From Gen AI to Gen BI with Omri Kohl, CEO and Co-Founder of Pyramid Analytics

Richie Cotton: Hi, Omri welcome to the show.

Omri Kohl: Hey, Richie. Thanks for having me. I'm pretty excited to share the next hour with you.

Richie Cotton: Absolutely. I'm looking forward to it. But to begin with, I was thinking that there are actually a lot of really great BI solutions out there. Like a lot of the platforms just kind of work. So what's left to innovate in the BI space?

Omri Kohl: So, first of all, I'm not a future teller, so it's hard for me to tell you what's going to be. but I think when you look at the the evolution, of data. And analytics is a big part of that. I think that we're definitely at the maybe beginning of or the middle of the beginning of the next generation of analytics tools, which refers to AI.

So I think a lots of things to innovate in around access to insights and, creating insights. That's one side. I think the other side is getting to the data assets. data became, the next whatever uh, golden era of data is today and people refer to it as, the next maybe oil.

So, drilling into the data or harvesting data or collecting data is definitely another area where loads of innovation will have to happen in order for those two big tectonic changes to happen. On the one hand, getting people to build insights from what they collect in their companies. And the second part is how do you actually.

You know, manufacture the data to make it usable.

Richie Cotton: Okay. Yeah, that's really interesting. And I like the idea that the next sort of evolution is just about making data more accessible. So everyone in your company can get those insights. And it's also interesting that it's like a, it's a kind of multi stage process. You need the data processing upfront and then you have this sort of BI layer at the end and then that's how you're going to get the insights.

So it's like a, it's a whole flow. So you also mentioned AI. We're definitely going to spend plenty of time talking about AI, particularly generative AI,

Omri Kohl: How can you not?

Richie Cotton: I know it is, it's just invading everything. But yeah, are there any other trends you think are like that aren't related to AI that you think are particularly notable?

Omri Kohl: I think that, one thing that is not even related to technology when you talk about data driven culture, it's actually internal behavior. How can you transform people that are very much driven by intuition, they're driven by their experience, they're driven by sometimes conversations with their peers and colleagues to ignore that noise and look at the data and drive their business from that lens exclusively.

And this is one extreme. Obviously the other extreme is completely ignore data. So how do you augment the two behaviors together? On the one hand, build top down and bottoms up data culture. So management will only discuss, I don't know, sales performance. Once they have the data, we're not going to talk about your aspiration.

We're not going to talk about your expectations. We actually want to look at the data and then we can discuss what's that additional layer on top of that. So that's top down and bottoms up. How can I in my organization make sure that the next decision I'm going to make, and people make sometimes, you know, hundreds of decisions a day in their in their line of business, how can I make the next decision using data?

Is it accessible? Is it accurate? Can I trust it? Is it available for me? What do I need to do to get access to the data? So I think that embracing that culture without that, there's no point in implementing any analytics tool.

Richie Cotton: Yeah, so process is always like the hard part and working with people like generally much harder than working with technology, I think, a lot of the time.

Omri Kohl: You know, data literature is probably the most important, difficult first step to even starting to appreciate the importance of becoming a data driven organization.

Richie Cotton: And so you mentioned the idea that sometimes you have people who are completely driven by intuition. They don't look at the data, the other extremes, like, well, we're not even going to talk about what we think until we've seen the data. Is there like a happy medium and how might you go about implementing it?

Omri Kohl: Yeah, I mean, first of all, I think that happy medium is, is proportional. My happy medium might be very different to your happy medium and to someone else's. I think we need to have a very critical Experience. You need to start using data and start making decisions and then look back and say, okay, that decision about that data for that line of business was successful to that degree.

How can I improve it? How can I fix it? So I think constant feedback and constant feedback into the data and into my decision making is the first step to building a much more successful analytics implementation. We're talking about behind, we're talking about AI and those are amazing tools, but eventually If you don't use those tools properly and you don't, utilize the insights that you will receive from those tools, they're useless.

It's just a tool. we eventually will control the outcome and the narrative that we will be creating using those systems.

Richie Cotton: I like that. And just thinking about my own experience. When things are going wrong in some area of the business, that's when I tend to notice my intuition is probably wrong. And that's when I tend to start pushing for like, well, I need to bring more data into this. So, do you have a sense of like, what the point is where you go, okay, we need more data here.

Omri Kohl: I'll take it to the macro level. when, companies are investing the most in their data assets in times of crisis. So, know, macroeconomic crisis is maybe the first step for people to say, okay, I need to understand what's going on in my business. Where am I bleeding? Where am I successful? I want to double down on the areas that I do well.

I want to shut down, you know, line of business that don't perform and kind of when you're cruising along, you couldn't care less. It's fine. Everybody's happy. But in a disastrous moment, in a critical moment, in downturns, that's when you actually want to have super visibility to your business.

And I think that it's really interesting to hear you say, kind of reflecting about your own experience. But think about it from a global perspective. When you look at companies holding off on technology spend. It's true. They will stop spending on all kinds of tech stuff, but they will increase their spend on data and analytics.

Why? Because eventually there is a very clear ROI to implementing and using properly data and data analytics. So I think that those moments are definitely defining moments. for our space,

Richie Cotton: Absolutely. So yeah, when you've got some big disaster happening, that's when the decisions really are critical. You need to make sure you've got the right answers. That's where the data driven side of things comes in most useful. I guess, that leads to a problem. So. If you are waiting until there is a disaster, and then you're suddenly like, well, I need the data, it feels like maybe that's too late.

Do you think businesses need to be investing in data before they get to that crisis point?

Omri Kohl: but first of all, I'm not objective. I want everybody to buy from us, right? So I would say buy anytime, all the time. But think if you're only starting in the moment of crisis, you're too late to respond. Uh, You want to make sure that. At least you built the foundation to be able to act on time in good times and bad times.

It's not necessarily that you need to prepare for the catastrophe. You should also be prepared for good times where you actually see one line of business, you know, exploding and you actually want to go and double down on it. How do you do that? Where? Why? Why does it happen? Which product? Which territory?

Is it the right people? Is it not? Is it my marketing department? So many questions. Is it my engineering group that created some ingenious, you know, idea and everybody just wants to jump on, on that uh, product. So I think that it's a constant investment. If you look at the, the most successful companies in the world, There are eventually data-driven companies. If you look at Google, you look at Amazon, you look at, Facebook or Meta, you know, even Tesla in manufacturing of cars. What made them so, so successful is the ability to collect data, to do something with the data, to build those autonomous drivers.

So all of that eventually is data. And if you want to be ahead of your pack, invest in data. That's what you should take from all that hand waving story. Data is the driver for every successful business today.

Richie Cotton: Definitely with you there on that. It's like, if you're going to be successful, you do need to get your data game 

Omri Kohl: Sort it out. Absolutely.

Richie Cotton: Yeah, yeah. Wonderful. So, We're going to have to talk about generative AI. So, but I know Pyramid, you have this term generative BI. So can you just, first of all, explain to me what is generative BI?

Omri Kohl: Absolutely. So, you know, Gen AI is a something that everybody uses. My kids use it. My, friends are using it. My colleagues are using it. People create homework and people build, you know, new songs and, do lots of stuff. And we were thinking, how can you take that experience, but to the business world?

Why is it a problem? It's a problem because my company's data is not public. It's actually my asset. I'm not ready to open my banking system to, one of the LLM creators, if it's Mistral or OpenAI, I don't want to insult anybody else. So. All of them. I don't want to give them access to, to my assets, but I still want to leverage those LLMs.

So we were thinking, can I actually take the public LLM from one of those big companies? Take my private data from my company, connect the two together, and allow me to have a journey that is secured, governed, enterprise ready, that is not exposing me to any risk outside of my gate, my environment.

And that was the idea behind all of that. And we started, building out. All kinds of, of solutions. And eventually we came out with what we call Gen BI. And the idea behind it is that we're using LLM for free language. You can, go into your analytics infrastructure using pyramid and basically start asking questions.

Tell me how my marketing department is performing for the region A, for product B, for campaign X. And you'll start seeing, and then I want to see it in a pie chart. No, you know what? I want to see the scatterplot. Can you add the linear regression as well? Absolutely. And, and then kind of a flowing conversation, which were very common to do in the public space.

is happening in your private governed data. And say one more thing that one of the challenges in analytics and specifically in BI tools is that there is an adoption challenge, right? When you look at the adoption rate of, of BI tools across companies, It's, I want to say in the 20 percent ish mark, should ask yourself, why aren't anybody using analytics? It's, it's almost, you know, surreal that people still don't, are making decisions from their gut feeling. and because those tools sometimes could be intimidating, they're hard to use, it's not for everybody. Not everybody knows what to do with it.

And I think that the idea of Gen B. I. is adding more and more personas, more and more of that audience, That it's not so intuitive for them to start embracing analytics tools. it's easier for them to email, the analytics person. Hey, can you send me my report on my yada yada stories?

And they'll get it. And GNBI is the attempt. to make that journey for them much more accessible.

Richie Cotton: It does seem like there's a very natural sort of marriage between using generative AI and more traditional business intelligence. It seems with business intelligence, you've got two distinct groups of users. You've got the end users of your dashboards, and you've also got the creators of the dashboards as well, and they're going to need different things.

So maybe we'll go into a bit more detail on how does generative AI help people who are creating dashboards?

Omri Kohl: So I think, again, it's the skill set, right? and we're all acquiring skills as we kind of go along our career and you learn more stuff and, you know, sometimes you do it yourself. Sometimes you lean on other functions. And I think that generative BI allows the entry point to those tools to be much more accessible and easier.

I don't think that if I'm like the top notch, you know, data scientist and I love to write my Python and I love to do those joints and connections and all that fancy stuff, which is incredible. I'm not too sure I will tell, my genie, Hey, please build the next milkshake for me and I'll just drink it.

I'll probably want to be in the kitchen and do it myself. But I think that it just lowers the bar for more and more creators. that's why it's generative BI. Eventually, we're trying to make creation of those, those dashboards, those narratives, those insights. much more accessible for everybody. So in a way, we're almost trying to narrow the barrier between those two groups and tell those people that are not necessarily thinking about themselves as the dashboard creators, that there is a way for them to actually embrace that analytics journey by themselves.

Maybe for the more advanced users that are what you call creators or developers of, of analytics tools. This is just a supplementary or complimentary solution for them at least. You wanna start somewhere. So instead of all the, kind of legwork that you need to do to build your first dashboard, imagine that you're adding to a, I don't know, a board meeting and you need to build a PowerPoint for that.

The board, if someone gave you the first draft and told you, all right, start working off that rather than from scratch, then, okay, it makes your life easier. So I think for the very basic people, it just allows you to do much more. And for the advanced people, it maybe takes away some of the mundane prep work that you would need to do.

Richie Cotton: Okay, yeah, that certainly seems to reconcile with my experience where if I know how to do something, I can just do it. But if it's something where I've not done in a long time or something that's completely new to me, that's where Germ2Buy is really helpful because it can give me that at least the first step.

Okay, so, switching to how it benefits users. You mentioned the idea of just being able to ask questions about your data set and just gives you an answer. So that sounds very much like sort of chat interface rather than a dashboard. So do you think that's the future? A dashboard is going to go away now?

Omri Kohl: I'll go even one step further. and I think the bigger question is. would AI kill BI, I think that's eventually maybe where we're headed. So first and foremost, I'm not too sure that the dashboard is dead. Like the password should be dead. I actually think that dashboards are still part of the way we consume data, right?

Rather than just simple numbers. We like to look at visuals. We like to see red and green. We like to see bad and good. We like to see a thumbs up or a thumb down. We So I think that the graphic representation of our performance will stay and I think it's, actually a very useful way to maybe a first snapshot of my business.

think that the drill down and kind of the further exploration and starting to ask questions and starting to go through that rabbit hole of your data and your, your analysis. will be, think, simpler and easier. And maybe for everybody, once you can do it with a chatbot or, you know, we have, and I encourage people to go and search on YouTube for Pyramid GenBI demo, you'll see us actually talking to Pyramid and asking, voice to text to analytics.

Questions. Some of, some of them are complex questions. Like do that math for me and do this and do that. And I think that might completely change and revolutionize the way people actually interact with data.

Richie Cotton: Okay. So it sounds like there's a difference between exploratory data analysis and explanatory data analysis. So if you're just exploring your dataset, you're just asking lots of different questions, you're not quite sure exactly what you want at first, then the chat interface makes more sense.

But I guess for something like, your core business metrics, like you're always going to want to know, like what your monthly active users are or something, and that's not going to change. So maybe the dashboard is a better solution in that case. Is that sort of about right?

Omri Kohl: Yeah. I think you're, you nailed it. I mean, I think that for my first glimpse into what's going on in my business, Show me a dashboard, show me happy face, sad face, show me I'm fired or I'm getting a raise. That's all I need to know. But if I actually want to further understand why I got fired or why I got a raise, then I think a chatbot experience or a voice to text experience or anything that is much more intuitive, almost the conversation that you and I are having, why not have it with your data?

Richie Cotton: Okay. That certainly makes sense. And it does feel like there's always been this sort of gap with BI where you create a dashboard, but actually what you want to get to is maybe making a decision or finding out some reasons why. Okay. And the dashboard has not been able to provide that. So can you just talk me through like how you close that sort of last mile?

Like, how do you go from here's some results from data to this is how I make a decision.

Omri Kohl: We're definitely, we believe in, in decision intelligence, not just decision making, but actually we call it DI. So too many, PI and AI and LDI, but the idea of decision intelligence. is that eventually there is a constant feed or feedback into your data.

We made a decision, you did something, it feedbacks to us, it tells us what improved, what didn't improve, and we continue to do that. So and maybe connecting it to AI, I think that AI could definitely give you constant feedback. Not just when, I think when we're saying AI, people think about, you know, the chatbot, but AI can, or machine learning or non rule based automation can happen in multiple layers of your data stack, of your analytics stack.

And we can build semantic layers. We can acquire clean and fixed data with lots of machine learning and deep learning. We can create those semantic layers on top of that and build you a map of your conversation with your data, not verbally, but build your mapping of your schema, then we can create automatic insights.

so I, I think AI and non rule based automation exists across your entire data stack. And once you have that implemented, Then decision making becomes almost an inherent part of that. Why? Because let's say that, to me, sales is almost the easiest example. So, you know, I'm seeing one region of mine kind of dropped their quota and they're unable to sell.

And I want to understand why. And one of the reasons is that maybe it's too expensive for that region. Why? Because. Maybe people in that region will spend less. buying something. So if it was a good system, it would tell me, listen, it seems like you're losing on pricing. Why don't you adjust your price list. We, call it in our space prescriptive analytics, but basically the idea is can I prescribe a solution to the insight we found? The insight was you're losing business. The prescription was change your pricing. I did it fine. I listened to your fancy machine. They told me to reduce the price.

I did it and guess what? Nothing happened. I continue to lose business. So either, you know, the prescription wasn't good. The data that I was using to make that decision was not great. Or there are other parameters that are the reasons why I'm, unsuccessful in this region. And I think that back to the conversation we had at the beginning of data culture and am I going to listen or not going to listen?

think that decision making using systems, is a discipline. Like you, you really need to decide that you're willing to go about taking your analytics journey to the next level and actually drive your business using those, those insights. And I think that AI could eventually go, it might sound like a weird comment, but could eventually add more comfort to people leaning on, on those tools to make decisions.

Why? Because it almost feels like I'm talking to you. I'm almost getting insights from someone who cares, someone who actually understands the business, understands me. And then when he tells me, okay, reduce the price, I might do it even faster and smarter, and then maybe it will work.

Richie Cotton: Yeah. So I really liked the fact that you can make use of the subjunctive AI chatbots to sort of ask questions, like they can give you advice. The tricky part is. When the AI doesn't have as much context about your business as you do. So you mentioned like the idea of, well, does reducing price work well or not?

Unless the AI understands like the state of the market and why your price is the way the price is. It may just make stuff up and then, because it speaks confidently, whether it's right or wrong you're not going to know the difference. So, can you talk me through how you might go about, like, making sure that AI has the right context in order to give you a sensible decision?

Omri Kohl: Yeah. First of all, I, I don't think that there is a easy answer to that. I think that AI is in its infant days and there are lots of hallucination and lots of, you know, reasons why we should use it carefully and we should, decide how we're going about the usage of the insights and that stuff.

So I think that from a technology standpoint, lots of, systems has been built to try and reduce the risk of using AI. RAG is one idea behind it that kind of framing the way you use those underlying data assets. to make sure that you reduce the mistakes, if you will, or the hallucinations.

So that's on, on the tech side. think that given that it's still humans to machines we also need to be somewhat, critical about what we're using and how we're using and what we're doing with with the insights that were created. And shouldn't we actually fall in love with it and use it 100%?

And I'm only going after my chatbot, what it tells me. It's, you know, the words of heaven. Or I'm augmenting my experience. And I think that, you know, Today, augmented analytics is probably most common practice that companies will embrace. Yes, we're giving you tools that will make your life faster, smarter, better.

 You'll be able to understand your business in a, you know, in a heartbeat rather than if sometimes it took you, I don't know, months to get some insights on what's going on in your business. So we made it super fast, super accessible, super valuable, but also it adds lots of responsibility on you to make sure that you're going to use it in a proper manner that, you're going to continue to question the insights that you receive.

So I think my advice to everybody that is using AI is augment or AI in our space is to augment. your experience with what the automation is kind of driving toward.

Richie Cotton: Okay, so this is a fairly frequent topic on DataFramed is like, do you want the AI to augment your own capabilities or do you want it to replace you? And there seems to be a divided opinion on whether AI and humans should work together. Actually, that's maybe the most common opinion, but the other opinion is, well, long term we want complete automation.

We don't want to have to have humans in the loop at all. I was wondering where you stand on this. Like, are there any sort of specific cases where it is better to have that human plus AI combination when you want to completely automate and go AI only or software only?

Omri Kohl: I think that, you know, kind of the core story is what is the use case? It is use case based driven to take two very extreme ideas. One would you put your life in the hands of a AI or a robot? And on the other one would you let an AI make a decision on what's your meal for lunch today?

And, and I think, you know, it maybe, silly ideas, but eventually behind it is how much of the decision would you live in your hands or in the machine hands. And I think that eventually what we're looking at in business is mission critical decision making and or mission critical application.

So if it's going to fundamentally impact the overall business, I think I would do what we typically do, A. V. testing further exploration. cross pollinating of data and data assets coming from various places. I would do a thorough research before I let, the AI decision to drive my, my next step.

And maybe eventually, I learned that it said exactly what I was finding out from my research and then with that confidence. In what the AI is doing, but I'll definitely not fully augment or not fully allow my AI or my data or my analytics. run the decision making. And if it's, you know, what should we order for lunch?

roll the dice for me. Like, you know what I like, you already know me because I shared with you my entire life on, on Facebook and Twitter and who knows what, and you know my preferences. I roll the dice and I'll be very happy to be surprised with what you ordered for me for lunch. So I think it's, it's really about the use case.

I think that there are lots of practices and processes to prevent the risk of taking the wrong decision driven by AI, and there are lots of gates that you should cross when you do those, decisions. But imagine it's really about the use case.

Richie Cotton: So, just think about is, what's your use case? What's the solution that's actually going to work? So yeah, going back to your example about the heart surgery, I've seen those dancing robot videos. They look very cool, but I'm not sure I'd let one of those things like operate on my heart. But on the other hand if I had a heart problem, then a pacemaker, which is a completely automated solution, is perfectly valid because it does actually work and it's going to work better than having someone like a human just punch you in the heart, like, once a second.

So, yeah, pick the use case and see what the best option is there. Getting back to this idea of decision intelligence and making decisions using data. It seems like the hard part is scaling this. So I think every organization, you're going to make some decisions with data. How do you be more consistent about it?

Omri Kohl: Well, one of the challenges with analytics and, data analytics is that it lives in silos. Because of the way analytics tools have been built. They were built to support either a specific function, specific need a specific group of people, maybe a department. And, you know, when you look at upper mid market and enterprise companies, usually those big companies, they have everything under the sun.

And. not really built for consistency. It was built to, to solve a very anecdotal problem that I have. So one of the challenges is you know, think about it. You have one CRM system, which CRM, by the way, used to be also like, 20 different tools, Mike. customer list and my Rolodex and my customer leads and my this and my that.

And it's slowly but surely consolidated into a singular solution, CRM, ERP, the same. And analytics is still fragmented. It's all over the place. And I think that What we're seeing today as, as a trend is that vendors are starting to figure out how to consolidate different aspects of your analytics stack into a similar wholesome experience from a technology standpoint.

So that's the first step to try and solve for. inconsistency because if my data acquisition, my ETL tool is a separate entity and my dashboarding tool and my data discovery tool and my reporting tool, and maybe my data science workbench is a different tool. All of a sudden, each one of them is a whole entity.

They don't necessarily talk to each other, but they need to be connected. It could be quite a challenge for companies to maintain the flow and consistency of, of of analytics experience across those, tools. And I, so I think, One challenge that I think will definitely need to be resolved over the next few years.

I think we as a company, that's what we believe will happen. And that's why our infrastructure is built like that. As a kind of a wholesome experience with multiple disciplines in the analytics space combined into a singular platform. But put aside, I think that's where the market is headed.

It's headed into consolidation of your analytics and data stack into a platform. So that's one. Second, think that the adoption of analytics across non technical users is also very important. usually you have the practitioners that the people that you mentioned earlier that are building those dashboards or building those insights, building those narratives for us.

There's it people that will provision and facilitate those solutions for us. But eventually the adoption rate is by, the line of business, the people that eventually will need to use those systems. And I think that if I'm using data to make decisions, and I mean, let's say the marketing department and my colleagues will not do that.

then it's a completely inconsistent experience. Again, we're back to people are eventually the hardest problem to fix. So think that consistency is on the tech side and it's also on the human behavior side.

Richie Cotton: Absolutely. So I, I agree again, yeah, the humans are generally harder than technology, but just on the, the technology side for a moment, it sounds like you're advocating for having a simpler business analytics stack using fewer tools.

Omri Kohl: I'm not so sure that it's simpler, but it's more consistent. So what is your, your analytics stack typically? It's few disciplines. It's the data acquisition you acquired your, whatever, your EDW or your data lake and now you have data and you wanna access it. So you access it with an ETL tool and you wanna build out some models from it so you can actually start exploring it.

So you put on top of that ETL, now you build, you acquired it, did a discovery tool and then you wanna report. builder to push it out to your customers, maybe. So now you have a reporting system and now you actually want to build all kinds of um, predictive analytics capabilities. So you bought your data science workbench system.

And each one of them is a different vendor. It's a different story. It's a different technology. They don't necessarily talk to each other. So I think one problem is super siloed implementation. It's all over the place. It's disjointed. It's very difficult to maintain. our space, it's called TCO, Total Cost of Ownership.

It's not just a license cost, but it's actually maintaining the entire infrastructure intact and actually operational and usually in place. Why? Because it was never meant to be connected. so that's one, thing that I think we as a company are trying to address and so forth. But I think that's where the market is headed.

The market is looking for not necessarily the best of breed for each one of those tools, but actually the best experience. And the best experience will come from systems that will work, that will give you insights on the fly, that will connect to the right data in near real time or real time, that will be available for you, that will cater to any persona in the company and not just to the, you know, most advanced users.

And then it goes on and on and on. So I think eventually simplifying the experience and the ease of use and the democratization of access to those tools and systems is what's going to make analytics, you know, it's almost sometimes I think about it, I wake up with shivers at night, then maybe a decade ago, analytics was nice to have.

It wasn't like a massive system. It was nice, fine, you know, build me a nice pie chart. It's not about the pie chart. It's about building a consistent decision making process that actually will help you compete in your industry. That will help you be successful. That will help you make the right moves in the market.

It will help you build the right products and this goes on and on. So I think luckily today, analytics is a mission critical application. You can't move anywhere without those kind of systems.

Richie Cotton: And so I guess moving to the people side of things. So since this is the hard part, I guess you want everyone in your organization to have some kind of data skills and maybe even business intelligence skills. So first of all, what do you think are the most important skills that you think people need to know, like, throughout your organization?

Omri Kohl: You need to be curious You need to be able to ask questions. You need to be critical about yourself and your business. but those are characteristics, not everybody has those. Some of us, you know, live differently. So think that even if you don't have those ingredients in you, analytics helps you.

It helps you to be curious. It helps you explore. It helps you ask more questions. It helps you be much more critical about what's going on in your business. So need to maybe at least keep an open mind that it's okay to ask questions. So that's one. Second think that, analytics is data eventually need some capabilities around the logic of what the underlying assets our build off.

You know, what is a data lake and what is the difference between a data lake and a a data warehouse and why it's important to use one or the other, and maybe both. so I think having the basic understanding, at least for people. in the line of business, it will give you context of what is it that you're using.

And then, at least understanding the foundation of what is analytics and how do I connect to the data and ask questions and get some, some insight. So at least I won't think that it's complete mumbo jumbo. Some of it actually does make sense. and maybe the last part is how deep am I going to be as a practitioner in the decision intelligence.

space. I actually think today everybody needs to know how to ask the next question from a data and data analytics perspective. So if you're using a BI tool and you got something, your business, you know, is underperforming. You want to know why you need to know how to ask the next question.

Rail down, slice, dice check your hierarchies. figure out the next layer of information and more data into your data lake. Ask someone to pull your, your marketing campaigns, not just your sales performance. Data. So think that cannot not know those basics, at least I

Richie Cotton: like the idea of asking questions being an essential skill. I guess as a podcaster, I'm biased, I ask questions all day. But Do you think generative AI is then changing the kinds of questions you need to ask, or the skills you need in order to be able to ask good questions?

Omri Kohl: think it opens spectrum of questions you can ask if before, analytics tools or BI solutions have functions. Maybe a hundred functions, maybe a hundred thousand functions, but it's limited to the amount of functions that the vendor developed inside your BI tool, and that's what is available for you.

I think the beauty of generative ai, or in our case gen generative BI, is that you're using your language and then you can ask whatever you wanna ask. You can build whatever context, you can add whatever context to your questions that you wanna ask. So I, I think that it almost created a limitless.

world of question asked and in that perspective, I think that, you know, as much as it sparks your imagination, you can ask, to connect things that might should not be connected. business is exploding. How many people showed up in the office this morning? And maybe you'll see that there is a correlation between those two things and maybe not.

And, you know, think that. The beauty of using kind of LLMs to ask business questions is that it really can bring in lots of unexpected answers that are valuable.

Richie Cotton: Absolutely. So I love that it's kind of opened up the scope of questions you can ask. And it just seemed like imagination or creativity is now an increasingly important skill. Okay. So, how is this changing the role of being a business analyst? Is the job different now with all these changes in the tooling?

Omri Kohl: I think, is AI going to take over different roles and functions? in our businesses. And I think the answer is yes, it will. the agriculture revolution changed the way we, you know, we plant and we harvest our crop. The industrial revolution changed people's occupations.

The internet revolution changed the way, you know, we communicate and learn and build businesses. So I think that, yes, it is an, an everlasting changing story. And I think that we as humans and practitioners will need to learn how to adjust and adopt. To the fact that some of the mundane and some of the other jobs will be replaced by systems and AI or robots or whatever it is.

but I also think that back to your question, is it going to change or, or replace? the analyst, think this is where I think it's going to be difficult for AI to replace people that are curious, that are, excited, that are interested, that will ask the non trivial questions, that will bring their own perspective into the mix.

And I actually think this is back to augmenting humans with machines. I do think that the successful data analyst people will be the ones that will take advantage of the ability to actually build out faster, smarter, better insights for their company, augmenting their own capabilities with what the systems can produce.

Richie Cotton: Okay. That's interesting. I suppose, yeah, I'm not sure whether. AI can reliably, discover the question behind the question and be able to generate insights uh, on that. Okay so, beyond just individual data analyst or business analyst roles, do you think data teams are changing now because of changes in technology?

Omri Kohl: I think that a, there's much more focus on data. And because of that, they're becoming much more strategic and valuable for companies. So, you know, if historically maybe had the data group only part of it today, it's their own division, right? There is a chief data officer. There is a CDO now. Now it's probably in the last decade or so, but I think it became, you know, super evident that data is a significant asset.

If it's a significant asset, your team that runs your data asset, your data estate, your analytics estate, becomes your critical asset. So that's one I think that it wasn't the case, a decade ago. I also think that, you'll find the need for much, many more data scientists, And there is a shortage of them.

So, the world created citizen data scientists, which is basically people that maybe have less experience or are starting their journey in the data science world. And they're part of that, that group, that team. So I think that new roles are emerging and kind of creating in the data group.

and I think they're becoming super valuable and critical. They're probably the hardest to find.

Richie Cotton: Okay so that's interesting. I like the idea of treating data as assets and, you know, you really think about what's the value there. Do you think there's anything that sort of traditionally worked for data teams that you think, okay, that's no longer the case, just stop doing this now?

Omri Kohl: Yeah. I mean, I, I think there may be lots of, lots of things that have changed, but the fundamentals didn't change. the fundamentals are keep the data, refine the data, acquire the data, distribute the data, use the data to run your business. Those things are maybe hasn't changed forever. What changes, how it's done.

And I think the how is a, once it became an asset, then people treat it like an asset. And it's not just a, afterthought, it's actually something that is super well architected thought through, it's secured, it's governed, it's sometimes it's a secret, sometimes it's compliant, it's.

Probably the things you hear the most when it comes to data is data breach, right? Every time there is a leak of some people's passwords, the world collapses. Imagine what happens if, you know, you're one of the credit card companies and your data has been breached. You basically lost your business. That's your business.

Your business is that data. So I think that data as an asset changed everything around it from human perspective, roles and responsibilities type of um, knowledge and know how that you need to have in order to treat that universe eventually to me, data today is everything,

Richie Cotton: Absolutely. And yes, certainly data breaches, incredibly worrying, far too common. Cyber security is just hard. So are there any things you think that data people need to know about security?

Omri Kohl: that it's important and you don't want to breach it. And you need to invest in it. I don't want to go into technicality of it, but I think that because data asset. people steal assets. They don't steal nonsense. They don't go after things that will not be valuable.

They're going after valuable assets, which means that the same people that would like to steal your car or maybe not the same people, but people will make their life experience to try and steal your, your data assets. And. the more your an asset is important and critical, the more security gates you need to put on top of that.

Obviously, I'm, you know, we're not in the cyber space, but there is a reason why cyber is growing double digits zero over year. It's because. the amount of attacks that are being done on, data today is probably growing more than the double digits that cyber is from.

Richie Cotton: Okay. Yeah. I like the analogy with the car, cause certainly you wouldn't just leave your car with the doors unlocked, but maybe that happens a bit too often with, with data is like, well, yeah, it's not protected well enough. all right, super. So, just to wrap up, what are you most excited about in the world of BI?

Omri Kohl: Yeah. I mean, listen, everything we talked about, for the one I'm super stoked about the generative BI side. And I think that it's going to, been a gradual progress and in BI. I think generative BI is going to be a quantum leap. I actually believe it's going to allow everybody to use insights to drive their business.

If it was difficult and cumbersome and annoying and unexplainable, how you got there, what to do with it, I don't want to use those BI clunky tools. All of a sudden, with those generative BI systems, you can actually give it to everybody. And I think once it's in the hands of everybody, everything changes.

So to me, you know, GenBI is probably the most exciting. It's a dramatic change in the analytics space.

Richie Cotton: BI for everyone. It's a, it's a nice thought. Okay. Wonderful. All right. Thank you for your time Omri.

Omri Kohl: Thank you, Richie. I appreciate it. Thanks for having me. It was it was super great. Thank you so much.