#263 Tim Tutt on Democratizing Data Analytics, AI, And Bootstrap Success

0:00:01 - Mehmet
Hello and welcome back to a new episode of the City of Show with Mehmet. Today I am very pleased to have with me joining from the US, tim. Thank you very much for joining the show today. The way I love to do it is I keep it to my guests to introduce themselves, so the floor is yours Well, perfect. 

0:00:18 - Tim
Hey, thanks much, mehmet for having me on. Really appreciate it. My name is Tim Tutt. I'm the CEO and co-founder of a company called Night Shift Development. We build a data analytics product called ClearQuery. Appreciate you having me on today. 

0:00:31 - Mehmet
Thank you very much again, tim, for being here, so this is something I always love to do. I mean, I'm curious to know what was the motive for you to be interested in this field of data analytics and then, later on, to establish your own company. So, if you can like a little bit, tell me about the journey that led you to here today. 

0:00:52 - Tim
Sure, yeah. So I had a long background as a computer science major in college. My first job out of school was working at a company called Ndeca, which was a search and discovery solution. That was a company that I worked with right out of school. The entire intent was how do we help people filter and find interesting value in data? I worked on behalf of that company with some government clients and was doing implementation for large scale how do we find needles and haystacks, type thing. That's really what started the journey on data analytics in particular. I'd been kind of in the tech field for a long time, but this got me a little bit more focused in on data analytics and search and discovery type things. Yeah, great, I kept rolling. 

0:01:44 - Mehmet
Good, good, tim. So, tim, you know, like now everyone says like everything is about the data, and you know I'm trying here to simplify it because sometimes I have in my audience people who might not be familiar, not very tech oriented or though like it's a city or show, but I love to always to take one step back and you know, highlight, and of course, I have now the expert with me today. So you know, first of all, like why actually we need, you know, to have data analytics, why it's now it's not like a you know something, okay, it's good to have, it's something you have to have. 

0:02:22 - Tim
Yeah, you know, these days, data analytics really provides a major competitive advantage. So if you look at any kind of data that we collect because we collect massive amounts, things on user behaviors, things on buying patterns for individuals you know how are, you know, people attacking our systems, for instance all these things kind of roll up into data analytics where we're trying to look at and find interesting patterns so that we can make better decisions at the end of the day. So you know, if you look at pretty much any industry, you're collecting some amount of data and you're using that data to make a better decision, to improve your business outcomes. So, while a lot of people may look at things as kind of hey, this is my intuitive approach for how we drive how we drive business, we also need to have very, very specific data-driven insights. 

So you know, hey, more people in this category males, females between this age range buy these types of products. So here's how we need to actually, you know, here's how we need to market to those individuals. Here's what we need to do to get them to be enticed to buy other things. 

0:03:45 - Mehmet
Great, I know, tim, one area you would like to discuss and it's something part of, I would say, the mission that you are on is to simplify. You know another term like democratize. You know data analytics to be accessible for even non-technical users. So what inspired you to pursue this and how do you see that can be changing the landscape of business intelligence? 

0:04:14 - Tim
Yeah, absolutely. So. You know that started for me back in my days again supporting government customers. I played a role where I was the middleman between several hundred analysts and their data. I was going and writing queries against super computer. They'd come and ask questions. We wash, rinse, repeat, give them more answers. They have more questions, more follow-ups and really you know, at that point realize that hey, you know, I'm kind of the holdup here between myself and the 13 others that we were working with. 

You know we had a small team that was supporting a lot of analysts. So really we wanted to solve this problem where we got ourselves out of the way but also enabled us to kind of go and work on harder problems. So myself and my co-founder started the company really to focus on that issue. At the end of the day, you know, I believe technology is meant to make things easier for people and if it's too complex where only your most technical users can use it, we're not doing a good enough job here. So ClearWare really is designed on that principle of democratizing data analytics, making it simple for any user, no matter their technical skill level, to get value from their data. That means your subject matter experts don't have to go and learn a bunch of technical skills to you know, find the interesting patterns, develop the charts or make those data-driven decisions that they need to make. 

0:05:40 - Mehmet
Yeah, that's great and, you know, of course, especially in times like we are living at now, it's crucial that everyone is able to access these without spending a lot of maybe time and money on getting the I would say expensive stuff. But at the same time, I know you also focus on helping and empowering the data stakeholders, right. So first let's discuss, like, who are these stakeholders right and how you know the solution that you developed can help them. Whether you know I know you're going to mention who are the stakeholders, but mainly I mean maybe data analysts, scientists. So how, how you know you can empower them and what I would say, business outcome you know they can expect once they are empowered to deal with the data. Yeah, absolutely. 

0:06:43 - Tim
So when you're looking at you know data scientists, data engineers, in particular BigQuery does provide a lot of value there for those tech, those more technical users. You're talking about accelerating the time it takes for them to immediately triage that data. So if you're in the data science profession, if you're looking for interesting patterns for you to go in build your models on top of and BigQuery helps you find those without you having to go and write a bunch of code to do that or do a lot of experimentation. We're going to highlight some of those insights for you right now, right off the bat. As a data engineer and we actually do this for ourselves the first time we get a data set, one of the things that we look at is hey, how clean is this data set? Do we have data that is mismatched across the board? In one case we've got a data set where it said, hey, there are 63 states in the United States and obviously that's not correct, but that was because the data wasn't clean. It was hand jammed in, so we knew we needed to go and clean that field up for people to actually be able to get value from their data. It helps us solve this garbage and garbage out problem at the end of the day. 

So for data scientists, data engineers, for your more technical users, it really does help to drive some of those behaviors. If you really start looking at application developers or product managers, you can start analyzing user behaviors in your application in a very simple way. What features are being used the most? Where are people encountering the most errors? So that we can drive from there. Because you move a little bit down the stack to your data analyst and your subject matter experts in a particular arena maybe it's in marketing, maybe it's in sales they can start to ask these questions in very simple ways so they can get the value that they need. Hey, how many deals did we have that are coming from, let's call it, facebookcom in the last 30 days? What's the makeup of the individuals that were coming from there? And are those things converting? What sites are we getting the highest conversion rates from? And they're able to ask those questions without needing to go and learn SQL, without needing to have days and weeks of training on a particular platform. 

0:09:02 - Mehmet
Yeah, that's great. And out of curiosity, Tim, do you can deal with any kind of data source? 

0:09:12 - Tim
Yeah, so we have a couple of immediate data sources that we work with out of the box. If your data is in Elasticsearch, we plug directly into that. We can also pull from SQL, mysql, sql Server. But you can also upload a raw CSV or Excel file and start getting value from that right off the jump. If you have a data source that we don't already support out of the box, we can go and build a connector for it. We're continuing to build those connectors as we're building the tool here. 

0:09:46 - Mehmet
Yeah, that's very cool really Because, again, I was telling you before we started this episode that I work in consultancy and sometimes I hear a lot of initiatives and the question number one. I hear okay, what's the data source that you are using? Nowadays it's very fragmented, I believe. Back to bridging the technical gap. Of course, this is something you are doing with the team to bridge it as we say, but of course there are some challenges down the road. 

So what are the major challenges you see when you try to get this non-technical business users to start to understand the value, and what are the approaches you use to address these challenges? 

0:10:37 - Tim
Yeah, absolutely. When we first started the company and when it first launched ClearQuery, one of our core features was this feature that allowed people to ask questions in plain language. Ask questions in English hey, what is the breakdown of traffic by referral source type thing? It comes back to this beautiful chart and you're able to start interacting with that and drilling in. Now one of the first immediate pieces of feedback that we got from a very early customer was hey, great that I can ask these questions of my data, but I don't always know what to ask. I don't even know where to start. How do I move forward with that? 

So, as we kind of took a look at that, we took a bigger step back and said, okay, well, what if users didn't have to ask questions? What if we could surface interesting insights for them to start looking at right off the bat? So the capability inside of ClearQuery that we call automated insights it does just that. From the second you upload your data set, it starts doing some quick analysis and highlighting interesting, statistically relevant data points that you can start drilling into. Hey, did you know that the top referral source is Facebook and it's about 30% more often than Twittercom or something like that, so you'll get those insights right off the bat. 

0:11:58 - Mehmet
Great. Now, out of curiosity, do you use an LP, Natural Language Processing? 

0:12:04 - Tim
We do so. We started the company in 2017. So it's kind of been a core of our product offering here. But yeah, we're doing some natural language understanding to identify the intent and then some entity extraction to figure out. Okay, what are the things that we need to go and build the right query against the data source, run that against data source and, as that data comes back, we're able to kind of automatically determine the right type of visualization to show to that user. 

0:12:37 - Mehmet
Great. Now maybe I'm sure that you are prepared for me to be asking this question with all this hype and noise or whatever you want to call it about AI, Because people started to mix things up right between what AI actually is and the genitive AI and data analytics. But now, with all that's happening around us and we started to see, for example, Microsoft with the co-pilot approach and we start to see everyone trying to leverage the data, so where do you see the intersection between and, of course, a lot of part of it is data science, which everyone, I think, knows but from your perspective, what are you seeing the intersections? Where are you seeing things that are headed in the future, especially when AI comes to data analytics? 

0:13:32 - Tim
Yeah, absolutely. Look all of these things really. They're very much so all about the data. At the end of the day, data drives any model that you're building from a machine learning perspective. So when you talk to an AI, it's trained on massive amounts of data, massive amounts of text. 

How do we do predicting what the next right word is to answer a question? It feels like magic at the end of the day. It feels like your generative chat bots that are out there actually know the answer, but it's actually just doing simple math on the back end here. What's really interesting, though, is, with OpenAI and the rise of language models across the board large language models across the board we're starting to see these new trends of how we can leverage those things for data analytics. 

So, as a part of our own roadmap internally, we're looking at how we can identify not large language models, but smaller language models that we can embed in our application for focused, specific tasks. It's like, hey, I've got a raw PDF document or a bunch of Word documents and I want to get some analytics out of that. Well, sure, we can do the standard metadata extraction approach and help you analyze your file types, but that's not as interesting. What would be more interesting is if we can extract things like people places organizations out of that data for you so that you can analyze based on those bits of data points. Or how do we do things like translation on the fly inside of the application so that you don't necessarily need to know the source language of your data and you can ask questions and drill in and still get those answers in an appropriate way. 

So there's, a lot of intersection with how AI is. Machine learning models, I'll say, because none of this is really true. Ai how do you things interact with, data analytics in particular? 

0:15:43 - Mehmet
Yeah, just also again, you know what, tim? If someone comes from an engineering background, you're curious by design. I would say so. When I was trying the first time, when OpenAI released what they used to call it the code interpreter now they call it data analysis actually. So I tried to give some open source data sets and I started. 

I'm not a data scientist by any mean, but I took a course back in the days about Python and these things. So do you think, like here, for example, the AI can help us actually, and especially again back to the point of non-technical business owners or line of business so maybe it can help them to ask the right question when they deal with the data. So, for example, I gave a data set about the real estate in Dubai and they have to open data, the transaction and so on, and then it started to suggest to me what kind of things it can do. So do you see this as something can go more in the future and become also kind of autopilot kind of thing? So you don't need the layer of the data I don't not sure it's data scientist, but the layer which is before the data scientist. 

0:17:01 - Tim
Yeah, no, I think that's actually a really interesting point, and one of the things that we're also diving into. How do we get to these automated recommendations? How do we predict the questions that people are going to want to know before they even ask the questions, just based off of the data that they have? Here are the things that you should care about immediately, so short answer is yes. I think we're still a little ways away from that, though, and in the large part, it's just because some of these things require a lot of deep internal business knowledge, and, depending on how that business operates, it may or may not use certain techniques or use certain approaches, for how they run against. 

Things Doesn't mean you can't do it in a general sense, and I think AI is going to help with those things. One of the big challenges we have, though, is understanding how these models work under the covers. A lot of the things that you see today are very much so black boxes, which means that we're running into this issue of hey, how do I know how you came up with that answer, and if that's actually accurate? Did you give me a real answer that I can validate, or is this something that I am just going to have to trust blindly, and that's something that we really need to advance before people can rely on those automatic recommendations. 

0:18:20 - Mehmet
Yeah, great, one time I liked this approach, but when I was preparing I saw that you have, you know, like you did, software security also before, and so, of course, everyone concerned when it comes to data is about how we secure all this. So how important is also like, while dealing with data, to keep it secure and you make sure that you conserve the privacy and all these things. So if you can shed the light a little bit on these important aspects also, which usually people they don't talk much about it. 

0:18:56 - Tim
Yeah, absolutely no. Keeping data secure is immensely important, and this is actually one of the big challenges that we're seeing right now with OpenAI and ChatGPT. You have this issue very early on, where people were feeding their corporate data and there were two major leaks that occurred where companies' private confidential information was being leaked to the public because they were feeding this into ChatGPT. Now what the immediate, neutral reaction is from most companies is to say, hey, no one is allowed to use ChatGPT, no one's allowed to use OpenAI and I don't know that that's necessarily the right answer, but it does become a bigger question of what data do we want to share with these open platforms. 

One of the bigger challenges with things like OpenAI's setup is, hey, it's an open API. It requires a lot of resources to run. So in order for you to leverage it, you wouldn't be able to pull that behind firewall without spending a lot of money on a number of different resources. So people have to find this balance between what's actually secure, what do I really care about, and what can I do to get value from these platforms at the end of the day. So one of the things that we focus on with our product in particular, you can deploy it behind firewalls in public private clouds. 

We never have to see your data at all. You don't ever have to share anything with us, and that's in large part because of the techniques that we're using for the natural language understanding and intent understanding. They aren't massively resource intensive, so it allows you to have a lot of that security and privacy and your data. I was actually just reading an article last night where the new chat assistance that the ChatGP has open. There are certain ways where you can ask questions and say, hey, I want to get the raw data that the author used to build this in the beginning and ChatGPT will return that for you. So lots of concerns with how you start to lose your competitive advantage that way. If you're losing the data that you use to build out this model, that becomes more of an issue for you in general. So data security and privacy is a major, major concern in all analytics solutions and it's something that we always recommend that people take a big, deep look at when they're trying to determine what platforms and what tools to use in their organization. 

0:21:45 - Mehmet
Yeah, and, by the way, Tim, just as a side note, because of this approach that you took, I can tell you, if one day you decide to expand in my area here, they would love it, because although we have the hyperscalers, they have local data centers and so on, but in some and of course I know it's in the States it's the same in Europe, with the GDPR is the same for some industries the data should stay within. Yeah, so this is big plus, I would say for you. I can tell you this right away. But you just one thing about, because you mentioned something about the search and you mentioned elastic search. Right, is this something that when you deploy the solution, that can happen in the background? So again, I don't need to be a wizard or an expert in elastic search and all these terms. Is that right? 

0:22:46 - Tim
Yep, that's exactly right. So what we're doing and we provide as we're- setting it up. We'll set up that elastic search cluster for you as a part of an implementation, and that's usually a very quick implementation. But one of the things that we do is we're actually translating the questions that you're asking into elastic search queries and everything that we're doing. We're building that on the fly for you so that you don't have to even know what elastic search is, or even how to spell elastic search at the end of the day. 

0:23:19 - Mehmet
Yeah, great to know this, Tim. Now I want a little bit to shift gears and talk about the entrepreneurial aspect of your journey, tim, which is honestly, it's very fantastic results and knowing that you bootstrapped the company and achieved 160% of really growth so I'm sure this is the dream of every founder out there. So, first, why you decided to bootstrap and not raise any funds in the beginning and the second thing, what were? I'm just asking this question to shed some light also for other founders who the first thing they think about before doing any other task is going and raise funds. So if you can give us some hints and strategies that work for you also as well, yeah, absolutely. 

0:24:17 - Tim
So starting the company and when we were starting the company, both myself and my co-founder we wanted to maintain control over the direction, the technical direction of what we were doing and also kind of the internal strategies for how we were going to build up the company. And when you take on money, one of the big challenges that you have is you now have other bosses and those other bosses are very focused on growth, growth, growth and growth at all costs. We wanted to grow, but we wanted to grow in a way that was scalable for us. That enabled us to kind of say this is what we want to focus on now we can focus on the customers and we don't have to focus. We can focus on solving these problems that those customers have and build interesting cool technology to solve those problems very quickly. That was really kind of the driving factor from us. 

Bootstrapping Also allowed us to kind of maintain ownership of that company. The way we kind of went about it was we started off very much so doing heavy services work in the beginning, helping people with their data and manually going through and helping them process it, do the data engineering work, all of those different types of things to kind of keep the lights on. The reason the name of the company is Night Shift Development is because we were kind of working these day jobs to kind of pay bills, keep lights on and then at night, building out product. 

0:25:44 - Mehmet
Wow like this is really inspiring them. 

And yeah, I'm sure at some point I asked anyone about why you bootstrap. They come up with similar answers it's about not being under the pressure of investors. And second, yeah, not all the time I believe you need, you don't need really sometimes to get funding, especially if you have some. You have your runways and you have your customers. One thing I'm interested about Tim so for you, you are in a, the company is in a hot vertical, I can say so, everyone needs this, but there must be something that you kept hearing from customers that also encouraged you that. Okay, I should continue, but I'm curious in other way, if I want to ask the question how did you validate that yes, there is enough market for what you're trying to do and actually customers need this? So again, what strategies have you used and how you were able to know that, yeah, I have product market fit actually. 

0:26:56 - Tim
Yeah, this is actually one of the interesting things. When we first started the company, it was very much of this game of okay, we have to be secretive about the product that we're building and all these different types of things. One of the things that I have learned and that I continue to preach now today is, when you've got an idea, you should talk about it all the time to everyone, and the more people you talk to, the more feedback you're going to get and the more you're going to see whether or not you're really solving a problem that people care about or not. Go out and demo. Go out and show things at conferences, and that was one of the things that we did early on. I was showing early prototypes of Clear Query at conferences, which allowed us to really drive and focus on what things that we needed to continue to build out. 

So getting that customer feedback really drives where your product development efforts go in the early days and before you actually have a paying customer, you're really focused on the feedback of people around you. That said, you also have to have that conviction that what you're building is the right thing, as you will hear a lot that I don't know that this is necessarily needed or how is this different from something else, and you have to be able to have that conviction to say no, this path is different. 

And even though I'm hearing a lot of you know, hey, no, or I don't think this is great or you're wasting your time, type thing, I have to know that this is the right path and we have to keep driving on that. 

0:28:31 - Mehmet
So it's about, I would say, persistence also as well, and perseverance, right yeah, but definitely like this is very inspiring, tim, I would say, your journey and the success that you had, and it's a proof, I would say, that guys not always go the same route, that it's not for you, because I can see, tim, that you choose something which is unique for your offering and for your vision and mission with the co-founder of your company, which is great. And I think here which is also I want to hear your opinion about now, when you are working in a corporate job or you have a day job, so, of course, like you start to think about okay, I need to have a leadership skill, I need to be innovative, but when you are working on your own, as we call it, baby actually, because this company becomes your baby. So which leadership principles you try to focus on and foster within and again, to inspire your technical team and get this business really to this great success. 

So what are the key principles you have used and you have seen successful growing the company? 

0:29:54 - Tim
Yeah, the short answer there is all of them. Now, I think there are a couple of key ones, in particular One what we look for is hustle when we're hiring. I'm looking for people that can really, even if they don't necessarily have a skill set right off the bat, I have the conviction and the confidence that they will be able to go and learn that and build off on top of that, that they will be able to do what they need to do, that they're really interested and passionate about a project or something that they are involved in and that they like solving problems in interesting ways. 

But there's this other big thing that we really focus on and that's giving people the freedom and space to operate without them needing to necessarily mother, may I? So if you've got an interesting idea, go and try it. We want to enable, we want to allow our team members to kind of go and try things and bring new ideas to the table, even if they, as everyone, knows what the core vision is. So being super transparent is a key facet for us. Being super transparent and enabling our team so that we can all drive in the right direction, these are kind of key things that we focus on as a leadership team. 

0:31:10 - Mehmet
Great and I believe them also. Maybe, if you can shed a little bit light, I hear always from founders that you know the thing that made also this work for them is themselves being able to learn skills like sales and marketing and this stuff. So how important this and especially for like technical founders like yourself, tim and I know from myself because if you asked me like 10 years back or 12 years back about sales marketing, I knew nothing so how this is very important also when starting your own company. 

0:31:49 - Tim
Yeah, you know, one thing I always say now is CEO's number one job is sales. Yeah, technical, founder, technical background never really done sales before. A lot of it was, you know, learning on the fly. You know, I think there's a lot of different sales approaches. The sales piece is the most important thing, because your job is revenue. At the end of the day, and especially when you're bootstrapping, revenue matters above all else. 

So how do we get in front of the right customers? And a lot of this boils down to hey, I am asking and talking to people. What you'll find is, when you start a company and this is, you know, definitely my experience I reached out to almost everyone in my Rolodex and said hey, I'm starting a company, here's what I'm doing. I'd love to talk to you about it if you're interested. And everyone is willing to help out in some way or another, whether or not they become a customer. They may become a customer, but they also may just know someone in their network that needs what you have, and that becomes a very, very important facet of all of this. Learning how to do the marketing piece is also a very, very major issue, because that's where you start to scale. You have to. You know kind of get the sales motions down first, but then okay how do we market this so? 

that we can scale this in a very effective way. 

0:33:10 - Mehmet
Yeah, great, great points. Now, you know I stopped to introduce these questions, maybe they are a little bit cliche, but I'm curious also myself. So did you have Tim, like any idol founder in the tech space that you said, okay, like you know, he's, he's or she's my idol? Did you have someone when you decided to start your journey? 

0:33:33 - Tim
I don't know if I had one specific kind of idol, but I had a lot of founders that I leaned on their experiences from as I was starting my journey. You know, obviously you know Jeff Bezos, marc Zephyr Berger, always you know very interesting ones and their founder stories are, you know, very fascinating. In particular, there are others, like you know, marc Randolph, one of the co founders of Netflix. You know those are really interesting stories and seeing how those journeys kind of played out for them is very fascinating. 

0:34:07 - Mehmet
There's a book that. 

0:34:07 - Tim
I always tend to recommend. That, you know, was also a big piece of my journey as it's getting started, called the Hard Thing About the Hard Thing, the Hard Thing About Hard Things. But you know that book in particular. I just provided a lot of interesting founder stories and challenges that you'll run into, but provided a lot of good tips on how to engage and work with those challenges as you're moving through and figure out what the next best move is. I go back and refer to it all the time these days. 

0:34:46 - Mehmet
Yeah, it's good because you answered the second question about a book if you would recommend and it's a good recommendation actually you know, like also how important Tim is to have a mentor as well. 

0:35:01 - Tim
Yeah, I think you know it's definitely important to have outside advisors and you know whether it's, you know, a specific mentor or it's a number of other advisors that you can lean on. For me, I had a number of folks that I've worked with in the past that had, you know, provided some advice. When you know I'm running into a hard issue, when I'm running into an issue on, hey, how do I get this deal closed? I'm running into this issue with this customer. Or you know, hey, I'm dealing with this issue with this employee. What's the right way for me to approach this? How do I help make this better? Because I'll tell you, you know people, challenges become one of the big things when you start growing a company and that becomes a really interesting thing, dynamic, to try to navigate. 

0:35:45 - Mehmet
Yeah, that's great. And you know what's the vision or the long term mission that's called Tim for for, for you, I mean for the company. 

0:35:55 - Tim
Yeah, I mean we're going to continue on our overall mission of democratizing data analytics. How do we make this simple as possible for anyone to get value from their data? You know we're trying to take down some of our bigger competitors those like Tableau and Power BI, etc. And we really start to eat up some of that market space because we really do believe we have a differentiated solution that enables people in a very effective way to get value from their data. 

0:36:23 - Mehmet
Great. You know I get excited. You know I'm passionate, as I was telling you before we start like, about startups and scale ups. I think you are a scale up now, not set startup anymore, but I can see you still have the spirit of a startup founder, tim, and a lot of things you shared today. Now, this is not a tricky question by any mean, but is there anything that you know I should have discussed with you? I asked you and I missed it. 

0:36:53 - Tim
You know, I think we covered a lot of ground here, a lot of good, interesting areas. I think one thing I would quickly note is you know, listen as you're kind of if you've got entrepreneurs on the on your listening list that are kind of going at this, one of the big things that I always say because this is the hardest part for anyone is just start getting off and go is the hardest part. Once you get off, go, you start to figure everything else out. You don't need to have it all figured out from the beginning. Just start and you'll figure it out as you kind of move along. 

0:37:33 - Mehmet
Yeah, exactly, you know, this is something I also repeat and I tell them. Take me as example. I didn't start early enough. I did the mistake, you know, and just go start. This is what I tell people also as well. Tim, like, really, I enjoyed the conversation today. Before you know, we close. Where we can find more about you and about Night Shift. 

0:37:53 - Tim
Yeah, absolutely so. If you're interested, you can head over to clearqueryio. That's our website. You can find out more about our product and Night Shift there on the website. I am on all of the socials as Tim Tut, so you should be able to find me across all the platforms. So one exception is on. I guess they call it X now. I am Tim F Tut there, but pretty easy to find me around. 

0:38:23 - Mehmet
Yeah, honestly speaking, usually when I put in the show notes, of course I will put the company URL and I put the LinkedIn profile only because I believe you know, like it's kind of a professional I use but I'm not so active there, honestly. So, let's, let's the guest ask me can you, can we put also like the profile of Twitter or X, whatever we want to call it? I said, of course, like I have no issues, but, yeah, so LinkedIn also is the place for me where people can find me more. Of course, I'll put the you know the links in the show note. And again, Tim, thank you very much for being with me today and I enjoyed the conversation and I'm sure the audience, both who are interested in data analytics, they will. They will find a lot of you know knowledge in that and, of course, the bigger audience for me, which are entrepreneurs and startup founders, they will also find a lot of useful information and this is how we close the episodes usually. 

So, thank you for everyone who tuned in today. If you are a first time, you know visitor here, thank you for passing by. I hope that you become a loyal fan and for the loyal fans, thank you again for all your feedbacks, for your messages and for your recommendations and suggestions, and if you are interested to be on the show, like you, have this super cool idea, you have built something and you are not finding the space of the PR there, so please reach out so we can arrange a session. We can do a recording, same as Tim was on the show today. Tim's on is not at the front, so I have audience globally. I can say this, I can claim that and, yeah, thank you very much for tuning in. We'll meet again very soon. Thank you, Bye-bye. 

0:40:08 - Tim
I'll try to name a minute.