Leveraging AI

41 | Supercharge Your Decision Making with AI's Data Superpowers with Anthony Alcaraz, Chief AI Officer at Aldecis

November 28, 2023 Isar Meitis and Anthony Alcaraz Season 1 Episode 41
Leveraging AI
41 | Supercharge Your Decision Making with AI's Data Superpowers with Anthony Alcaraz, Chief AI Officer at Aldecis
Show Notes Transcript

What if your business data could talk?

In this episode of Leveraging AI, Isar Meitis interviews Anthony Alcaraz, Chief AI Officer at Aldesis, about how AI can help businesses unlock deeper insights from all their data - both quantitative and qualitative.

They discuss how current business intelligence systems rely too much on surface-level consolidated data, masking crucial granular details and anomalies. Anthony explains how his company leverages new multidimensional AI to systematically analyze finance, operations, and other text data at scale - detecting issues and opportunities early and even prescribing solutions.

👇Topics we discussed 👇

  • The problem with aggregated data analysis
  •  Defining an “insight” vs just stats
  • Using AI to connect siloed data sources
  • Finding patterns across dimensions
  • Combining quant data with qualitative data
  • Detecting anomalies early
  • Prescribing data-driven actions

Anthony Alcaraz is the Chief AI Officer at Aldesis, where he leverages over 20 years of experience building AI and data analytics systems for large companies.

About Leveraging AI

If you’ve enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Isar Meitis:

Hello and welcome to Leveraging AI, the podcast that shares practical, ethical ways to leverage AI to improve efficiency, grow your business, and advance your career. This is Isar Meitis, your host. And as you probably know, if you've been listening to this podcast, I'm a huge believer in data and data analysis. And it enables us. To make data driven decisions, making it allows us to make smarter decisions based on data that we have. And that's one of the benefits that we gain from moving to the digital era. However, if we look a little deeper, you can see that we became very good. And some people say addicted to quantitative. Data analysis, we know exactly how many people visit every page on our website and how many clients are paying on time versus paying late and how many people applied for the recent job posting we opened and what's the percentages of proposals we win, et cetera, et cetera. These are all quantitative insights that we get basically from every system we have in our company that gives us these kinds of insights. Reporting in almost every department. What we do very little of is qualitative data analysis. And the reason We do less of qualitative data analysis, just a lot of work. So things like interviewing the companies who are not paying on time to see if there's a reason that maybe we can help them resolve, looking at an interviewing at clients that have left us and not use our service or product anymore, and trying to understand why. Aggregating customer reviews and trying to find the silver lining of what people like very much and what people do not like. So we can improve on both aspects and so on and so forth. The sad thing about not doing a lot of qualitative data analysis is that while quantitative data analysis tells us what happened, qualitative Data analysis tells us why it happened and the why is what really what makes you help what helps you make better decisions. If you know why something happened, you can change it and then get better results. And as I mentioned, the reason we're not doing more of that is just a lot of time. If we take one of those examples that I gave, reviewing all of our customer service reviews. People have to go and read every single review and then define them in different buckets of topics and then define if they're positive or negative and then dive a layer deeper of what are the things within that topic that people are saying. It's a lot of work and it requires a lot of manpower. that was true. Until a I showed up. So now a I enables us to do better quantitative data analysis, but also overlay qualitative data analysis at scale without investing huge resources. Our guest today, Anthony Alcaraz, is the chief AI officer of Aldesis, which is a company that specializes in exactly this, assessing data, including qualitative data, to help companies make the right decisions Based on both quantitative and qualitative data is because I think this is extremely critical for successful companies. I'm really excited to welcome Anthony to the show. Anthony, welcome to Leveraging AI.

Anthony Alcaraz:

Thank you for having me.

Isar Meitis:

Anthony, let's really start with the problem that you're trying to solve, right? Because you're in a company and you have a problem that you're addressing. Can you Define it better than I did. Yes. What is the problem and why does it still exist in the year of almost 2024? We're recording this in November.

Anthony Alcaraz:

I think there is a problem in both sides, in quantitative and qualitative. For quantitative, there is a huge issue currently with a reporting system that businesses have implemented is that they are mostly doing what we called consolidated analysis. Is that they are aggregating data. to have another view of their situation is a problem with, consolidating with a consolidated analysis is that it hides anomalies. It hides there is, there are compensation effect between values. If you take an average, you don't, you can't see outliers. Yeah, outliers. Exactly. And it is, it seems simple, but to get the right granular level of analysis. Let's say, the correct view of your data is very complicated. And so at my company, we developed a systematic method to analyze data at the right level. And thanks to this. we get seven times seven times more insight than consolidated view. And it is important to do this because obviously you can see problems at the consolidated view, but the moment that you see the problem, it already happened. Because consolidation is aggregation over time. And if a problem has appeared, and it is so important, that it is like a rooted branch. So it, it has, disseminate, it has contaminated the consolidated view. So by only looking at consolidated data, you only see 50% of current insight, current anomalies, where the value is because as a businessman. You want to act on on anomalies, on opportunities and risks because anomalies can be positive, but also, negatives, like a problem on the client, or a business unit. Okay? So at the quantitative level, there is a huge issue with with business intelligence reporting.

Isar Meitis:

Let me pause you for one second, because you keep going to something that I think is very critical, which is getting insights. So let's define what's an insight, because you said you lose 50% of the insights by looking at consolidated data. You said by not looking at it, you're getting seven times more insights. Let's define what is an insight, because this, I agree with you, is the most critical measure that at the end when you're looking at data. Yes.

Anthony Alcaraz:

So basically, there is many definition of insights. There is, currently there is no scientific definition that would say an Insight is this. Okay. But papers that are have been, worked on, that, I've been a writer on this, said, that an insight is something that will change your view of a situation or business. So it's most, it's more a subjective approach. What we have taken at Aldesis is a more, business related approach. for example, our app works, with symbolic artificial intelligence, and we define thresholds. For example, our current use case is for corporate data analysis, and we know that for analyzing corporate data, current experts, use certain thresholds of alerts. For example, you can have, I will tell you a quick example. You can have a gross revenue increasing while the net revenue is decreasing. This is an example of threshold that our app is capting and we have hundreds of those thresholds that we are tracking. Okay. there might be insights, mathematical insights. A value that is an outlier and you can have different types of threshold for outlier. But most of the time that will be outlier that are, I don't know, 25% percent, exterior of the average. Of the average, yes. The mean, extra. But you can have also a logical business insight. Something that is weird, but it's not functioning correctly. Like I said, just for the gross revenue and net revenue. What we do, because currently we work with our clients, we work on on corporate analysis. corporate finance analysis, we have defined insights based on expert analysis and we use symbolic AI to do that in a systematic way. yes, this is our definition of insight.

Isar Meitis:

to sum it up, an insight is something that is not normal within the data. Yes. That is connected to a business outcome, right? This business outcome can be something that is numeric. It could be something that is behavioral. so it could be like saying an increase or a decrease in a number, but it could also be too many clients are not renewing their service right? That's not a number. It's a behavior thing. So it could be like we said in the beginning either qualitative or quantitative and it's basically a red flag saying"You should probably either take a look at this or take an action on this thing because something that is beyond business as usual is happening."

Anthony Alcaraz:

And I would have something is perfect. What you just said, but I would have that there is no performance without comparisons. So to define an insight, you need to compare products, clients. Over time that are comparable, for example, our app, is comparing a business unit that are similar or the same business units with itself over time. So you need that reference, to compare and to get this insight. So it will be an evolution.

Isar Meitis:

You need enough data in order to even get started because otherwise you don't know what the baseline is.

Anthony Alcaraz:

Exactly. You need a baseline, a standard, with which you will compare and detect an anomaly, a threshold that has been crossed, etc. Yes. And to do this, to go back to, consolidation if you do this at, a very average, a very top down, what we call a top down approach, you won't track many insights here.

Isar Meitis:

Okay, so quick summary. So far, we talked about aggregated data being something that is the norm today, but then you're losing a lot of those insight. We said that an insight is something that is not business as usual. That should hint that you should take an action or at least pay attention to something. And I stopped before when you're about to talk about qualitative analysis. So let's continue really with that. Like. How do you approach the qualitative data in your view, in your business? Okay, so

Anthony Alcaraz:

we talk quantitative, but there is much more, a lot more to say about quantitative, but for the qualitative, so on our approach is this, once we detect insights, quantitative insights, what we are doing, what we're building is to leverage, generative AI that are impersonating, stakeholders, personas uh, to look at operational data quality, qualitative data that are already present within the companies. So we work with large companies. most of the time they don't leverage, all their text data, all their emails, all their, I don't know, reports, analysis, et cetera. So we use generative AI. So let's say, a lot of people right now knows LLMs and chatbot GPT. We use that generative AI combined with a way to to, to give, to those E I, corresponding data to find the root cause of the insight. So we start with an insight, a constructive insight. So it is an alert and alert or some symptoms. We do that at the most granular level. In the multidimensional analysis where we can talk about after a multidimensional analysis. Once we spot this insight, we, we have a module called Deep Root that help the analyst find the root cause of this insight to act on it. Because what we think, what we believe. Is that the where question of business intelligence is no longer relevant for human because the where question is much more dealt with artificial intelligence in a more efficient way, in a more systematic way, we think that analysts know their role. With for example, our application is to, is a more qualitative job is a more complex job because it's a complex, you can be in a complex setting, business setting. There are complex, a lot of factors, co-founders factors, et cetera. And it needs to be helped to find the root cause of, an insight. So what we do is that we automate. insight detection, in, in the data warehouse for corporate finance right now, because if this is our current use case, but it can be, it will be, I think any use case possible because it is a new way to do reporting. And after that, the user is helped with artificial intelligence, with generative AI. Yeah, generative AI will ask questions. We leverage methodologies will give it give him access to data will tell him where there is a maybe information missing so we give our artificial intelligence via techniques like knowledge graph a way to help intelligently and with methods. analyst. Yes, so this is a more. Qualitative analysis.

Isar Meitis:

let me pause you for a second, just to do a quick summary, because I think it's very important. And I think it's critical for people to understand the power of the systems that we have access to today. So you defined two different steps that are both critical to actually getting to an actionable outcome, right? The first step is identifying something happened. And you said that even this. Wasn't very easy with systems we had today because in order to handle the huge amount of data that we had to aggregate the data. Once you aggregate the data, you lose some of the details. So every data analysis platform out there today works that way. I'll add something on top of that. That was, the subtext of what we're talking about. Every business today has multiple systems that are tracking data. And they're not connected. So they're usually in silos and it's very hard to analyze information from your marketing system versus your CRM versus your ERP versus your customer service platform. And in many cases, they don't talk to one another and each one has some clues on what might be the reason for something happening, which leads to your step two. So step one is I can now better identify when something that shouldn't happen is happening. Step two is now I can actually look at all these sources of data together. And it's funny, I talked to a lot of CEOs, I teach courses, I speak on conferences and I consult to businesses. So I get to talk to a lot of business leaders about this thing. And a lot of people, especially in smaller companies saying, we don't have a lot of data. And the reality is, I'm like, you have tons of data. Just look at all your emails with all your clients and your potential clients. Look at all the marketing presentation that you give. Look at the recordings of every sales call or every support call that you have ever recorded. All these kinds of things are pieces of information that you have in every company. Now, the reason people don't look back at them is because it's just a lot of data and it's very hard to analyze, right? It's if you had. And we do now. You're literally telling me that these tools exist right now. You're developing one of those that can look across all these unique silos of data, connect the dots and say, Oh, In this email, and in this call, and in this customer service review, and in this channel, I can see a pattern that can explain why this outcome that we're seeing, that is an outlier, is actually happening. And this was the hardest part of data scientists, is to look through all these different silos and somehow connect them together to get better outcomes.

Anthony Alcaraz:

yes, exactly. Currently, our approach is this. You have finance data and, operational data. Finance data is often called, lag, lag data, lag, lag analysis because finance happens every month. Every month you got finance data. It is an obligation, for IRS, auditors, shareholders. Yeah. All of this there is a lot, a lot of regulation, it's regulatory to, to get finance data. And even with false rules, you get data quality issues with finance data. So our approach for our first module to detect insights, a quantitative approach is to look at finance data. Okay, we are currently also integrating logistic data, but we are not there yet. Okay, so we take an approach of data quality. So we take our finance data, our approach, allow us to detect. Very quickly. Data quality issues because we've our deconsolidated data analysis. You we have a super prioritization algorithm because one problem with our approach is that you get many insights. So you need some way to prioritize them. So we build up a prioritization algorithm and with this. We get a lot of insights, and at first, if a company has a data quality issues, you will get insights that are explained by, data quality anomalies. you will get, absurd numbers, but they are related to, wrong numbers put by someone, data quality issues, explained by many reasons. Okay, this is the first step. The second step. Is that we use, operational data. So we simulate stakeholders with generative AI, with a RIG framework. RIG is retrieval augmented generation. So we give to the generative AI, pertinent data to help the analyst. Find the root cause of the problem. What we do is completely reverse of the current trend. Okay. Lots of BI providers right now have automated data up to the reports. Once you get your reports with average data, consolidate data, it is to the human, to the analyst to find the problem. So what we call currently with Databricks, et cetera, is to data on demand, okay? We think that this approach is not efficient because it will multiply the needs for reports. If you stay at the average level, the problem that you will see it will be too late. Because by detecting, earlier problems by detecting at the right granular level, you detect problem much more earlier that if you wait for it, that it, it, it attains the consolidate level. You won't see many insights. So you will need to dig digging. To drill down, but drilling down it's very approximate. It's very hazardous, you don't know where to look at. Maybe you don't have the resources to look at systematically, for every insight. so I think currently it's very complicated to get all your anomalies, all your, things to get down, in a systematic way with current tool. Okay,

Isar Meitis:

yes, I want to pause you just for one second because you touched on two very important points. One is, and it's counterintuitive, but it's once you think about it, it's very obvious. Once you go to automated systems, because they can look at a lot more data in and don't miss anything, you're going to get a lot more alerts. Because now it's a lot easier for an AI to find where something is not the way it's supposed to be. Exactly. Then you get to the business logic behind it of saying, okay, is this important for the business? How important it is on a scale of whatever, one to five, one to ten, one to a hundred, it doesn't matter. And then. You really want to dive into only the stuff that makes a big difference in your business, right? So you don't want to spend the time and now have analysts and decision makers and meetings around problems that touch 1 percent of your revenue. But then if there's problems who touch. 20 percent of your revenue or 30 percent of your operational cost. If you're looking at the expense side, you definitely want to look at these first before you start diving into the smaller things, that, that are less relevant. So the critical aspect here. Is that building these systems? Yes. AI is capable of doing this today. And you can even do some of it with tool tools like Claude and ChatGPT and different, the API is to connect to these tools, right? You can do this in your business right now, especially if you're a smaller business, you can use these tools, the things you have to remember. And that's the big take from this last segment that we talked about is that. Yeah. You have to prioritize the things you're looking at based on its impact on business results, because otherwise you will have enough problems to take care of every single day, every single hour, instead of actually running the business, because they're not going to be prioritized to the stuff that's actually going to move the needle from the success of the business.

Anthony Alcaraz:

With our methods, it is humanly impossible to deal with all insights, so we get so much insights and what I haven't talked about just yet is that we systematize multidimensional analysis in most reporting today, you do up to three dimension at the same time.

Isar Meitis:

So let's say what that means. So give me the three dimensions.

Anthony Alcaraz:

Yes. for example, you can have a client's product region at the same time.

Isar Meitis:

Got it. So list of clients, list of regions, list of projects. That's the three dimension. That's an example of three dimensions you're talking

Anthony Alcaraz:

about. It is very hard to do it. three dimension, three dimension in the reporting system. It's hard to visualize. Yeah. And it's hard to do it manually. Yes. Okay? But you have much more dimension that you can do at the same time. You can include a temporal dimension, so have many years. You can include multiple scenarios, budget versus real, et cetera, et cetera. right now, our system is able to do, as much as dimension that is needed. Yeah. Yeah. so you can

Isar Meitis:

be, it goes beyond the limitation of a human to understand and present more than three dimensions because it's a, it's an AI and it doesn't care. Like from its perspective, it's another piece of data that knows how to connect. It

Anthony Alcaraz:

does it in a systematic way on the most, the most deconsolidated granular level. So right here, I am looking, I am showing, tab when you see the importance of doing multi dimensional analysis, because if you take only one dimension, the other dimension that will be in line and you report will be considered consolidate there will be average. okay, if you write, if you combine two dimension, you will see inside that you don't see with one dimension. Because the lines are consolidate, okay, so you need to systematize. you need to systematize a multi-dimensional analysis. We have some sort of, a way to measure the maturity level of a system that is an inside detection tool. And we think that the first step to have a maturity in your inside detection is a multi-dimensional analysis, systematic multi analysis. And the only capable system of doing that in a systematic way are artificial intelligence with massively parallel processing. So

Isar Meitis:

I want to clarify this for people who are less technical and explain what we're talking about and why is it so important. So let's say I want to compare the success of a new product that I've launched across several different clients, right? So that's already two dimensions because I can look at a success of each client and I can look at the success of the new product. If I want to combine the two, I'm already looking at two dimensions, but now let's say I want to compare this. in different regions. So now I want to look at North America versus Europe versus Latam versus, Oceania. Okay. So now I have another dimension that I need to look at and I can keep adding these levels. So as an example, I now want to compare this to the previous product launch that I've done. So I've done a similar product launch a year ago. How does the two compared to one another? The more I add these layers of data, the harder it becomes to a even do the analysis. But once you do the analysis to really understand what's going on. So it's very easy to take one client and look at one client on last year's product launch. And this year's product line, you go to two clients. It becomes more complicated. Look, do you look at 300 It's a whole different story. Now you look at three, four product lines. So once you start adding these additional layers, the ability to a) set up the data correctly and b) really understand what's going on becomes very hard. Unless you're using AI tools like today that can find the needles in the haystack that can really identify patterns. in this crazy amount of data that goes across multiple levels and layers and really give you the right business insights. Because at the end of the day, everything that we're talking about, that some of it is really technical is about one thing, which is making the right business decisions in order to have a more successful business outcome. Exactly. And

Anthony Alcaraz:

and quicker and better than your

Isar Meitis:

competitors and better than what you had before. So even like I speak to too many businesses who say, ah, we don't have real competition. Like everybody does, but even compared to yourself, you want to grow this year, exactly last year. And in order to do that, you need to do a better job. Then you too,

Anthony Alcaraz:

extremely important point points because even better than yourself. And with the consolidation, even your best business units. As some dimension, some sub categories to improve, but even your best department, that seemed better than every other department. If you deconsolidate, if you go at the most granular level, you will find ways to improve it. I, we, I give an example to, to make people laugh. even Michael Phelps. Okay. The best Olympic champions try to improve himself. Yeah, by improving in swimming, by improving is a plunging by improving every part of its subs routine, every part of it, of the sum of its sports. Okay, it's the same for businesses. So by looking only by average, by consolidation, you are not doing that. You are masking these sub anomalies, these subs area of improvement. And there is a tragedy, because We only work at my company with companies, so large companies that are invested in multidimensional database. Okay, so they are able to do this. They are able to do multidimensional analysis because they are invested in data warehouse that is able to do this. But at the reporting level, they are not doing this. Okay, so they have the means to do it. But they don't have the tool to do in a systematic way it at the reporting level. So currently, most multidimensional OLAP cubes are useless. And this is why, this is why Aldesis we built it. Because Aldesis, we have 20 years of experience of building. Of building those multidimensional data warehouse. And that's why we come up with this idea to use artificial intelligence to systematize a multidimensional analysis.

Isar Meitis:

So I want to ask you two very practical business questions. Yes. One is who are the people in the organization? Because now you need less technical people. Yes. Because the, if the system is doing its work correctly, it comes out with a business insight. Yes. Here's a problem. and I want to go back to the steps because I think your steps are very critical. Here's a problem. Here's the root cause. Here's a recommendation on how to solve it. Does this go? straight to the decision making level? Like where, how does this change business processes? That's basically what I'm asking. Like in the companies you're working with, does this go straight to the VP or the director in order to make a decision based on something?

Anthony Alcaraz:

What we do is that we are bridging the reporting world with the project management world. Okay, so what we're building is a tool to communicate between those two worlds that are not communicating right today. So we automate most of the reporting world like I just explained. And our second module that are looking for the root cause and, and proposing action plan and writing with the analyst, business action plan is a bridge. Between our tool and a tool like ServiceNow or Microsoft Project, et cetera, et cetera. So we are preparing the meeting with stakeholders that will be choosing between different business plans, et cetera. so this is, this is where we are

Isar Meitis:

at. Yes. So interesting point. I want to say something that connects to the previous episode of this podcast that I released that talks about four different concepts that people need to understand in order to better to get a better ROI when implementing AI. And one of them is. is that we're trained as business people to think about small incremental updates in a multi step process, meaning as business people through the years, they say, okay, find the step in the process. If you can get a 3 percent incremental efficiency in that step, the whole process now gains a 3 percent efficiency because that was your bottleneck so far. With AI, we have to stop thinking like that. We need to think of what is the required outcome? What is the required outcome? Not what are the steps that we're doing today that are common because that was the way it was done so far. What is the required outcome? And is there an AI supported tool that can get us straight to the outcome? Or with fewer steps to that outcome, which will completely eliminate, not build efficiencies, but completely eliminate some of the work. And this is just one great example where you can go from raw data to a business recommendation. It's still not a decision. Now there's a decision maker, but to a business recommendation with basically zero humans in the loop. Once the system works and you can trust it to do what it needs to do versus. I used to run a hundred million dollar travel company. I had really good business intelligence team, and it was always a pretty long back and forth of, Oh, you know what? I also need this kind of report. So go generate it. They would go and write code and they would run it on the database. And they would come to me with another report and they would look at that. And it was a very long iterative process. Now, if it's built into this black box that are, and I'm sure it's a process, but once the box works, you can go from step one to step. To the final step, I want to present this to a meeting to make a decision with zero other steps in the process.

Anthony Alcaraz:

And you touch an important part. Exactly. Our system automates the reporting part, but don't automate the high value of an analyst task. That is, find the root cause of the symptoms and, write and build an action plan. This we won't automate because it is too complex and the human, needs to be in the loop because very, maybe in data there is no information. There is information missing. It needs to take a call, to do Gamba work to see, the people, responsible for this project, et cetera, et cetera. So what we do is that we play the analyst in a new high value role. Okay. A much more valuable that's a current role because right now in most large companies, these reports are useless. Okay, they come too late. They don't see many inside that are buried behind consolidated you, etc. And there are a lot of turnovers also in these posts. Okay, this is the first point. And you touch upon a very important point. you spoke about black box. Our system. leverage many layer of artificial intelligence. What you need to know for our listener that are not, confident with artificial intelligence or not well known, most of the time we talk about deep learning, neural network, artificial intelligence, those system like shark GPT are like black boxes because you can't know how we come up with the result. Okay. It is very difficult to know all these model come up with such text or such prediction, etcetera, etcetera. What we did is that we combined. Symbolic artificial intelligence with connection X artificial intelligence. I will explain it in a short moment. Symbolic in tough in artificial intelligence are the all the way off doing artificial intelligence is a rule based artificial intelligence. So what we did is that we uncoded experts knowledge in form of rule based, artificial intelligence and we use the result. Of the connection is artificial intelligence to get an output from the symbolic artificial intelligence. So what we can do and we have, up our app is one page. We try to be lean. Okay, one page with a list of prioritizing sites and at the rights of the app, you get what we call the natural business analytics. So we explain thanks to symbolic AI, why is the system come up with such insights? what, why it has been, prioritized. And I saw we, we will improve over time. We add macro economics indicators. we will have, data on competitors if we get them, et cetera, et cetera. So you will have an explanation of why this insight is easy. It's important why it has be, We think because we are building a prescriptive, tool. It's very important for this to be interpretable.

Isar Meitis:

yes. So I wanna summarize, because we touched on a lot of points and you and I can probably geek about this for two more days. I want, I wanna summarize quickly because I think it's very important for people to understand why this conversation is critical for the future of businesses we tend to think that we're doing a very good job with analyzing data, and it's because 20 years ago we had nothing, and now we have a lot, and we analyze it, and we think we know how to analyze it. The reality is that the way we're analyzing data today is broken or is at least not complete. It's broken because... We're mostly looking at aggregated data, which loses a lot of the small, fine details, and it's not always looking at all the data just because there's too much of it, and it's siloed in different places. It also does not know how to look at qualitative data, so it misses the What happened, sorry, the why it happened, which is the way to get to a recommendation and the tools that exist today, the capabilities that AI gives us today is to solve all these issues with AI. Platforms today, and there's no doubt in my mind that every business intelligence platform in the future will be built on these kind of capabilities will look on a much granular level will look at multiple layers of data, or as Anthony called them dimensions, it will know how to identify anomalities very early as they happen. And it will know how to search through qualitative data to try to figure out what's the root cause and make a recommendation based on common ways of solving business problems. So following a structured process, make a recommendation of what could be. What the actions that we could take in order to overcome the issue or benefit from the opportunity that it has identified This is a completely new era of yes The way you run businesses forget about the data analysis things of how you run a business and it's a fascinating world we're walking into and you can start I want to say something this call was very much technical and it was a lot about how things are happening behind the scenes, but the reality is you can start small today. And I, when I work with companies, I show them that take your sales calls of your best sales person and your two worst sales people, load them to chat GPT. That now has a huge, context window, so we can actually load 300 pages worth of data into it and ask it questions and ask it. What's the difference between this person that's doing very well in these sales calls versus those two people that are not doing very well? what do you see? And it's something that's for a person is impossible to do because it will have to read or listen to hundreds of calls. And ChachiPT will give you an answer of what are the patterns that it identifies within 30 seconds. So the things that you can do when it comes to data analysis with AI are game changers right now, and it will just keep on getting better as we move forward. Anthony, this was really interesting. I really want to thank you for sharing what you guys are doing. I really want to thank you for the depth of knowledge that you share with us on some of these processes, because, I love to think about these things, but I think that I think about them. Thank you. As a business leader with some data experience and your level of knowledge, because you've been doing this for so long is on a completely different level. And it helps a lot to understand. Thank you.

Anthony Alcaraz:

Thank you for having me. It was a nice conversation. Goodbye. Ciao.

Isar Meitis:

Amazing. Thank you so much.

Anthony Alcaraz:

Thank you. Amazing. Thank you.