Max Wahba

Clara (0:03): We are living in a world where every major business decision, who to sell to, where to expand, what to build next, is only as good as the data behind it. But here's what most people don't realize it yet. Not all data is created equal. The companies that figured that out early, they win. Welcome to Conversations and Data by Tech Accelerator. I'm Clara, head of people and today I'm sitting down here with the person who understands us better than anyone I know, Max, CEO of Tech Accelerator and partner at Data Sharp. Welcome.

Max (0:34): Thank you, Clara. It's good to be here with you.

Clara (0:37): Yeah, of course. Thank you so much for joining me today, Max. I'm very excited for us to chat a little bit, learn a little bit more about you, your experience as a CEO, as a partner, a little bit more about the world of data, and yeah, see where we can learn today.

Max (0:53): No, that's great. And for the audience who may not know, so I've been working with you, Clara, for quite some times. So I think it's funny to do this podcast and uh yeah, I'm very excited just to share with the audience a bit more about what I learned in the past few years.

Clara (1:07): Yeah. Yep. Absolutely. And to start, Max, um for anyone who doesn't know you yet, who are you in your own words? Can you tell us a little bit more about that?

Max (1:15): Yep. So um I'm Max. I'm originally from Belgium. I'm 27 years old now. I moved to the US around seven years ago actually no it's 10 years ago now and graduated five years ago um where I decided at that time I decided to launch sort of a business in the data space which at the time was sort of a reseller of business data and over the years we have evolved quite a lot with the market as you may know and we'll share more later with the audience.

Clara (1:45): Yeah, absolutely. I've definitely been a witness of how much we've grown. And it's so funny that you said, you know, oh, seven years ago, and then you said 10 years ago. Sometimes we just don't see time pass. And then we're like, actually, it's been 10 years, not seven. Exactly. Thanks for sharing that with us, Max. And um, what did a younger version of you think that you would be doing right now? Did you ever picture yourself here doing this?

Max (2:11): So, the podcast, no, but the business, yes. So I like to think I'm still young even though I'm starting I it used to be exciting. I used to be this 23 24 year old CEO of this big data company. Now I'm slowly getting to the age of the market. I'm seeing many people that age doing this which is exciting. A younger version of me I would say I've always wanted to be an entrepreneur. It's been in my family bloods. Basically my whole family has been entrepreneurs. Would have definitely seen myself you know running a business. What kind of business? I don't know. Data fell into my lap in a way and then I just really like the space. So I would say where I am now, I couldn't have predicted it, but I could have seen myself where I am today.

Clara (2:54): Yeah, that's so interesting. It's only a few people. It's not everyone who has this entrepreneur mindset and wants to, you know, build something and build it from scratch. And, you know, I remember 2, three years ago when I joined and it was, you know, nothing like it is right now. So it's very cool to see it develop and um very proud of you always of what you've been doing. So thanks a lot.

Max (3:16): Thanks so much.

Clara (3:20): Yeah, of course. And then on the team side, you know, we have a lot of employees. We're always trying to chat with you, you know, get a little piece of Max to see what's up. But what's something that the team probably gets wrong about what you're actually doing all day?

Max (3:36): Yes. So I would say early on it was a lot more I was a lot more into the trenches. So I was basically running four position at once, right? I had to be the CTO, the CFO, the CEO. I had to do so many things and I had people helping me in each side. I would say now it's a bit more managing the vision and also helping the execution still for the revenue growth which is pretty important to do anything else. But I think when people think that you're a CEO, they either think you're doing everything or that you're basically doing nothing and you're just telling people what to do. I think the truth is kind of in the middle, especially when you grow where you have to do quite a lot, but you have to be more of a generalist and being able to help a bit more. And I would say no. Something that people get wrong about me is I'm not actually trying to move on to a point where I'm just getting people smarter than me in the exact task that they do. So that I'm actually learning from them rather than having to teach them. You're a good example, right? Like you joined and I had no idea how to do all these recruitment things. We were doing it one way. It wasn't perfect. And then you joined and you just made that 10 times better. I know if I need something related to that, I'll just ask you an advice. So, I think that's what people get wrong usually.

Clara (4:55): Yeah, absolutely. And it's so interesting to see that, you know. Yeah. In the beginning, you were doing everything. You were doing the role of, I don't know, 10, 15, 20 people and then you asked me to join and then you're like, hey, now you have to find all these people that will do all these roles for me because I just can't keep doing this at this rhythm. And yeah, it's been a total pleasure to find our team and to build this team of such incredible people that we have here at Tech Accelerator. So, thanks for sharing that again.

Max (5:29): And Clara, I tell you one thing, maybe that would be a great way for the future to discuss AI. I used to tell investors or people I've speak with when they ask, "Hey, how did that came to fruition?" I used to say, "We did AI before AI existed." Because when AI didn't exist, we had to automate things in a very weird way. What it was our internship program, which I think we could maybe at some point chat about with the audience, but or some of these other things, we're just thinking of solution to automate things at scale and now that AI came in is just like an unbelievable I will say mixing of what we have done with what AI is not doing itself.

Clara (6:11): Yeah. No, absolutely. and it's helping us so much and helping every single person in our team to just scale up the amount of growth that we've been doing really in the past like four months I would say of course we've been growing for years but now in the past four months it's been everyone locked in and using all the tools and just using it to the max you know so definitely we'll chat more about that but um let's talk a little bit more about your leadership journey you're now the CEO of Tech Accelerator and partner at Data Sharp, the company that acquired us very recently back in November. How did you get here? How did this happen?

Max (6:52): Yes. So last year we were kind of at an inflection point. So either we needed to go with venture capital money or we needed to be part of a bigger group because we're growing fast but as a bootstrap business sometime it's tough to scale faster and so I just came up with you know after sometimes looking at alternatives data really stood out first because it was one of the only real data venture being built and not just a venture investing in data but an actual data venture a whole world of data being created. the vision that Antoine and DD brought up to this with Strada at the time was extremely attractive to me. But the other thing that I realized is that the data world is more of a private equity game than a venture game. And so why I'm saying that is because as a private equity you are actually reliable on making sure that everything is ROI positive but also it helps you be better in scaling at scale and data is not a cheap product actually and it's still not today. I don't think this is something that will go out with AI because AI fuels AI and so it's better I believe to grow with more assets into the group and organically than just get you know a massive amount of funding from a venture capital and not know exactly what to do how to acquire. I just thought it was better structured and then partner of Data Sharp. Why I think it made mutual sense for data sharp and us to do this deal is because I come with the vision of what we can do and in selling data because we're a data hub where anybody can buy any type of data. So that is an insight that really doesn't it's very hard to find at the group level like there's people with quality and M&A and other things but in understanding everything about the data market all the different set the vendors the partners the why where why where are things shifting that's an insight that I uniquely have at least in the group and that I'm sharing with the group to scale it.

Clara (8:57): Yeah, no, thank you so much for sharing your vision of what that acquisition was and I completely agree data is feeding AI just like you said and it's something that it won't stop and it's the new boom right so we saw the AI boom in the past five 10 years and now we're going to see the data boom and whoever is leading it and we are there we're a group now we're a very powerful group of you know a couple companies under it that are really making an impact with massive customers that we have right now so yeah it's pretty exciting. Um but all righty cool and um what was the thinking behind the acquisition and what does becoming a partner at data actually mean in practice for you every day?

Max (9:42): Yeah I would say there is a difference a bit in my roles at data versus the accelerator. So at Accelerator, I'm very much still looking to grow the data hub, getting more assets into the hub, also training the sales team, making the marketing more perfect to scale up. At Data Sharp, my hat is a bit different there. What I'm doing there is I'm more helping on the M&A and vision side. So let's say a new acquisition come ups and it's interesting. You may have a financial view from people at Data Sharp, but I have a data view like is this asset going to last long if it's brought in-house? So I think that's more of my time and then also checking within data how to scale things across the group and I think you'll touch on it later but there's a few companies now in the group to help.

Clara (10:27): Yeah. Yeah. Absolutely. And I've been a part of it you know being head of people at tech accelerator and head of HR at data sharp. They are completely different groups and we have you know different purposes but at the end of the day we're all united and we work very well together. So there's a lot happening and there's a lot of different groups. Yes, there's an M&A side. There's the side that we're growing our marketing team and it's just been such a thrill. It's actually funny, you know, in the interviews. I have like 20, 25 interviews per week and then people always ask me like what's your favorite part about working at, you know, Tech Accelerator, Data Sharp, and all these groups. And I'm like, well, we all get along and we just are trying, everyone's trying their best and everyone is working really hard. So, we just find ourselves in that and it's just really interesting to me to hop on these and share more.

Max (11:23): And I would say that's a bit an off question. You haven't asked yet. I just want to say it for the audience. One thing that I've learned well of how you can get good people to join as a CEO of a group is to make sure I think young and motivated is always better than experience alone. So experience can be gained and it's important. But I believe that when you're young, you're motivated and you're smart and you want to adapt and you want to learn, you have the ability to push mountains and actually at the moment you gain the experience that someone else had, you'll still have that high level motivation because you have achieved these things. And so one thing I've actually given to like I've told other CEOs and it's a culture I'm trying to bring into the group is whatever the seniority is whatever whoever we hire the goal is that when they come in they still have that young fresh and motivated mindset and that they still want to push mountains for the group and for themselves like they want to grow themselves.

Clara (12:18): Yeah. No, absolutely. And I completely agree with you. And um when you hired me, I was like what, 23, 24? And you're like, "Hey, you're going to be responsible for building the entire recruitment and HR team of Tech Accelerator." And here I am. Here I am. You know, I remember in the first month of the job, I was working like until 10:00 p.m. building the entire thing from scratch. And yeah, it's definitely a priority to bring in people that are motivated. I don't care if you've done a gazillion things. I just want you to be hungry, motivated, because that's what we're building here. So, thanks for touching on that with me. Yeah. And um now a little tricky question for you. Was there ever a moment during the acquisition where you thought that I don't know, maybe this might not be the right move right now?

Max (13:16): I wouldn't say not the right move, but I would say during the acquisition, that's something I learned the hard way. There's a fire to manage every second of the day. So, you know, you're because you're still running a process. At the same time, you have to run a business. The business is not static. So, things change. When things change, it can also change things within the M&A. It's a constantly moving piece. It's like a marathon. And sometimes very tired. And at that point, you may think, okay, like is it the right fit? I believe as a general vision, I always thought this was the best option in order to really do something impactful in the space. At any point when I was feeling down about something, I'm like, okay, but you know what? If I don't if I want to zoom out and I don't want to look in the next two months, but in the next three years, I need to do this because this is really what's going to get us to have an impact in the space.

Clara (13:59): Yeah. Yeah. No, definitely. And you know, you're sharing your vision of how you felt and maybe if there was any hesitance from you on this acquisition and I remember us as employees, we were like, are we going to like this group? Are we going to get along with these people that are joining us? And then in the first couple of calls with Antoine, Dier, Natan, it was just like such a good fit. It's been going so well. So yeah, I'm glad I'm glad you took that decision and you made it happen. Great. Awesome. All righty. Cool. for you know talking a little bit more about this acquisition this group data sharp for the people who might not know the full picture can you walk us through data sharp and the companies under its umbrella what does each one do and how do they all fit together?

Max (14:45): Yeah, so the first one I would say is mapap so mapap is based in France they have two location one in Paris and they're sort of like a I would say for the audience who doesn't know data they're like a mini Google they're like a they're competing with Google map in a way not really but they have sort of a location intelligence product that is extremely powerful and accurate also they also have very strong technical capabilities so that is what we have I will say in the location space. Then there is infoel and I'll tell you where it overlaps with mapap but infoel is sort of a business intelligence vendor and also has intelligence on the places of the business. So also all the location of the businesses. So on the location side it's pretty there's a lot of synergies actually with mapap and on the business traffic side that's actually where there is a lot of synergies with us at the accelerator which is the third company of the group and the accelerator is sort of a data hub for all data and it's pretty tough sometimes to explain it to a third party but really we're a place where you can go and buy any data whether it's business data weather data data on audio data on the trade between countries data on the stock market. I mean you name it. Anything you can think of we have it on accelerator and our relations with infoel is actually that infoel was one of the first sources that we use as we aggregated product in the business data world which is one of the main categories we have and so that's a bit how we intertwined our assets on top of you know mixing our teams when we need.

Clara (16:25): Yeah, that's so interesting. And I always tell on my interviews, you know, and I'm sharing a little bit more about our companies to them. When I talk about Tech Accelerator, I tell them to look at Amazon and look at Tech Accelerator and we are doing the same thing. Tech Accelerator offers all these data sets and Amazon offers anything that you can find. So, thank you for sharing that with me, Max. And um cool. Well, now where do you see Tech Accelerator positioned within the data market? We have a lot of competitors. What's our edge besides you know yes we can offer any type of data.

Max (16:57): So the edge is there's a multi-edge. So the first edge is when you're someone in data acquisition. First of all the data world is messy. So it's all over the place. The accelerator helps centralize your needs in one place where you can come in and buy all this data but with a single license instead of having to contract with different vendors country by country. So that's one. It's just easier to use. The second edge I would say is the compliancy because we make sure that the data set comply and you don't have to check it one by one and having to figure out with pretty heavy fees also how you can can you even use the data that you got. And I would say the third edge is also commercial because we either license or aggregate the data at scale we tend to be cheaper than a source would be to an end user because we just sell these data sets bulk and at scale. Where do I see ourselves in a few years? That's the second part of the question. I believe that we can be really the main underlying source to AI. AI really does two things. You need GPUs and computes and you need data. I mean they need other things but these are the main things. And when they need data, they need a lot of data and very different type of data. So I would see ourselves as a really strong underlying source for data. And then for the rest of the market, I just think we can be a great way to build new products and do new things and have an edge on your products. And now with AI, we're seeing some crazy use cases. We see three people in their bedrooms creating massive companies with not many employees with AI automation, but the only thing they need is private data as an edge. And if we can continue to fuel them, I think we're making people successful and will be even more successful as a company.

Clara (18:43): Yeah. No, absolutely. And there's always that talk about, you know, the replacement of people with AI, but at the end of the day, when it's a data company, we still need, you know, we still need our employees to run things. So, that's a good thing about, you know, employee employment security within data. But yeah, it's very true. There are people in their bedrooms that only have a MacBook and an AI tool and they're building companies. They're making hundreds of thousands, millions of dollars. So yeah. Things are changing and they're changing extremely fast. Thanks for sharing your vision on that. A data hub, we've talked a little bit more about comparisons and things like that. What is a data hub?

Max (19:25): A data hub is a place where you can buy, test, and acquire any data basically.

Clara (19:31): Okay. Simple, easy, not very hard to understand, right?

Max (19:35): The what we do is pretty simple. Then it gets complex when you actually you have to look which data we have, how you test the data. That's more complex, but it's pretty easy to explain.

Clara (19:44): Yeah, data hub easy. The rest, whatever is behind, let's leave it for a next episode cuz then that'll take us an hour. But um cool, Max. Have you seen a shift happening in the data market over the past few years? What has changed? You've been doing this for a while. Within all these years, what has changed the most, you think?

Max (20:05): I would say the past year and a half has I have seen a massive amount of change actually. So we're used to see I would say a lot of stability even in the data market where people would just use a data set get updates put it into their products and continue with it for some products it's very much still the case right like there's some very successful SAS that cannot run without good proprietary data and those company needs constant updates of the data and that is a need that's not dying it's actually more challenging because SAS are having a tough times the tech world having a tough time but data is still like a must have to have an edge in the world of technology. I would say where I've seen the biggest shift is we're now seeing a lot of new use cases on how people want to use our data. first on existing assets that we had but also we started getting requests for new data on the marketplace or on the data hub that we didn't have before and I can give you a few example but we're getting now a lot of demands from AI companies to get things in the audio space in the visualiz space in the graphic space these were data sets we had we were selling but it was never such a strong use case and we're just now seeing an explosion with AI. um we're also seeing now that much you don't need to have as many engineers, 10 engineers to actually buy data. You can just have four or five people have good tools and get the data and then know the data can, you know, the tools can analyze the data, can create things with the data. I actually do think now that the data is a bit more democratized to smaller company where they can actually I don't know if they can always afford it, sometimes they can't, but they can always use it.

Clara (21:46): No, it is an expensive asset as you were saying in the beginning. So yeah, you know, smaller companies might have a hard time having access to this data, but you know, any large enterprise, massive corporation, they couldn't survive 10 minutes without data or let's suppose the biggest governments on earth or even the smallest governments on earth, they need data. So it's just such a relevant thing. And now even with AI, AI needs data to be fed to AI 24/7.

Max (22:16): Yeah, good stuff. I used to have an analogy I would give in my early sales calls. I haven't said it in a while. I'll just share it with the audience. I say data is like water, right? When you sell data, you're selling water. And then once people get that water, they do things with it. They create Coca-Cola, they create Pepsi, they create bread. You know, you need water to do things. And we're sort of a generalist selling water, but then each one is putting their own sauce on top of the data, on top of the water and creating their own unique offering, but once you take out the water, whatever you've put on top of it, then it just the product doesn't hold, you know. So, this is really where I think we...

Clara (23:03): That's a great analogy and that's very interesting that you were using it and you stopped using it. Maybe I'll start using it because I really liked it. So, I'll steal that from you. But it's very very true. And then you were talking a little bit more about the data that we're seeing a boom and on sales and things like that. What is the type of data that we're selling the most right now and why is it there's so much demand for it?

Max (23:27): Wow. The truth is right now it's extremely decentralized. I would say it's really all over the place. Like if I have to look at the past nine or 10 data sales we've done, I don't even think two of them are from the same data sets, which just to show you how broad is the need right now for data. I mean, just in general, we're seeing just an increase into the data set that can be used for AI trainings. That's something that I'm just seeing a general increase. And yeah, that's the main thing. We're seeing actually a bit less public data needs because this is more democratized but much more private data needs in the market.

Clara (24:03): That makes sense. And I thought that I swear that you were going to say I don't know import and export. So I'm very surprised to see that it's been you know completely different completely all over the place. And that is the good thing about our company right? So we sell all types of data. So we can do all this and we don't have to be like oh sorry we don't offer this go to another company we just can do it.

Max (24:28): Also well I give something for the audience the only reason I didn't mention firmographics or trade data is because you ask me for the biggest change but this was always selling well it's been always selling well there's no change it's just an amazing product and because it's so private I think it will always say it would there would never be a big change people always need this type of data to do things.

Clara (24:51): Yeah, absolutely. And which one type of data is the hardest to get and what makes it so difficult to source?

Max (24:59): Oh wow. Uh this is a really good question. I would say any data that is privately owned by governmental agency is tough to get usually because you just need to have local governmental approvals every time and they don't grant it to everyone and you cannot use the data in any way you want. So these data sets are extremely complicated to acquire whether it's in a business registry, a consumer registry, a vehicle registry or any of the likes.

Clara (25:22): Yeah. No, that definitely makes sense. And for someone trying to build a startup right now, there are times that there's a boom in building startups and then there are difficult times to build a startup. What's the biggest challenge for someone trying to build a data startup right now?

Max (25:40): So the biggest challenge I would say for someone building a data startup is to have the barrier of knowledge to be broken. You need to have a lot of knowledge into the data and what you sell in order to sell it to the market because the market is now a bit more sophisticated than it used to be. And so if you don't have really a top-notch data set to sell or an edge into the product, it's very tough to make it work.

Clara (26:04): Yeah. No, that definitely makes sense. And a little a little question to break the data, break the acquisition. You are a young CEO. You've been doing so much. You've been growing so much. Be honest with us, the audience. What is something that keeps you up at night? What's something that's, you know, bugging you at night?

Max (26:32): I sleep pretty well. I have to say I like to shut off from work when I don't work. Usually I would say it's I like I'm a problem solver. So there's always open-ending problems at whatever point of the day. Whatever is the challenge from the last day, I try to be really focused on day by day what is there. Yeah. What also keeps me up is I like to see where we stand within a long-term vision. And so when I feel like we're behind or something of the likes, I just I can be pretty obsessive to get things to get things done. That could keep me up at night.

Clara (27:06): Yeah. No, that makes sense. And you know, that's great that you get some good sleep. That's very impressive. I think if I was the CEO and running the company, I'd probably get no sleep whatsoever. But yeah, you know, I'm definitely the type of person that also, you know, okay, I have all these problems today. I have a list of things that I have to do today to fix to solve. I am going to complete it every day. Now, Max, talking a little bit more about the future vision, right? We've talked a little bit more about you leadership changes what we've been doing what we sell now the future vision of our companies or groups what's next for tech accelerator and data sharp as well new data sets new hires definitely that I know but what does the next 6 months to a year look like?

Max (27:48): So the next 6 months I would say just scale up what we're doing great get more data assets into the group I would like also at some point to be in a point where we could create some new sets that don't even exist on the market and people will just be blown away. We could create data categories of our own that would be great to see at some point and just you know be focused on scaling up our infrastructure or team making sure everything is aligned. I think that for the next 6 months to a year and I think if we do that every week or every month in two three years and we continue to adapt with where the market is going we should be in a pretty good spot as a data hub.

Clara (28:30): Yeah. No, I agree with you and I see the company growing every day and I see what we're doing every day and I have no doubt that we'll we are there and that we'll get even further. So, completely agreed here and now talking about you know in a general sense of the data world and what's looking like what do you think it's going to look like in 5 to 10 years? You know, we see a lot of changes very quickly. We had no idea that whatever is happening now with data and AI would be happening right now. What do you think will look like in five to 10 years?

Max (29:03): I think it's very hard to say what can happen in 2 years with AI. I think everybody is kind of looking at it this unknown thing that is happening to the world. In 5 to 10 years, it's very hard to predict. But I still believe that there will be new industries being created by AI and that data will need to fuel that AI in order for the AI to be more performant. I also believe there would be very niche AIs that need very private data that will do some very specific tasks like even think about a doctor right like today a doctor you know is not like you don't yet have AI really linked to robotic in a way where a robot can do a surgery for you but I think you would want that if it does it at some point in 10 years that robot has the best data and it's not doing it based on general knowledge from like a code or a chat GPT right like it needs took up some billions of data points to be extremely precise and I believe this is where things are going to end in the next 5 to 10 years. Very precise tasks of AI that will need very precise types of data to fuel it.

Clara (30:09): Well, you said the this is the way that things are going to end. I hope that they don't end.

Max (30:15): It's to think about, but you know, you can at some point you adapt to anything that happens. It just happens slowly.

Clara (30:21): Yeah. Yeah. No, definitely. And um you know you were talking about doctors and yeah no we have some tools of you know doctors tea doctors and things like that but I've seen a lot about surgeries that are done you know remotely within you know remote controls have family members who have done surgeries with you know the doctor in another room operating in a brain. So you know I don't know where we're going to get there with AI but potentially potentially there won't be even a doctor involved. So just a lot of interesting things to think about and...

Max (30:51): And I would say people don't realize how much have already changed. Think about now most teams are completely remote being able to speak across the entire world. Yeah. It looks like a lot of the barriers of being like physically somewhere or doing something physically are one by one just disappearing. And I just think there's a natural progression that is happening. And yes, if we look at it in 10 years, we're like, I don't want a world like that. I don't want a world with robots doing surgeries and more data and AI. But I think if you want to rationalize it year by year, every time there's a change and there's something to adapt into the market, it creates some new things which is creating also new needs. The question for humans is just is AI going to change so fast that humans can't even keep up with the change because it just has such good data and my philosophically thinking is no because I think you still need data to be updated and data doesn't get updated at the same pace as AI. I think actually data is the safeguard of the never-ending change of AI because if data change more slowly, AI will change also more slowly or its impact more slowly which would give more time to people to adapt to the market.

Clara (32:13): Yeah, absolutely. Well, Max, those were all the questions that I had for you today. Is there anything that you would like to say, ask me or just make a closing statement for you?

Max (32:20): No, I would say it's been great. It's great for also the audience to hear and have this introduction into the data world. So, I'm just looking forward to the next few years together and just continue to grow and have some even more amazing guests on the podcast next time.

Clara (32:42): Yeah, absolutely. Yes, we will. And this has been such a great conversation. Thank you so much for being our very first guest on conversations in data by tech accelerator. This is a wrap up and we will see you next time. Thank bye guys. Bye.