The Data Cloud Podcast

Selling Corona During the Coronavirus Pandemic with Ari Margalit, Global VP of Architecture & Data Solutions at Anheuser-Busch InBev

Episode Summary

This episode features an interview with Ari Margalit, Global Vice President of Architecture & Data Solutions at Anheuser-Busch InBev. Ari was previously the CTO & Head of Solutions at WeissBeerger and Vice President of R&D and Israel site general manager at Jive Software. On this episode Ari talks about ABI leading the CPG industry in AI, the importance of being cloud agnostic, the impact Coronavirus has had on Corona sales, and much more. So please enjoy this conversation between Ari Margalit, Global Vice President of Architecture & Data Solutions at Anheuser-Busch InBev and your host, Steve Hamm.

Episode Notes

This episode features an interview with Ari Margalit, Global Vice President of Architecture & Data Solutions at Anheuser-Busch InBev. Ari was previously the CTO & Head of Solutions at WeissBeerger and Vice President of R&D and Israel site general manager at Jive Software.

In this episode Ari talks about ABI leading the CPG industry in AI, the importance of being cloud-agnostic, the impact Coronavirus has had on Corona sales, and much more. 

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Episode Transcription

Steve Hamm: [00:00:00] hey, Ari. Good to meet you. And I'm really intrigued to see that photograph behind you and on the zoom call. Can you tell us a little bit about that? If it looks like a big brewery?

Ari Margalit: [00:00:11] Yes. , I took that photo in, uh, AmBev brewery and Jaguar Ruina. so, yeah, one of our breweries

Steve Hamm: [00:00:18] And that's that's in Brazil. Okay,

Ari Margalit: [00:00:20] One of our breweries in Brazil.

Steve Hamm: [00:00:23] What impressed you so much about that brewery?

Ari Margalit: [00:00:27] First of all, if you go there, we did a, some tour. if you go there, they have an amazing wall where you see the evolvement of AB InBev. so at the end, you see how this monster company was just, started with a one, one, one brewery, of a Brahma in Brazil. So you see I'm so where, where I was, this used to be a brewery of , Antarctica.

It used to be Brahma's biggest competitor in Brazil, and then they merged. So this is how this great company started. And I went to that brewery. it was super exciting to see, you know, and also you see there, like how technology meet, you know, classical, brew mastering. So it was pretty cool.

Steve Hamm: [00:01:14] You mentioned some of the subsidiary or, or, or previous companies to Anheuser Busch InBev there. Maybe it would be good if we started off by you describing it. I mean, everybody knows you're, you know, in the beer business, but if you could Describe the sprawling global company with all these brands.

I think that would be helpful to the, to the listener to understand, you know, the business and its various dimensions.

Ari Margalit: [00:01:42] So, first of all, just to, to understand AB InBev is a, is a very big company. It's a 180,000 employees company. I think, uh, lately I saw the number of MPS. We're having Microsoft. I think it's smaller. so this, this is huge. and it's, it was made out of, uh, M and A's and many companies coming together.

people that came from in investment banking, just, you know, taking right decisions and, and buying companies. And at the end it became a . So I think the journey started with AmBev that merged with InBev. So I'm bevel as a Brazilian big company. Didn't they merged with a beer company from Belgium called inter brewery.

And then they,  joined together to become InBev. Then they saw, they bought 'em and I was a Bush. Right. which was, the, the biggest, American beer company. So, and I was a Bush with InBev became and as a pushy InBev, they also along the way bought, uh, several other breweries, including Modelo in Mexico.

So this is a huge, beer company, but it's actually a joint, family of, of breweries that came together. and that's it.

Steve Hamm: [00:02:54] Yeah.  I think it would be helpful if you, , just list off,  the four or five or six most well known, brewery brands.

Ari Margalit: [00:03:01] yeah, our three most known global brands are a Stella Artois, a Budweiser. And Corona, right? We all know those brands. I think the next one in lines that are like globally known are a laugh and, uh, or garden, which are like our Belgium brands. And of course, Michelob, ultra, they just becoming very big in North America and Europe.

So, and, and we have many right, that are more, I would say more of a regional. That are very known, like in South America, you have the Brahma and this call, et cetera.

Steve Hamm: [00:03:39] , I also have a sense that, you know, we had that huge phenomenon, the microbrewing phenomenon that came up over the last 20 or 30 years or so, which has really changed the landscape for brewing. My, my sense is that your company has kind of both does these big, broad, you know, crowd pleasing brands, but also has some microbreweries and things like that within it.

Ari Margalit: [00:04:03] Yeah, we've got good silence for example, which I love that bear. Right? A very strong one. Actually I think, uh, all garden started like that. So we have our own like, home grown craft beer, or we have are the ones that we were buying in the last few years. There is the part of our company called Xanax ventures.

They're buying craft beer and, and joining them and, and maturing them. And once they become like more than acts, you know, in revenue, then they turn them into a global brands.

Steve Hamm: [00:04:36] Now you've been there for about a year and previously I know you were at a vice Berger and that is a data analytics software company focusing on the beer industry. So what are the particular challenges and opportunities in the beer industry that could be addressed by data analytics?

Ari Margalit: [00:04:54] that that is a great question. So, as I told you, ABI is a fortune 500 company, a world leading CPG accountable for 40% of the beer consumption and 400 brands of beer. Right. And they're going through and transformation. I think that many CPGs companies and low, low tech companies, you can see it happening with McDonald's.

We can see it happening with Domino's pizza, et cetera. they're fighting the fight of like digital transformation, the beer category, I think, uh, in the Westworld is going through a tough time. Like, fighting with, leakers and other spirits. and at the end of the company needs to show a growth thing, bottom line and growth in the beer category and stuff.

So wearing years in which, you know, we're trying, to transform ourself, right. To become more digital. we think that will bring us that growth. So, and when, when do you want to, to become more digital? The growth  has a link to what you do with data. So, data is, is as a decision-making, In front, or I would say data as the base for decision making gets more weight.

and with the addition on top of that, I would say AI and ML, right? AI for artificial intelligence and ML for a machine learning is becoming a. a big thing. So ABI wants to become number one in leading those, this technical transformation, becoming the number one CPG digital digital company, and a leader and a shaper.

So bringing advanced analytics on top of data. As a core capability, eh, is becoming very big. And we started to adopt best practices in that field, I think couple of years ago. And now in the last year, it's been accelerated dramatically since I joined, as data is now treated as a corporate asset. We have so much of it.

You know, we had to put a really like a structure in it. We had to bring it, in different ways to different personas from hundreds of data sources. For the story I told you about how this company was created. We had to create our global master data, which was like a mass with like many companies coming together.

And most important create is like, What I call full feedback loop of data between old business function of the company, and then unleash the power of AI. So all of that was, was based on top of a global data platform product that when it, since I joined, we initiated and I lead it's called brew. That. you will hear me talking about a lot and we have AI labs best R and D test and learn new algorithms approaches in the company like, Hey, appearing everywhere.

And, and we're trying to bring all of them on top of Buddha. so this is what I'm trying to do. we adjusted our operation model to be more and more driven by AI. We started to work with predictive models to forecast the future. And we created the  multi-country data and analytics, talent and culture.

So pretty exciting times. And today we realized really we start to realize the full value of what it means to have a global data platform, what it means to, to run AI and run business with AI. So a long, a long answer, but I tried to

Steve Hamm: [00:08:17] that's a great answer.  Now you've been global vice president for architecture and data solutions for about a year. You made a quick reference to that. Was that brew. That was, that was that platform what you were brought in to do, or is that what you decided to do when you were brought in.

Ari Margalit: [00:08:34] so first of all, I am not new to ABI. So ABI bought a company that I was a CTO of. Right. You mentioned it at the beginning Weisberger. So yeah, so. I was farcical, just CTO. And once they bought us, they acquired us at the January, 2018. So I've been, uh, and before that they were our customers. So I met them as our customer, and then I've been working as on the West burgers.

So they knew me. Right. And when they brought me, they brought me to help them transform. As solution organization, a global solution or organization into a technology organization and bring fresh blood of technology leaders that work in technology companies, all their career, right? I'm not the only one that was brought by the way.

And we were bringing more tech talents with us, right. Helping to increase our talent pool. And, and when I joined, I was tasked with some global responsibilities in that technology organization that included data integration, API, microservices, transformation, and overall to look over our global, architecture, but very fast, me and my team, we understood that the biggest and most important challenge we're going to focus on was data.

Right. And my organization today is putting 80%, maybe 90% of our efforts and mind into building BrewDog. So that was like really fast. We evolved ourselves to, to bring this a new brand, a new new idea. and, and it's already becoming a number one technology backbone of our company really fast, copied, really helped.

, and that would be the base platform that on top of all, our main global products will be built or transform to be built on. and the different functions in the company, a company, have, uh, they, they all have solution groups.

as I said, very big company, very, very complex structure, but at the end, there is like a function and every function has a solution group. And that solution group, as we are evolving, we're evolving to build in house development in house products and what me and my team are trying to do is bring all those products portfolios.

On top of blue dot. So that would be like the one backbone of technology for everything we're building. So it's becoming very big.

Steve Hamm: [00:10:51] It sounds like you, you really are, moving very fast.  I want to make sure I understand totally here when you're talking about brew debt, what exactly, what data are you collecting there and how is that data being used?  Is that a data Lake or something?

Or what is that?

Ari Margalit: [00:11:08] Burnett. First of all, he's a platform it's much more than an a Lake. It's an ecosystem. I'll give you an example. let's take Uber. So Uber, you have the drivers, you have the, and you have the customer that we go and we need a ride, right?

So at the end, what is Uber? They, they're not the drivers and they're not the cars. They're not users. So what are they? So they are the platform, right? They enable that ecosystem. Saint gross for BrewDog. We are an ecosystem for data. We enable. Data producers in consumer among all the company users, right?

For me all the 180,000 employees can be that customer. But at the end, I believe it's more the technical technical users. we enabled them to create a business value out of data. And because the company is so complex and so much sources, as I said of that. And it's so not structured. You need to build a platform to bring everything together and then to serve it.

It, you know, service that data to people. Per their needs. So there are like five different, maybe six different personas in a company. They need data. It could be a business sound. At least it could be a data scientist. It could be a business decision maker. It could be a BI, it could be a developer, the only data.

And you need to serve it in a simple way, in an accessible way, but in different ways. So we, we built that platform with couple of layers as an envelope to enable people to reach that data. So it's much more than the data Lake, right? The data is the lower piece. You need also API layer. You need microservices there, you need GUI for self service.

You need the master data management, you need governance, you name it. It's very complex. And, and what have we found in the process that you cannot go and buy that off the shelf. And it's very related to what the business you support. So we had to build our own in house IP, you know, proprietary platform to support that need in our company.

Steve Hamm: [00:13:09] Yeah. Well, let me ask you about that. I mean, people talk about alternative data and this is like data that, you know, it doesn't come from your own operations or, you know, but really gives a, a broader contextual view. It might even bring in, you know, whether. You know, geopolitics, all, all kinds of  healthcare.

How are you bringing that into a  Buddha?

Ari Margalit: [00:13:30] So it's interesting what you're saying. So first of all, you need the data that you're bringing. It has a couple of layers. First. We need to have the transactional level data. So you want to have, you know, what's happening out there. So we're bringing our own transactions, right? We sell beer. So we have transaction of selling.

we bring our financial transaction. We bring everything. We be not people transaction, whatever is in our system. Uh, religious sticks, our supply, our marketing. We have so many functions, they all generate transaction. So we bring those in right. we also want to buy transaction. That makes sense.

So for example, very important transaction is what our customer, right? Our customers are the point of consumption. It's a beer. It could be a, a restaurant. It could be a bar. It could be a supermarket, right? There are many type of customers. They also sell out. Right. They also sell our products. So those transactions do not exist in our system.

They exist in the point of sale. So there it's a bit more complex. So we have two types of ways to bring it because we want it in the transactional level. So we can go into buy it from a point of sale vendor, or we can bring it from products they integrate with the point of sale vendor. So for example, in advisor, we're integrating.

Inside the borrower bringing our on sensors, but we also integrating with the point of sale, we bring that transactional level data. And then we start to put algorithms and understand like, what is data brings? And in the big data world, it brings a lot. So this is something we, again, integrate. Integrate or we buy.

So that's the second time. And then comes like the metadata, the data that you need to bring from the side, eh, to enrich the data.  or it's our metadata, our employees that our customer that are our brands that are skew, or I need to go bring it, bring it from our side. and the last piece is what you just mentioned.

It's an enrichment of the data. So I want to go and buy or bring from open space, right? There are, out during the open and, and data that you, that is available, you don't need to buy, for example, you can get weather data, you can get events that, in the COVID-19 period, we go, we went and found like that around like, you know, demographic data, data about when governance took decision.

On restrictions, we brought out data and so on and so forth. And what you see when you bring it up, we think together only then you unleash the real AI, because then you start to understand like  the butterfly effect, you start to understand what is the effect of all this data together on, on decision making and also the, on the insight.

and from that, you can bring the real life recommendation to the business. You can reel. To your consumers and then you unleashed, you know, things that you cannot really understand before.

Steve Hamm: [00:16:18] You know, during COVID my beer buying habits have

Ari Margalit: [00:16:22] Yeah. How come?

Steve Hamm: [00:16:23] well, I walked onto my local brewery. I call them up, tell them what I want. And they bring a six pack of beer out to a little table in front of, in front of the place.

Ari Margalit: [00:16:33] That's cool.

Steve Hamm: [00:16:33] Yeah. It's not, I wouldn't call that a high volume business, but at least, you know, it's a way to keep the connection going,

Ari Margalit: [00:16:40] Yeah. I think coffee, they changed the way we consume, it accelerate the whole, like e-commerce remote. we were a business that we're still like classical business. We had a lot of like sales reps out there and now we're turning ourselves so much faster to be a digital company. Everything is like online B2B B to B to C is, is growing dramatically.

And I see it everywhere. Right. So, it changed the way we consume.

Steve Hamm: [00:17:07] So, when did your company and snowflake basically start doing things together,  are you a customer of theirs in, in a, in a big way or a small way or what.

Ari Margalit: [00:17:17] we started talking with snow flag at age two to 2019. So not long ago I started, I think, most of the conversations, and we had few months of like dating. Okay. Is he might say, we ran few successful PLCs. I know there were a couple of PCs before I even joined. And, but nothing went into a real contract and then I became like a sponsor.

I do believe in that solution as much as I saw it. and then, we started to test the POC is on top of. Our two main cloud solution are our number one main cultivation is Azure. And then the second one I think is AWS. So we started to test a couple of PCs in both, and the idea was simple. we're, we're looking for cloud agnostic solution.

So to become cloud agnostic in our data warehousing. Which self-imposed for that, right. we're bringing that, sorry, performance where we wanted to find, the best technology and solution out there to run at scale with data. Right. And we saw that as we were, scanning up with the different solution out there from Azure and AWS, it was a.

Is it becoming like a tough mission? It's not that it was impossible, but it became very costly. And we also saw some performance challenges. So that was the second reason we were looking at snowflake and the third was cost. We thought that we can reduce actually the cost of our, AWS Redshift, our Azure SQL database DW.

and when we did POC, is it w it was showing that. Exactly. So again, those are the three main reasons and we signed the final global deal during COVID-19 crisis, actually around the, I think March, April, so a global deal. and that's it. This is for your point number one, and then asking about the project.

We had like six initiatives. three, there are ongoing projects already signed sow, and three.

Steve Hamm: [00:19:17] So are the, are these the proof of concepts or the, the, these part of the global deal that

Ari Margalit: [00:19:22] No, no. We had the global deal. And then we had like a specific bills for per project. We had six projects that we would look at. Three of those, ended up with like a project that is live and we're working on together. I can explain if you want. And then there are additional three that I see them as more of a prospect and hopefully become a project next year.

Steve Hamm: [00:19:43] describe one of them that you're, that you're entering into now. That's, that's real and kind of like, what's the problem? How does this solve it? What, what benefits does it bring? And then we can talk maybe about something you're looking forward

Ari Margalit: [00:19:55] I think the biggest, is a biggie, a huge transformation uh,  to step back, uh, ABI is built out of six zones. So it's own is actually a continent. So if I will use the word zones. Just for you to understand. So Nazi's are North America, the zone. So it's the America North, usually mainly us and Canada.

We don't have the middle America there. and, in us we had a Teradata environment on a data center, which is what was a big pain because we really want to move out of it, to the cloud. And, and, and NAZA is our biggest zone. In terms of consumption and revenue. and, and we were looking at a project when I joined, we were looking at a project over one year, many question marks to transform it to the cloud, move it to Azure.

Uh, we brought a couple of third party companies was looking very expensive, a lot of like things that were unknown, that people were a bit, you know, scared to, to step in into that project that we're talking about 30 years, Teradata amount of data. and I, and I offered to bring snowflake, to, to that point.

It was a, an idea that came from me and, and I pushed the team and snowflake came in big and really offered that help because it's not a, I would say a typical let's move to the cloud. or as a small company moving to the cloud was a big deal and stuff like step in. And we, we, we joined forces and we're now in, in the middle of the progress a couple of months already of that project, , it's a side project, right.

And the idea is to move all the Teradata data to map everything, and then the important data, everything to migrate to, to snowflake. Into the cloud. and again, it's more percent snowflake. we're looking at the six months project instead of a one year, we're looking at a project that was mapped completely with snowflake, you know, moving all the data.

We would stop like a team and experts. So it looks really promising. There is a great progress going on, so I'm pretty happy.

Steve Hamm: [00:21:58] What kind of benefits are you expecting out of that?

Ari Margalit: [00:22:01] first I want to have as much as I can, our solution to become, as we evolve with cloud agnostic. My solution. So it's not flux is going to help with that. Right. So, I can have one zone, in a data warehouse in the cloud in snowflake and he's agnostic. So that's great for us. This is one, and as we evolve, I want to become more and more agnostic.

Steve Hamm: [00:22:24] So the advantage of being cloud agnostic is that you're not overly dependent on any one cloud provider. They don't have that leverage on you. You you're, you're free

Ari Margalit: [00:22:35] Only in this part, right. I'm not free really, because we are. Where I was so deep into the cloud and have such a big deal, going on, with, with both the big companies I mentioned, but at least part of it, we want to be free and not just to, for it's great to be free. Right. But it's not just because of that.

It's because then we can always choose the best technology. we can connect between our different, we have data in both the cloud so I can connect them through self, like, so this is second. And also to be honest, when we were using SOFIC, we're saying, I think, and don't get me by number, but when we tested it versus a brew dot that we did a POC with Buddha and we tried versus Azure SQL database data warehouse, and we saw that, Well, I have something like 20% improvement in performance, which is very big for us.

And when we tested it in Weisberger for a Redshift that aroused, with AWS, we saw that we're bringing something like 40% again, from what we saw. So that is a big thing. It's more, even bigger than that. The agnostic thing like it to, to improve performance in a big data environment when the world is changing.

Become much more retired, much more close. You know, you need to bring the data fast. This is, this is big for us.

Steve Hamm: [00:23:56] Now I want to drill down on this just a little more before we move on to the, to the future project. But, so what does Snowflake's cloud data platform allow you to do that you could not do or do as well on Terra data and your own on your own data centers and all that?

Ari Margalit: [00:24:16] For me, it's cloud agnostic to,  step up step in really big year, helping us do a transformation from an old data center. When we had almost zero knowledge already in home for the old details that we built and they helped us, you know, put the experts and re rebuild those ETS, transformed them into the snowflake language or snowflake format.

So that was a huge help. they help us make it faster. So it's, it's, it's time to market and it's saving money. Right. If you do things faster. So I think, and add to that, what I just mentioned around the performance and costs of running the day to day, that else you think it's big.

Steve Hamm: [00:25:00] you mentioned,  you have this huge supply chain, you have huge distribution chain. You talked about. Needing to get data  from both directions. Are you looking at the snowflake data exchange as something you're going to be using for those things?

Ari Margalit: [00:25:15] first of all, just to mention, we don't have just a supply logistics. We also have a huge business of sales. With digital products, we have huge business of finance. Like we have more than that. So many functions. Yeah. We're looking at that. We didn't get yet to use it. I think if you will ask them about the future project, maybe that will be an opportunity to look at that.

Steve Hamm: [00:25:41] What about that future project?

Ari Margalit: [00:25:43] Unfortunately in 2020, we were trying to bring a, a very important project to life, which is bringing soft, like in Buddha, right? Our global data platform or biggest, data piece of the pie. and we're very close to do that. And unfortunately, as I said, due to COVID. We had some, I would say restrictions on our budget and on our new investments.

So I had to put it on hold until 2021. So right now Buddha is the global platform is not yet using snowflake. And as I'm a big snowflake fan, I hope we'll find ourselves in 2021, exploring that again and, and going into a project that we can move our. That our seeing of the global data platform to self, like if that will happen, that will be not just a very important step for hospitals.

So I think a very important project for, for snowflake.

Steve Hamm: [00:26:40] So you're using snowflake in a more limited way now. And is that with the North America and then you're in the future.

You're planning on just going global with it. Is that, am I understanding

Ari Margalit: [00:26:51] understand? Correct. But there are more,

Steve Hamm: [00:26:53] Cause even

Ari Margalit: [00:26:53] we're using a small flag in our Zeta to venture, which I mentioned that, is, uh, like we have our, family of. Companies under ABI. So FedEx is a, is a business unit that runs on its own does eCommerce that does ventures capital, and also have like set of, most of our craft beers, and some, some of our own retail.

So they have. A lot of data and analytics and they're moving their data and analytics to snowflakes. So this is one, two is Weisberger that we're looking at moving and we put it on the shelf. We're going to bring it back in 2021. we have bees, bees ease are. Biggest portfolio of products in the company is our digital sales, a initiative, a huge initiative by the company that was even accelerated in the COVID, right, including what we call B2B Delta, which is our new B2B, business.

So, you know, to book, to sell through an application that has a strong algorithm selling and, and, and all kinds of like, Cool analytics built in inside that are being built on top of Buddha. and they have a piece that is capturing the behavior of our consumers and all the behavior or analytics of our consumer is going into snowflake now.

So this is a new project that, that is signed and started. And in addition to all I just mentioned, there is also our mass zone, which is our middle America. That is also the last zone that is still in a data center, a big zone in a data center that we were talking with snowflake and with them to bring them into snowflake.

And it really got on hold. And I think in 2021, we're going to explore it again. If NAS project will be successful, I do believe it's going to be a huge project from snowflake. So a lot of opportunities, a lot of prospects happening here. So a cross fingers.

Steve Hamm: [00:28:50] you know, we, you've made a couple of references to, you know, kind of the, the macro or the, the, you know, the, the global market conditions, you know, some challenges in sales and beer. obviously COVID,   like a lot of other companies of all different sizes.

It's under stress. How are you using analytics to get through this very stressful period?

Ari Margalit: [00:29:15] I think a reference a bit to that. we're trying to, to use analytics to drive decision making. Right. I mentioned that. So think about that. We had the COVID for example, let's take it as an example, and everybody was trust. I did the management. Nobody knew what's going to happen. Right. it was a tough time for, for companies that are manufacturing beer, because for example, our Mexico Modelo plants were being shut down by the governance.

From good reasons because they didn't want people to spend their money on alcohol in such times. we had a lot of quarantine countries, so we're, we're, we're, we're having a tough time. Right? so we won, we were trying to help our decision makers to take the right decision. And without analytics,  they're in the dark. So what we did, we took that data. We bought our. Talented pool of like analytics, data science that we've built through the years. And they're analyzing all the data we had. Plus bringing in the enrichment on data that I mentioned, like, you know, the academic data, the governance decision data.

And we were putting everything into the, the graphs and running the models. And we're trying to predict what is the, you know, pessimistic and what is the optimistic. Prediction of our future. And then we took the decision how much we need to cut, where, where do we need to focus? What, what is the hit list of customers?

We need to go and, and, and send our people to how we need to change the way we work in order to go through this crisis without really knowing what will happen. So without analytics, We were being too dark. And now we brought them that light. We were coming with analytics, graphs, making them more and more relevant than ever to our senior leadership and, and giving them, you know, the eyes and ears they needed to take decision.

And actually we were helping the company in ways that we never did. And it was amazing if we didn't build all this infrastructure through the last few years, we could not do it. So that was amazing.

Steve Hamm: [00:31:17] So I would imagine that AI and specifically machine learning are extremely useful in helping deal with complexity and uncertainty, which is what everybody faces these days. Are you bringing AI to bear on this stuff?

Ari Margalit: [00:31:34] Yeah. Yeah. It's all AI and ML. so the analytics, what I mentioned before, for example, is, building prediction models using AI, right? You take data. from historical data, right? And, and you take also ongoing data that is happening right out there for, for things that are happening. Are there events? as I said, academic data, demographic data, consumer data, like what people are consuming, the behavior you bring all of that plus the history and you try to predict.

So you use the AI to predict the machine learnings is another step, right? You can predict, but you need to accurately make yourself more accurate. So by bringing a feedback to the system, Using machine learning, you can make yourself more accurate because the one thing that happened with COVID, we actually thought things will be worse.

So when, when everything happened with Italy, Italy, right. And, and, and, everybody got scared of what happened in Italy. And people got really pessimistic. So we gave the pessimistic model and we give the optimistic model and we had to adjust it every week. So without machine learning, we cannot adjust.

So we brought the feedback. And we start to adjust with machine learning and the, and we became more and more accurate. And right now we became like what we did industrial months, the level of accuracy we brought with machine learning, our decision making is only on top of that. And it became a reference.

So this is pretty cool.

Steve Hamm: [00:33:00] Now with, with COVID you have a misfortune, which is that one of your big brands has the same name as the

Ari Margalit: [00:33:08] Oh my God. Yeah.

Steve Hamm: [00:33:10] How have you, how have you dealt with that and, and has, has, artificial, I mean, as AI or analytics kind of played into that, or are you, I mean, are you doing different things in marketing or, or listening

Ari Margalit: [00:33:24] To be honest, it didn't affect us. yeah, because, first of all, people start to say Corona, but then it became like, COVID, it helped us, they start to use the right name. but, it didn't affect that

Steve Hamm: [00:33:36] That's right. Then they went, they went from, they went from the disease, the virus to the name of the disease. And that was a good

Ari Margalit: [00:33:43] Yeah, it was good for us, but, but anyhow, it didn't affect many people ask us it didn't affect the brand. And, and actually we start to think that it will create a positive effect once, hopefully everything, you know, we will get rid of this horrible disease. we, we think it will, we can turn it into a positive effect one day, but only time will tell.

Steve Hamm: [00:34:04] you know, I noticed that earlier in your career, you worked at a little company called jive software, which is very kind of social business conscious. And here we are here, we are in the coven crisis and there are a lot of people kind of talking about pivots about society.

The economy, are there things that we need to adjust? Not just in companies, big companies like ABI, but you know, in the way we operate as, as, you know, homosapiens and all that kind of stuff,

you're obviously a very sharp technical guy and you think about business a lot.

Are you thinking about, you know, transformation in society and how technology can help that?

Ari Margalit: [00:34:43] So super interesting question. listen, first of all, the amount of data that was generated in the world in the last three years is bigger than what we have gathered in the history of mankind. Just for you to know, we're in the middle of like an analytics, analytics transformation of the world, right.

In every field, everybody feels that, and that was expedited with the COVID-19 crisis. So seeing the whole world, you know, moving to work remote,  seeing the health industry changing and expediting to become a remote lab. Right. Something that I think it expedited in 10, 15 years to we're doing the coffee and everything is led by data.

Think about it. And, and AI that I is the infra and the oil and AI is the platform. AI is the way we turn it. So, so I'm seeing governance. As I said, taking decision with AI prediction. Right. I don't think it happened in the past. I, I, with, with AI model, I see companies making decision. As I said about us in their future on forecasting using AI, I see our prime minister of Israel, baby.

He came live, live on TV several times during the coffee. And he shows analytic reports that people made for him, using analytics right during the Cove. Wait, so it never happened before. So, of course the world is changing. I think it's changing for the good, in that sense.

I believe in every topic we're going to have AIS in every stream of our life. we start, we will start to evolve. We're going to have IOT sensor, internet of things, sensors that transfer live data. And I am models being built on top of that, that data that has been brought to,  such data platform.

So I will give you examples. today everybody has the, where the, the, the smartwatch, right? And he collects only, or, what I collect your, heart rate.  and maybe calories and steps and things like that. This is going to be evolved fast two together, all the data from your body, and then it will be sent to your doctor and you'll be able to give life decision.

You will have to go to the doctor. We will be able to prevent diseases like that. We will be able to, to, to detect and advanced problems, right. We'll be able to detect problems with our children. So I think, In some sense, the world is going to be a better place to live in, in some sense, it's a, it's a bit scary.

Steve Hamm: [00:37:00] you know, one of the things I wanted to mention to you is, and another project I'm working on, we're working with spark beyond, out of Televiv.

Do you know those

Ari Margalit: [00:37:07] course, they're amazing, amazing product.

Steve Hamm: [00:37:10] yeah. Yeah. I just think the whole idea of having these hypothesis generating or ideation AI machines and, and bringing together, you know, the, the human experts and these machines and just pack, you know, pack it a bunch of data into them and having a collaboration between the humans and the machines, I think is really very powerful.

Ari Margalit: [00:37:34] I agree completely in spark of beyond our, our partners. We work with them with our AI labs. and I also know them very well because of course they are Israelis and it's a small Lake community. but they're doing great.

Steve Hamm: [00:37:47] it's a wonderful company.  Well, Ari, it's so good to talk to you today. This has been a really lively and fun conversation, and I feel like I've learned a lot.  I feel like the people who tune into the podcast have their eyes opened a bit as well. And it's also very refreshing to talk to somebody like you, who has, you know, you really have that hardcore technology look that, that urgency you're really focusing on the business, but you also have a broader view about.

Society and the economy and things like that. So that's,

Ari Margalit: [00:38:19] Thank you. Thank you. I appreciate that. Appreciate that. Thank you very much. It was a pleasure for me to talk with you and I hope, we will have a better future for all my account with, with everything that is happening. Right? So cross fingers.

Steve Hamm: [00:38:34] Let's hope so.