about what data do I wish I had more available to make data-driven decisions more quickly. Jorge, welcome.
Jorge Sancha [00:02:05]:
Thank you. Great to be here, Jess.
Jess Carter [00:02:08]:
Awesome. How's your day going?
Jorge Sancha [00:02:10]:
Great. A bit jet lagged because I'm in Madrid today, flying from the US. Just a day ago or so, but other than that, never been better.
Jess Carter [00:02:23]:
I mean, not cool for the jet lag, but it's cool that you're in Madrid.
Jorge Sancha [00:02:27]:
Yeah, this is my hometown, so we have a team here. So I'm here for work and to see some prospects as well. So always exciting to come back.
Jess Carter [00:02:38]:
Awesome. Very cool. I've been to 26 countries, but I've never been to Madrid and so Spain and so I'll have to pick your brain on where to go and put that on the list.
Jorge Sancha [00:02:47]:
You should stick around for some tips after we're done.
Jess Carter [00:02:50]:
Okay, happy to. I would love that. This is a perk of podcast hosting. Well, now, Jorge, you are this Co-founder of Tinybird, and I wanted to ask you to kind of get into what is that and how did you get there? And I think we're going to sort of land at a conversation I want to have around data warehousing, just kind of in general. So will you unpack what is Tiny Bird and how did you become a co founder of this cool company?
Jorge Sancha [00:03:16]:
Sure. So Tinybird is a real-time data platform. It helps developers to build over large amounts of data with very low latency and very high concurrency, just using SQL and Git. So it basically using the tools that developers already know how to use. The way we started was because we realized that first data was growing at an incredible pace and a lot of the data warehousing technology was not meant to build applications on top of it. And we saw in all the companies that we were talking to, also in the company that all of the co-founders of the company were working at, that people would throw cathedrals of infrastructure at this problem. You have technology to capture the data, you have technology to store the data for an amount of time or forever. Then you have pipelines to move the data from one place to another one.
Jorge Sancha [00:04:23]:
Then you'll need low latency stores to build applications, and then you'll need to build a back end. And that's essentially a very conceptually it should be a very easy problem to solve, which is you have data, you want to query it and expose it somewhere in an application. And that's what we set out to solve, which is we want to make it easy. And I want to make it easy for data teams, but also for engineers and for data teams to enable other engineers to build on top of that data, on top of large amounts of data in the same way that they build on top of small amounts of data. So that was how we went for it, basically, and what we were trying to solve.
Jess Carter [00:05:04]:
If I try to step back and look at this sort of really interesting problem and solution, you've created some of the questions I have, and I've been in state government for a while and so a lot of that kind of notoriously is known for being maybe a decade behind the private sector. And so, yeah, we're still, hey, you should have a warehouse. Most of the time we're like, that's a good idea, let's put your sources together and look at what we can learn about your whole customer. The full picture of somebody who's on unemployment now seeking reemployment or somebody who's in the Bureau of Motor Vehicles trying to get a license. And so one of the questions I have for you is how do you explain the need for this kind of product to somebody who is maybe just learning the value proposition of a warehouse? Hey, it's not just my applications, it's the data within them. But then you have metadata. Is there a simple way you found to kind of explain the value proposition here.
Jorge Sancha [00:06:01]:
How we explain what we do is something we are constantly grappling with, as you can imagine. Because depending on who you're talking to and where they are in the data maturity curve, let's say, they either get it immediately or you have to paint a picture that they can clearly understand in the way that they have been conditioned to understand data or data warehousing and so on. One of the things it's important to understand is when I basically start from scratch, I start talking about transactional databases like Postgres or MySQL. They're great to keep track of states, what's happening with my shopping cart, what's happening with my order. And then those databases are great at updating individual records and reading just a bunch of records. Then you have analytical databases. Those are the data warehouse. They're interested in the history of those states.
Jorge Sancha [00:07:05]:
You can track the history of what's happened, so not just how many orders you've had, but how long do they stay between purchased and shipped on average. And that is what data warehouses allow you to do, is understand your business much better and what's happening and what could you do to improve on your business and whether you're getting better or not at it. That's the best reason to get a data warehouse, which is it's not just about delivering on your promise, it's about getting better at it every day. And it's important to look at the data if that's what you want to do. And then finally, once you're able to collect that data and you can generate insights out of it, you'll want to automate a lot of those insights and what to do about them. And you'll want to build other things on top of that data. You'll want to show your customers all that information and give them a better understanding on how they benefit from your product. And you'll also want to release new features based on that data.
Jorge Sancha [00:08:22]:
And that's where real time becomes really important and it can be a great competitive advantage. The value of data decreases over time. So the sooner you can leverage those insights, the sooner you can do something about those insights, the more benefit are you going to get out of it, the more you can iterate about over those insights. And we've seen that over and over now over. We're in a fifth year now, but we've had enough time to see how when a company starts working in real time, it is mind shifting in the sense of hey, if I can do these, I didn't realize we could do this in real time. And this is changing how I operate my business. I can now automate certain things. I can now have a better sense of what's going on in real time.
Jorge Sancha [00:09:21]:
I can react to problems and opportunities much faster. And then it basically triggers a lot of hey, what else can I do in real time, but it also triggers the competition as well because it is such a competitive advantage. We always say speed wins in this company and that's what we're talking about. We're talking about being able to make decisions faster, use less infrastructure, faster, queries, all of those things.
Jess Carter [00:09:48]:
That's amazing.
Jorge Sancha [00:09:49]:
Does that make sense?
Jess Carter [00:09:50]:
It does make sense. I was going to ask you too for do you have like one or two really clear examples where you kind of met a client and they had maybe the analytics, they had of course, their transactional, but then they kind of added this on top and what it yielded.
Jorge Sancha [00:10:04]:
Yeah. So I'm going to give you three different examples of three completely different use cases. The first one was with a big retailer, the biggest fast fashion retailer in the world, who was they're very competitive and very conscious about the value of moving fast. There's huge competition and there's very low margins. So selling more faster is very important. And when we met them, they had about an hour latency from the moment that a transaction took place somewhere in the world in their ecommerce until it was visible in their reports. And we helped them bring that down to seconds. So very near real time.
Jorge Sancha [00:11:01]:
And also because Tinybird, very easy to build new use cases only based on SQL. So once you have the data in there just creating SQL, just with SQL you can create APIs that you can integrate in your product and they're very low latency, ready to scale. So they started adding more and more use cases and then they went from about 60 internal users to over 1000 internal users of this application. And now all their decisions about their business, it's based on real time data. It's not based on what happened yesterday or what happened an hour ago. It's based on what's happening right now. And that's more or less important for human decisions. Like some decision doesn't really make a difference if it's right now or an hour ago.
Jorge Sancha [00:11:46]:
But the compounding effect of first, everyone making decisions with real time data, and second, the fact that now some of those things, some of those decisions they can automate because you can have a process that is listening to what's happening in real time and triggering other actions downstream and observing the result of those actions immediately. Like if you have a marketing campaign and it's not working as fast or as well as you want it to be and you make a change to the price and so on, you're going to see immediately what's the impact of that. Imagine during Black Friday, for instance, huge volume, so the difference of getting it right or wrong can be very important. So that was a great use case for us and it was one of our first use cases as well, and one of our sort of the first customer that sort of validated this, I guess a huge potential market and opportunity for us. And then we've repeated the same kinds of things in different industries. Another one, we work with a company called FanDuel who's the sports betting application and company, which they're amazing technical team and they've made a big bet in real time. And we help them solve a number of use cases around personalization and what happens when you get to the app and what happens when you and that is really interesting in the context of understanding that going back to what I was saying at the beginning, that data warehouses allow you to understand your business over time. Analytics has been traditionally used to understand the past, but real time analytics can be used to affect the present of the user experience and that's another huge opportunity as well.
Jorge Sancha [00:13:40]:
It's affecting the user experience based on not just what he's experiencing, but what is the other users of the site experiencing as well. What could be interesting for them? Mixing different sources of data. So that's another great example of real time analytics as well. I can go on, but I think those are a couple of good examples.
Jess Carter [00:14:05]:
This is awesome. Okay, I have so many questions already just based on those examples. So one of my questions for curiosity is I'm going to imagine that most of your clients aren't going from a different real time platform to yours. They're going from not having real time data to having it. Can you speak to what that journey is like for a customer? Is it an awkward dance, is it beautiful? Do you have some coaching or advice you give them? What does it look like?
Jorge Sancha [00:14:34]:
It's beautiful as you can imagine. Actually one of the things we always say is that speed and scalability and security are super important, but they're table stakes. Of course you need speed, security and scalability, of course. But we are betting very big on developer experience is what is your experience and how fast can you build, can you turn data into revenue? That's really what we're aiming for. So it's very easy to when you sign up to, I mean we obviously have like a self service, but we also have dedicated infrastructure, we have for enterprise and we have all kinds of setups as you can imagine, depending on type of customer and what the customer wants and the type of the size of the project. But essentially with Tinybird there's no infrastructure to manage. So you just sign up and immediately you create a workspace. A workspace underneath has its own database that you didn't have to set up.
Jorge Sancha [00:15:40]:
Then. For instance, if you're using Kafka to capture your data, you can simply connect to Kafka. It'll start ingesting right away, and you can start writing SQL right away with a few clicks, and then turn the result of those queries into APIs that you can immediately integrate without having to worry about the scale or anything like that. So it's very easy to get at least one thing done or a POC done. If you want to say, okay, I have this stream of data here. Maybe Kafka, maybe it's events you want to send to us directly. Maybe you're using something like Confluent or Red Panda or some other provider. Or maybe you have data in your data warehouse that you want to build, on top of which you want to build APIs.
Jorge Sancha [00:16:28]:
We can also bring data from batch type of sources and sort of run a query and bring it into Tinybirds. So you can then build APIs that are very low latency and that can scale. So it's a very easy way to start. And normally what we recommend with customers is to start with a one problem, let's solve one problem together and then move on from that because it's very straightforward and then it's not always a new thing. Sometimes it is a new thing, sometimes they don't have a real time database. We also see like real time databases. There's a few out there. They all have their good things and their bad things, but they are notoriously hard to scale and manage or manage at scale.
Jorge Sancha [00:17:19]:
So we've also gotten customers that were already using a real time database and were struggling or didn't want to invest in infrastructure teams. They wanted to invest in developers and they wanted to invest in going faster rather than in managing infrastructure. So that's another example of we've also helped companies to migrate from some other real time database to Tinybird.
Jess Carter [00:17:45]:
Okay, so then it really does seem like a niche for you guys, is if an industry or area speed matters. Speed is going to be where in the same I liked your example about marketing because I think in a lot of times, it's like you're running campaigns and it could take one month, six months, a year to really understand the value. But if you can introduce cycles that are near real time where we can make adjustments and incrementally see the change that's coming from the decisions we're making.
Jorge Sancha [00:18:15]:
Or a B testing it as well, like different promotions for different people or segment your users in real time. Another cool example, for instance, is in gaming. So gaming, they have some companies, like millions of players playing at the same time. And I don't know if you play any mobile phone games type of games, but my husband does. You'll know that in the middle of like when you finish a level, you get offered things like buy this box of gold coins or whatever at a particular promotion. That promotion is not the same for everybody. It depends on what's your level, it depends on your profile in terms of characteristics like whether you're a male or a female or it's based on a number of things. And that segmentation.
Jorge Sancha [00:19:13]:
One problem we've seen in gaming companies is that segmenting those users with data warehousing technology like Snowflake or BigQuery and so on is not fast enough. Like maybe you can do it every 15 minutes or every hour. And what happens is that they end up offering to the player the wrong marketing promotion. Because if you've been playing constantly, you'll be ten levels further when you're offered that promotion and it's already dated. So that's another really interesting use case, which is, hey, I want to try different things with different segments of users and see which one performs better. So now with technology like Tinybird, you can do that really quickly and then constantly improve.
Jess Carter [00:20:04]:
Okay, that is super cool when it comes to building some of your data maturity, because some of this is it's neat that the product exists, it's neat that it's available. But what's interesting to me, Jorge, is there's an intersection of buyers, users that are either extremely business oriented, leaders of businesses who want a different outcome, faster, and highly technical CDO CIOs who appreciate what needs to this isn't more infrastructure. You do need developers as a separate team. And so when you're watching customers or people start to get their arms around this product and understand the sense of real time data, are you watching that combo where it is very much business led or is it more technology led? What are you noticing?
Jorge Sancha [00:20:51]:
That's a great question. Today someone said to me the business side of any company, it's insatiable. Like they'll always want more, faster, and if you give them faster, they'll want it faster and more things and so on. And the interesting thing is that that grows or those needs and that competitiveness, that sense of speed grows as fast as data grows. So there is this double pressure to, hey, you have more data, so you need to be better at solving the problems, but you also need to deliver more. And there's this huge cost pressure right now, the way that sort of an economic situation is. So the need to do more with less, it's becoming increasingly more relevant. And so we see two different things.
Jorge Sancha [00:21:56]:
One is pressure from above, either to go faster and not hire more people or reduce cost, which you can look at it however you want. And then we also see that's a good reason why companies start looking into real time analytics and technology like Tinybird, because there's only so much you can do with data warehousing technology and maybe you can hire more people to go faster, but that goes against some of these things that we're talking about. So that's one type of sort of pressure. The other thing is wrong tool for the job is we have this thing we want to do, we don't think we're using the right technology, like for instance, the gaming use case. We need to segment users faster, and we're hitting diminishing returns with the current technology, and that's more a technology driven search as opposed to business driven search. Okay, so you see both yeah. And I think the difficulty to sell for a company like ours is that people will always first try to use the tool that they have, and they'll just hammer at it, even if it is not the right tool. And solutions will be defended for months until someone just says, you know what? So it really depends like some companies are very speed and opportunity minded and they'll be happily try something if it feels that it's going to go faster.
Jorge Sancha [00:23:47]:
Some others it's really difficult to get them to commit to trying something new and so on. It's a balance. I prefer it when it comes from the technology because it's a clear pain a technologist has already identified and then you might be or not the right tool but they're looking for something and you can try to figure out with them. That's also because I come from product and technology I don't come from sales but ideally both things collide like someone in technology really under pressure to deliver on some use case that is not then not the right tool for the job and the same time under pressured to do it fast and so on, right?
Jess Carter [00:24:25]:
It's a clearer scope usually I would imagine where a technologist really understands what this might be and they can chase it down with a little more sense of data literacy and maturity of what to expect. I think a lot of business users will just say why can't we just do it faster? And then you're under this pressure of who's the guy or gal that says snowflake can't nobody wants to be that person, no one wants to say that and it's because you can make those solutions work for a variety of things. But I think your comment on when are we experiencing as a business diminishing returns? When are we trying? I mean I have been on a couple of projects where we were working our butts off, right, 18 hours days for 64 days straight trying to make something happen and the painful exercise of stopping to say okay, this is a 17 trial and it didn't work and what did we learn the 17th time? But at some point making the decision to sort of kill the sunk cost and pivot to a new approach, try a new trial. Maybe it's a new technology but that requires some serious leadership and somebody who really understands when we need to be at our wits end and stop the chaos.
Jorge Sancha [00:25:33]:
Yeah, exactly. It's a process and one of the internal metaphors that I use a lot when explaining what we're trying to do and how to get into companies and so on I don't know if you've ever seen how big rocks get split in two. You get like a little nail, like a nail and you start putting nails across a line and then you hit every nail and nothing happens and literally you can see certain cracks and eventually it opens up. So you need to be pounding on that door and explaining the benefits. And then on the other side, the same thing needs to be happening. They need to be hitting their head against the wall against some problem. And then when you're hitting all of those things, eventually companies open up and try new things and that's how we get into opportunities and so on.
Jess Carter [00:26:37]:
It's an awesome analogy. There's pain and grunt work and a whole bunch tied into the analogy. And in the real experience exactly. Okay, so then I'm not the data engineer myself here, but I've been around them enough. So let me play this through and have you correct me where I'm wrong. So you kind of said, hey, the experience would be that you'd sign up, you get access, you kind of get your ingestion moving, whereas soon as there's data that we can access now, we can start playing with the normal tools with Git and et cetera, and kind of start to do our analysis of SQL. Is that right? So the experience really is get access and then the ingestion really starts rolling. What we can do with our real time data, I'm assuming one of my questions about ingestion is, are we automating kind of continual ingestion? What does it look like with real.
Jorge Sancha [00:27:27]:
Time ingestion, from the moment the data gets generated until you can use it for something, the latency of that whole process needs to be very short. So if you think about how traditionally at the beginning when I was talking about these cathedrals and infrastructure, one of the problems with that, even if it's a perfectly valid setup and you're great at using those tools, is the handoffs between each component. Every handoff adds latency, especially if you need to do pre aggregations or massaging the data to leave it in a particular state so that you can use it. So that gets you further and further from real time every step of the way. So in the case of Tinybird normally forced real time, you have some type of streaming data. It could be events from a website or it could be topics that you're using in Kafka to connect different microservices in an event driven architecture and so on. So normally we can connect to those topics, whether it's Kafka or something else, and then immediately start ingesting with very low latency. So without any lag, pretty much there's always a little bit of lag depending on a number of factors, but subsecond latency.
Jorge Sancha [00:28:55]:
And then the moment it hits Tinybird, it's being stored in an underlying database that is already designed to scale and withstand production use cases. And then the moment it hits the database, you can already be querying it. So from the moment we read from the Kafka topic to the moment you can then create an API in Tinybird exposed the result of a query as an API and have that consumed is very, very small talking subsecond here from ingestion to query. And the key thing is that there's no middleman there. The data is being generated, we can ingest it right away and you'll be querying that same data in the same database for these analytical use cases. In our case it's managed connectors. So you don't have to manage your own connector. You can also use our API to just send events to us if you want.
Jorge Sancha [00:29:54]:
And that also super convenient for if you want to integrate your application to just send or instrument your application to just send events to us, things like that. But especially in large companies we see some kind of streaming infrastructure. I always use Kafka, but it could be kinesis or it could be PubSub or it could be something else and we can connect to that and constantly, automatically bring data. And then we can do things like materialize aggregations and joins and things like that in real time. So at ingestion time, so you don't have to program that materialization like you would do with views and other types of databases. The ingestion triggers the materialization. So let's say you wanted to build a chart or like a temp series chart, you can have those buckets, let's say, aggregated at ingestion time so when you query it, you're only querying the result already.
Jess Carter [00:30:58]:
Okay?
Jorge Sancha [00:31:00]:
And it's always up to date, basically.
Jess Carter [00:31:02]:
That's cool.
Jorge Sancha [00:31:03]:
That enables us to scale to massive amounts of queries as well.
Jess Carter [00:31:10]:
One of my other questions is this does kind of all lead to data literacy, right? Because even if you have the technologists who are learning how to do it faster and they're the ones who came at some point, it's not going to be valuable if the business keeps responding to it at the pace and rate that they did the old stuff, right? So have you seen clients sort of start to get their arms around, maybe the technology team starts, but we have to start to pull our business in and get closer to it. What does that look like and feel like?
Jorge Sancha [00:31:42]:
I think normally we see the opposite or the companies we've worked with are generally digital natives or very advanced corporates, very competitive corporates. And so there's a lot of pressure, there's huge objectives on the business side and technology is sort of coming behind. And then in some cases, some startups we've worked with, they've been able to demonstrate, hey, this is possible with Tinybird. And for instance, we've had a few instances where some problem they've been wanting to tackle for a while in an internal hackathon in the company, some developer using Tinybird has come in and said look in real time, so just for the hackathon in 24 hours or whatever. So those things we've seen, but generally the pressure, we see it coming more from business and hey, we want to do these things than the opposite.
Jess Carter [00:32:50]:
That makes sense because in those industries, when there is this sort of streaming data, they're already used to this. They know speed matters. They're not trying to figure out if speed matters. So they're chasing it down to see they're finally satisfied with pace.
Jorge Sancha [00:33:04]:
Exactly.
Jess Carter [00:33:05]:
So they're just thrilled that they have the answers at the pace that the business needs them when they feel like they haven't maybe in the past. Is that right?
Jorge Sancha [00:33:10]:
That's right.
Jess Carter [00:33:12]:
Okay. I don't know, what about a company? I'm thinking about a mid sized company who hasn't figured out yet what role speed might play in their business. They're sort of not quite as sophisticated or mature in chasing that down. Do you have any thoughts or advice for a business that hasn't quite contemplated that yet fully and maybe reasons or use cases for a near real time data in their solutions?
Jorge Sancha [00:33:42]:
I think the way to think about this is that we've heard a lot of times I don't need real time for my use case. But actually real time is revenue is not cost. It used to be cost because doing things in real time used to be very expensive, but it isn't anymore. And what you need to be thinking is what? That speed wins. That the compound effect of being able to do things faster is huge. And if you can make decisions faster, it means you can make more mistakes faster and iterate faster and come to the right solution faster. And if your queries take, let's say that all your queries take 1 second, which for us is very slow, but let's say all your queries take 1 second. If you need to run ten queries per second, then you need at least ten CPUs to run those queries.
Jorge Sancha [00:34:50]:
If you need to run 1000 queries per second, then you need 1000 CPUs to be able to scale to that. Let's say that your queries take 100 milliseconds, then you need ten times the amount of CPUs to run 1000 queries per second. So that's money. That's money that you can dedicate to other infrastructure or to other projects or to hire more developers or whatever. So whether being able to make decisions faster, to react faster, to spend less in infrastructure also in terms of development, like one of the things that we see is development speed. Like if your queries as opposed to using something like Spark, where a query will take maybe a few minutes, if your query takes a couple of seconds, what effect does that have for developers in order to build things, to put them in production? They won't run a query and go get a coffee. They'll run a query and then make whatever change they need, just put it in production. So all of those things added up.
Jorge Sancha [00:35:50]:
And thinking about speed and embracing speed as a way of working is what drives being the market leader. If you look at market leaders in any vertical, they'll probably be fast movers. Very likely.
Jess Carter [00:36:12]:
Yeah, absolutely. I like the idea too, as we are definitely hearing, as you mentioned earlier, questions about how do It teams get more efficient, how do we make sure that we're driving the most value out of their time? And we're looking at a whole bunch of companies are probably looking at their budgets for next year already, right? And they're looking at, do we need more developers? Is our infrastructure cost going up? What's going to change about next year? The ability to understand that this can actually drive efficiencies in your technology spend and how you leverage it more successfully or thoughtfully. That makes a ton of sense to me. And I just love this phrase you used. You talked about turning your data into revenue. I love it, and I think that in my experience, there are people that 100% understand that concept and 100% don't. I don't know many people in the middle. Like, either you get it and you understand that your data is revenue or it's potential revenue you're not realizing.
Jess Carter [00:37:12]:
And if you can look at it that way, you'll get way more incentivized about speed and accuracy, et cetera. Okay?
Jorge Sancha [00:37:20]:
Absolutely. This is what we see in the best companies we work with. They understand that, and they take that to the limit. They don't waste time. I mean, probably the worst companies in terms of data are those that are buried in data and they don't know what to make the best of it. And the best companies is they're very focused on what are the things that make the difference to us, what is really important to the business. Let's make sure that's fast, it's available, it's consistent to everyone in the company so that everyone's looking at the same data and they can make decisions over that data as soon as it's available.
Jess Carter [00:38:16]:
Yeah, I think you've defined data driven leadership in that comment there, that you appreciate the data as an asset and you want it to be quick and available for everyone who needs it to make the right decisions. Okay, so then I have to round you out as a person because we've talked about speed so much that it can almost sound right, like it's like we're machines. And so I've also heard that you have a pretty good jorge himself has a pretty good principle around work life balance or an ability to be present where you so when we talk about speed all the time at work, how do you juggle that with also being human and being somebody who's well balanced? What's your take?
Jorge Sancha [00:39:02]:
Think that's a great question, actually. If you look at Tinybird, this is not a company where people work to the death until the late hours of the evening or whatever, but we're pretty intense while we're working in the sense of not wasting time. And whenever we're thinking, hey, should we do this, should we do that, we always say, guys, speed wins. You make the decision because it's probably like 80% of the decisions are reversible, and it's better to make a decision, just keep moving. And then some things require more time. And then even myself, I'm not a young kid anymore. I can't work those many hours. I have kids of my own.
Jorge Sancha [00:39:54]:
So as much as possible, I try to balance work life. And during the weekends, I stay away from slack and I stay away as much as I can. And we ask people to be flexible the same way we allow them to have flexibility in their work hours. Because starting a business, it's complicated, and you have customers and they have needs, and you have to be there for them. But we take that very seriously as well. We see this as a marathon. We want to be in top shape ten years from now. We don't want to die in the process just because we're all so exhausted that it doesn't make any sense.
Jess Carter [00:40:46]:
I respect that.
Jorge Sancha [00:40:47]:
Yeah. But it's difficult. And I would lie if I tell you that I'm getting it right 100% of the time. Even though I stay out away from slack and all these things, my biggest problem is how do I actually disconnect? How do I close the laptop and actually say, hey, now I'm going to be present for my family, and I'm going to be present for that's. To me, the hardest things and the things that help me on a personal level is exercise. Just going for a run or cycling or doing some exercise and then just spending time with the family and listen to them and put them first in the weekend.
Jess Carter [00:41:34]:
Yeah, that sounds good. That sounds like a healthy balance for me. I think. I certainly am on a recovering side of 2000, 903,000 hours, years. And so it got to a point where I have two small kids, too, and it was like, hey, this isn't I had some life hit me in the face a couple of years ago, and I just realized somebody said to me, you're living your life like a video game. And at the end you get a prize. They're like you're missing that. This is it.
Jess Carter [00:42:04]:
This is the price. You have to enjoy this. That doesn't mean you work Jess hard, but it does mean, and I like your sense of when I'm working, let's work, let's get some stuff done. And speed does matter, and let's trust each other and make decisions. And most of them are reversible. I think that that's right. But I use the phrase like, be where my feet are. If I'm at work, let's be at work.
Jess Carter [00:42:23]:
If I'm at home, let's be at home. And I also get that right, not 100% of the time. I think it makes us good people that we get to practice. It's like yoga. You have a practice, we're practicing being present, right?
Jorge Sancha [00:42:35]:
Yeah.
Jess Carter [00:42:35]:
Well, Jorge, before we go. Is there anything else I haven't asked you about that I really should or we need to make sure we cover?
Jorge Sancha [00:42:43]:
Not really. Just that Tinybird is free to try and we have a slack community. I'm there. My co founders are there. I would love to get people to use Tinybird and give us feedback. That's really it. It's been great to talk to you.
Jess Carter [00:43:07]:
You too. And hey, if people want to follow you or find you, where can they find you?
Jorge Sancha [00:43:14]:
Tinybird is Tinybird. Co. The domain. Tinybird Co. on Twitter, and I'm Jorge Sancha on Twitter as well. I don't tweet much these days. I must stay. One of the things that I'm trying to do to make better use of my time is not spend a lot of time in social media, but the two places where I post something sometimes is on Twitter and LinkedIn.
Jess Carter [00:43:48]:
Thank you so much for being here today. Really appreciate it.
Jorge Sancha [00:43:52]:
Thank you for having me. It's been great meeting you, and enjoyed the conversation very much.
Jess Carter [00:43:57]:
Thank you for listening. I'm your host, Jess Carter. Don't forget to follow the data driven leadership wherever you get your podcast and rate and review, letting us know how these data topics are transforming your business. We can't wait for you to join us on the next episode.
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