The Exchanges

Q many layers, I guess, I guess sites and started in. I believe. So this, uh, it's been a lot of product bills, um, over the years. So as of, as of today, um, so maybe let's start with the cloud stuff since you, you mentioned it. So is, is part of the idea that this can work, work in a hybrid environment, like on-prem and cloud, multi-cloud is that correct?

A Uh, that is correct. So, so we want to fit within our custom customers changing technology ecosystem. Um, I would say over the years, so, so Sisense came out of stealth mode and with their first version of their product in. And as they evolved the product, which was analytics at its core, they noticed this gap in performance within organizations. There wasn't a lot of, um, A lot of big data handling going on within the platforms that we're currently in the market. And so they introduced something called the elastic cube, which is a very intelligent and powerful in memory engine. So now fast forward a few years, um, you start to see redshift, big query, snowflake, and people are investing heavily in those. And so what Sisense always did a great job of was seeing into the future. And I unfortunately take no credit for this. I just am lucky enough to join at this, at this stage. And so now what size sense does is they hit that data live. So keep your data in snowflake and we can go work on top of that. Um, but you know, Matt, as well as I do, everybody's data is not in one place. So, so where we are today is we have this great powerful in memory engine. If you want to leverage that, we also can hit your data live and you can do it all within our platform. And so as people migrate data, they can use us as they start to move their entire business to the cloud. We're completely micro…

AI assessment note: “Uh, that is correct. So, so we want to fit within our custom customers”

Q by everyone. Um, and so I'd love to start at the super high level and talk about business intelligence and what it is, because, you know, it's one of those terms that Um, everybody uses, but when you actually ask people, okay, what is it exactly? Uh, it's, uh, it's actually, you know, very few people, people actually know the, what it does exactly. So, um, what is business intelligence?

A It is a loaded, loaded term. It's a term we used a lot in the past that we stopped using and now we're using again. Um, but from my perspective, It includes everything from business analytics, um, understanding your data to drive business change, data mining, understanding trends and patterns within your data, which is super important right now because we're seeing data grow at massive scale, particularly in the cloud. It does include visualization. A lot of times people tie BI just to dashboards. So it does include the visualization component. And then there's a bunch of tools and infrastructure around it. And the piece that a lot of people miss as part of BI is, is best practices and processes. And that's, that's more of the historical version of it. We've seen an evolution over the past few years. It includes things like data prep, data storytelling, descriptive analytics, understanding what is happening right now. And we are starting to see the BI in the more of the data science world collide to include more of predictive analytics. So don't just tell me what's happening now. But tell me what's going to happen next. And then prescriptive. What should I do about it? Give me some hints about what I can do to get ahead of whatever is about to change. And so all of that is starting to come together and be encompassed as part of this world we call BI.

AI assessment note: “from my perspective, It includes everything from business analytics, um, understanding your data”

Q Thank you. I think that's going to be very helpful for, for folks. Um, So there was a big wave of consolidation in BI in, in, in, in particular. So obviously there was a massive acquisition of Tableau by Salesforce. Um, it was the acquisition of Looker by Google, uh, but also the acquisition of a clear story data, uh, and zoom info. What, why do you think that happened?

A From the, from the cloud perspective. So let's start with Salesforce, Google, Um, their value in what they're looking for is to bring data to their ecosystem. And what do you do once the data is there? Typically analytics on top of it. So it's a natural fit that we see them wanting more, um, analytics as part of their ecosystem. I will say where Sisense is actually very unique and fits in really well in this market is we're not tied to one cloud. We're able to work across the clouds. A customer's data is typically not in one, in one place. And so from a, why did those acquisitions happen? It makes total sense from the platform's perspective. Um, in some of the cases, some of the other ones that you named, um, it was, it was about consolidating the skill sets that were needed or pieces of their platform that they think we're missing. One that you missed was Periscope and Sisense. So Sisense made an acquisition in of Periscope data. Um, it was a big one. So we actually consider it a merger. And the reason we did that is Sisense is known from an end user perspective as drag and drop analytics, you know, being able to drag and drop and we write the SQL queries and give you back the visualizations. Periscope took the opposite approach. They said, there are hundreds of thousands of people with coding skills like SQL R and Python. We want to enable them to type code and get back the v…

AI assessment note: “their value in what they're looking for is to bring data to their ecosystem”

Q Very, uh, very good. All right. So there is an ingestion layer. There is, um, a, uh, an engine, uh, that does a lot of the cranking, uh, and, uh, get smarter over time as we discussed. What's on the sort of the presentation layer of it. We got dashboards. What, what, how does it manifest?

A So we have a pretty strong viewpoint on this, um, based on the success we've seen within our customers. We of course provide dashboards and we have a mobile experience. We have the ad hoc analytics experience, but what we see our customers asking for and where we see the market going is People want the analytics to come to them. They don't want to stop what they're doing and go look at a dashboard and come back to what they're doing. That's why we're seeing a low adoption still today of analytics. And so Sisense has the viewpoint that we need to do more to bring analytics to the people at the right time and where they're spending that time. That could mean, you know, a large portion of our business is embedded within products. So embedded within, um, the products you use every day within internal portals or CRM systems you use, and then also getting intelligent about doing things like generating insights for you and sending you alerts on your phone. And so our viewpoint very much so, um, as, as our current and future part of our roadmap is going beyond the dashboard, but the dashboard of course is step one. Um, and we do provide that.

AI assessment note: “provide dashboards and we have a mobile experience. We have the ad hoc analytics experience”

Q And from a product as a product leader, how do you think about the various personas that you need to Address because I think you guys have this concept of builders, which is really interesting, but that's, you know, there's like BI folks, there's like product folks, there's engineer folks. How do you, how do you serve all those different, uh, personas?

A Well, it's a challenge in both of my hats that I wear on the product side, who do you build for? And on the marketing side, who do you market to as part of that buyer's journey? But I would say, um, there's three different main ones, um, The person that's going to find more of our periscope like code driven technology is, um, the data teams. So the people that typically have, you know, some sort of data title data engineer, uh, they, they, they're called something different everywhere. That's the type of persona that really just wants to be able to get code in there and quickly get their answers and then share with others on the BI side. It's typically, um, it's typically the head of analytics or the manager of analytics. Sisense is, it is a land and expand, meaning we start in organizations and we get larger, but it's not the same as some other products. Some that you've mentioned where it's like one person that finds us, right? That one person, because they downloaded something, it's more of the, an organization has an issue and they want to bring us in to get everybody together to collaborate and solve. And so that's more of like, The manager of BI type level and what they're looking for is I need single sources of truth on the data side, which size sense provides, and I need an easy end user experience. So my team can be quickly successful. And then on the final side, which…

AI assessment note: “there's three different main ones”

Q it's always interesting to, for like people to understand how those, those groups run. So first of all, you have a very, um, interesting title that I personally hadn't seen before, but I'm sure it exists as a way you cover both, you both Like CPO and CMO. Um, what, what, what do those organization organizations look like today at a, at a company that's, you know, scaling like Sisense?

A Yeah, sure. Um, I think from our perspective, you know, you generally see product and engineering as great partners and marketing and sales as great partners. And that always leaves a gap between what's happening in product and how are we messaging it? And so what we're, what we're doing at Sisense is bringing marketing and products together. And so I run the product team out of, they have their own investment areas. Some of that we talked about, and I have a leader of product. I have a leader of the overall experience. And then from the marketing perspective, we have a leader of all of our go to market activities, all of our corporate marketing and brand, um, events and press. And then we have, uh, what we call growth marketing, which is how do we continue to get more efficient and optimize, make sure that pipe generation is happening. And if you think about those five leaders, the importance of them staying in lockstep is, is really critical, particularly at this stage company. Um, and so by bringing that all together, we're seeing, um, massive, massive movement as far as, um, being able to, to really take all of our marketing and all of our product to the next level.

AI assessment note: “I have a leader of product. I have a leader of the overall experience.”

Q And, uh, maybe to close on the, on the, On the product part. Um, so you mentioned AI. So there's like this concept of like learning. Um, I read somewhere where maybe you mentioned it as well. There's a concept of using increasingly AI for like data prep, uh, for just like helping the cleaning of the data. Is that, is that part of the product?

A Yeah, we, we have things like being able to dedupe data and identify issues within data, um, to get the data ready, um, in the right format to be, uh, to be analyzed. We can also change things at the metadata layer within the product. So, um, so all of that is included as part of, of our standard platform. And I think it's, it's very important because even when you have the data, you know, you've used the five trend, you have it within the cloud data warehouse. Most of the time it is not Exactly ready to go. And that's where Sisense is. Um, we call it our elastic data engine. Our semantic layer can really help blend other data and, um, via our AI identify some issues in, in data quality, uh, places to, to improve and enhance.

AI assessment note: “via our AI identify some issues in, in data quality”

Q but ultimately we have three analysts that know how to use the product. And then basically you get access to the, um, intelligence based on where you are in the picking order of the company. So if you're the CEO, sure you're going to have access all the time. Uh, but if you're a product manager, then you just have to wait in line. Is that still what you're seeing?

A So what we're seeing is we want to change that paradigm. So I was just looking at something online that there's over 400,000 data analysts jobs posted. And that's because whomever is saying that is right. Like there's only so many people that can look at the data and interpret what it means. And so you have to wait for their time beyond the visualization. So our viewpoint is we want to actually send the insights and the intelligence in an understandable way. So we want to go and send you, this is what's happening. This is what it means. And here's some suggestions. And so that's where we see this next wave in analytics because we're not going to data literacy is not going to solve itself quickly. And we're not going to see this explosion of trained analysts. And so we need to help by augmenting within the platform, um, to get beyond that.

AI assessment note: “there's only so many people that can look at the data and interpret what it means”