Sisense is a fast-growing business intelligence startup that was ranked #31 in this year’s Forbes Cloud 100, and reached unicorn status at the beginning of 2020 through a $100M Series D led by Insight Partners.
We’ve had Sisense speak twice at Data Driven NYC over the years, first CEO Amit Bendov (now CEO of Gong) (video of the talk here) and then new CEO Amit Orad (video of the talk here).
With all the recent progress, we were particularly excited to hear the update and welcome Ashley Kramer, who recently joined Sisense as Chief Product and Marketing Officer, after a very impressive run at Amazon, Tableau and Alteryx.
We covered a bunch of topics, including:
- What does “Business Intelligence” actually mean?
- The convergence of BI and data science
- How does Sisense position in the context of the consolidation of the BI industry (hint: multi-cloud and focus on different personas, including business users, data analysts and more technical folks)
- Where Sisense sits in the modern data stack
- How Sisense has been building data network effects with its knowledge graph
- Dashboards are great, but embedded analytics are better
As always, Data Driven NYC is a team effort – many thanks to Jack Cohen for co-organizing, Diego Guttierez for the video work and to Karissa Domondon for the transcript!
FULL TRANSCRIPT (lightly edited for brevity and clarity)
[Matt Turck] I’d love to start with your background. You’ve done really interesting things in the world of data analytics and maybe starting from the beginning, your first job was at NASA?
[Ashley Kramer] It was, and it’s funny, that’s the thing that people find most interesting, which I don’t know what that says about me, but yeah. So I would say I’m actually the opposite of Amit [Bendov, CEO of Gong and previously of Sisense, who spoke at Data Driven NYC the same day as Ashley], where he seems to be more of a serial entrepreneur and likes to start things. I would describe myself as a scaler. So I typically get brought in at the stage a company that Sisense is today and beyond. But if we go back and start at the beginning, yes, I was an engineer in my early days at NASA Oracle. And it was a manager at Oracle that shined the light for me that I’m probably not meant to talk to computers, I’m meant to talk to people. So I got a great opportunity to build out Kindle in the early Kindle days at Amazon.
And more relevant to my experience, what I learned at Amazon is that if there isn’t data to back it up or customer evidence, you’re not doing it. That’s just the Amazon way. And I learned the importance of cloud because AWS was starting to grow up. I was an early internal beta customer. So next step, I met the Tableau founders who are wonderful people, and they convinced me to come and build out a cloud solution for Tableau. This is back in the 2013 timeframe. They were actually a little bit early to the cloud analytics market, but I really, really started to understand the value of what you can do with data to drive change. That was really, really interesting, I was there for about four years. And then the final step before my stop here at Sisense was I was head of product at a company called Alteryx, a data science and analytics platform. So what happens beyond descriptive BI type analytics is the next step of the data science world. Predictive, some of the stuff Amit talked about, machine learning. And so I spent three years building out a data science platform with them.
When you think about my experience, cloud, plus analytics plus data science, when I met Amir, who sounds like you have also met. A wonderful, wonderful person and he sends his love, he started telling me about the Sisense journey and what Sisense has been doing since their origins and where they’ve come in the past few years, it was just the perfect fit. So I’ve been at Sisense now as the chief product officer and chief marketing officer for about two quarters and excited to talk more about it today.
[MT] What a great journey. This topic of business intelligence and predictive analytics feels like a great place to start. I’d love to start at a high level and talk about business intelligence and what it is. Because it’s one of those terms that everybody uses, but when you actually ask people, “Okay, well, what is it exactly?” Very few people actually know what it does, exactly. So what is business intelligence?
[AK] Business intelligence 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. But from my perspective, it includes everything from business analytics, understanding your data to drive business change, data mining – understanding 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 tied BI just to dashboards. So it does include the visualization component. And then there’s a bunch of tools and infrastructure around it.
The piece that a lot of people miss as part of BI is best practices and processes. 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. We are starting to see the BI and 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. So all of that is starting to come together and be encompassed as part of this world, we call BI.
[MT] So historically, would it be fair to say BI has been about what’s happening now and what happened in the recent past? Whereas data science would be the world of more predictive, what’s going to happen in the future?
[AK] Historically, those worlds have been separate. But what we’re seeing is a gap that there’s just simply not enough data scientists. So what we’re seeing is more of these data science-like algorithms being put under the hood. This is something Sisense believes in. We are not a data science platform, but we have a data science team, a great data science team that is responsible for putting those algorithms into our platform. So any user, I’m not a data scientist, but now I can do forecasting within my BI platform without having to go figure out how I can have a data scientist help me get there. And so it’s starting to close that gap a little bit, which can largely be seen as a skill set gap.
[MT] There was a big wave of consolidation in BI in 2019 in particular. So obviously there was the massive acquisition of Tableau by Salesforce. There was the acquisition of Looker by Google, but also the acquisition of Pure Storage Data and ZoomInfo, why do you think that happened?
[AK] From the cloud perspective, let’s start with Salesforce, Google. 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 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 place. And so from a “why did those acquisitions happen”, it makes total sense from the platforms perspective. In some of the cases, some of the other ones that you named, it was about consolidating the skill sets that were needed or pieces of their platform that they think were missing.
The one that you missed was Periscope and Sisense. So Sisense made an acquisition in 2019 of Periscope data. It was a big one, so we actually consider it a merger. The reason we did that is Sisense is known from an end user perspective as drag and drop analytics. Being able to drag and drop, 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 visualization.” So it was sort of the perfect marriage to bring those two together, because in organizations you don’t want to have two, three, four different analytics tools, you want to be able to have one platform. And so from our perspective, that’s why that merger made so much sense. It was also a complete SaaS product and we’ve been investing more on the cloud. And so we got a complete team with cloud DNA, which is very, very important as part of our overall company and culture.
[MT] Let’s dive into the Sisense product itself, which is so interesting because there’s so many layers. Sisense started in 2004, I believe so there’s been a lot of product built over the years. As of today, maybe let’s start with the cloud stuff since you mentioned it. Part of the idea that it’s going to work in a hybrid environment in on-prem and cloud, multi-cloud, is that correct?
[AK] That is correct. We want to fit within our customer’s changing technology ecosystem. I would say over the years, Sisense came out of stealth mode with their first version of their product in 2010, 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 big data handling going on within the platforms that were currently in the market. And so they introduced something called the ElastiCube, which is a very intelligent and powerful in-memory engine. So now fast forward a few years, you start to see Redshift, BigQuery, 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 stage.
So now what Sisense does is they hit that data live. So keep your data in Snowflake, and we can go work on top of that, but you know, Matt, as well as I do, everybody’s data’s not in one place. 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 a completely microservices based architecture. Another decision made to stay ahead of the game was a rewrite of the platform. So now we can fit into any cloud ecosystem in scale as the data and the analytics scale. So I think I’ll give the founders, Amir and the team that’s been here for many years credit for always seeing a few steps ahead and keeping up with the market. And I’m sure we’ll talk about Roadmap, we will continue doing that and innovating on to stay ahead of the market.
[MT] Just to make sure I understood this right and to paraphrase. So you had your own, and you still have your own engine, which is ElastiCube, but you don’t necessarily have to use it, you can plug Sisense directly into a data warehouse. But at the same time, you don’t have to have a data warehouse. You can get the data from multiple different places. Okay. And otherwise, maybe to place this in context for the people that attended the session last time we had George, the CEO for Fivetran. So there’s this whole concept of a modern data stack, which starts with sources of data. Then you have companies like Fivetran and Stitch that extract the data and load it into the data warehouse, whether that’s BigQuery, Redshift or Snowflake. And then the BI stack, including Sisense stands on the other side of the data warehouse. So you’re working at environment, but also you don’t have to have that environment either.
[AK] That’s right. And Fivetran and Stitch are good partners of ours along with all the cloud data warehouses, because you often see the three together as a solution – Sisense, Fivetran, Redshift, for example, but that’s exactly right.
[MT] Okay. So we talked about ElastiCube, there is Sisense Live and Sisense Data Pipelines, what do those do?
[AK] So live connectivity would be what we described as sitting on top of the cloud data warehouse and being able to do the processing right on that. The data pipelines that we can write as part of our Periscope technology integration. So being able to write what we call materialized views and then right back to Snowflake, Redshift, or what have you. So being able to do some of the data pipelining and manipulation right within the Sisense platform, and then sending that back to the data warehouse, and that’s important for optimization of your analytics within Sisense.
[MT] Is that because you do some level of transformation in Sisense? So the way Looker for example, as a tool to do that, like you guys have a tool to do that?
[AK] Yep. It’s a slightly different approach, but it is the same concept in allowing people to have more accessible and easy to use analytics right within the platform. And so the pipelining capability, as I mentioned, we’ve always had it as part of what we call our semantic layer, but the new capabilities that we’ve added is being able to write back to the cloud data warehouse.
[MT] And what is the knowledge graph?
[AK] Oh, the knowledge graph is one of my favorite things to talk about. So, again, something that the very smart early team at Sisense did was they were constantly collecting the metadata of how customers were using the platform. Things like what their role was, what they were doing within the platform. What we were able to do this year was introduce two things. The first one it’s similar to some of the stuff Amit was talking about. The first one is a local knowledge graph. And what that means is as your organization starts using Sisense, we collect knowledge to get smarter and smarter. And so since Matt, you and I have the same role, it knows that since you liked this recommendation for something to look at next, it will also give that to me because we have similar roles.
What the universal knowledge graph does, the second piece is it takes 650 billion data points collected over the past 10 years, again, metadata points. And it’s able to provide knowledge out of the box. So the way I like to describe it is to think of the Netflix experience. The day I sign up, it knows that I’m female, if I gave it that information that I live in California and maybe my age. So it starts to give me some recommendations out of the box. That’s the way of thinking, the universal Knowledge Graph. And then as I start seeing movies and doing things, it gets smarter and smarter, and now it’s recommending even more refined. So it’s the same concept. And we have both out of the box as of earlier this year within the platform.
[MT] And local is within the enterprise and global is across the customer base?
[AK] All customers. Yeah. All customers. And we run it as a cloud service, so it’s highly scalable. So we’ll continue to collect, it’s 650 billion points today – we will continue to collect and make that available to customers.
[MT] Right. So you have a great data network effect throughout the customer base. Okay.
[MT] Very interesting. But presumably, that’s only the learning that’s shared.
[AK] Absolutely. 100%.
[MT] Okay. Very good. All right. So there is an ingestion layer, there is an engine that does a lot of the cranking and gets smarter over time, as we discussed. What’s on the presentation layer of it? We’ve got dashboards. How does it manifest?
[AK] So we have a pretty strong viewpoint on this, 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 a large portion of our business is embedded within products. So embedded within 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, as our current and future part of our roadmap is going beyond the dashboard. But the dashboard of course is step one and we do provide that.
[MT] I read somewhere that actually the embedded part, which is in some ways a white label of sort. It’s sort of disappearing in the background, but providing the intelligence. I read somewhere that it accounted for 50% of the revenue, which is a very substantial part of the site. Is that correct?
[AK] Yeah. So from the OEM white labeled side, so being embedded in other products, it’s about half of the business. But even in the internal cases, we have one use case is a large financial organization that found that their intraday traders weren’t actually using data to make the right decisions. And they weren’t able to identify things like fraud happening. So what they did was they embedded Sisense within the internal portal. And so now every trader sits down every day and they’re in Sisense. And of course, you can build things around. They have all of the systems they need within that experience. And so 50% is what you would consider OEM, but we also see a lot of these use cases within the internal analytics business as well.
[MT] Where do you think we are in that journey of precisely making BI more available to a broad group of users? The classic sort of Tableau story, not against Tableau specifically, but it’s very much like when you talk to people, you hear the same story again and again, which is like, “Okay, yes, we do have BI, but ultimately we have three analysts that know how to use the product.” Then basically you get access to the intelligence based on where you are in the pecking order of the company. So if you’re the CEO, sure you have access all the time. But if you’re a product manager, then you just have to wait in line. Is that still what you are seeing?
[AK] 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 analyst jobs posted. And that’s because whomever is saying that is right. 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. Our viewpoint is we want to actually send the insights and the intelligence in an understandable way. 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 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 to get beyond that.
[MT] To close on the product part, you mentioned AI, this concept of ‘learning’. I read somewhere, or maybe you mentioned it as well, there’s the concept of using increasingly AI for data prep, for helping the cleaning of the data. Is that part of the product?
[AK] Yeah. We have things like being able to de-dupe data and identify issues within data, to get the data ready in the right format to be analyzed. We can also change things at the metadata layer within the product. So all of that is included as part of our standard platform. And I think it’s very important because even when you have the data, you’ve used the Fivetran, 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, we call it our elastic data engine, our semantic layer, can really help blend other data, and via our AI, identify some issues and data quality places to improve and enhance.
[MT] As a product leader, how do you think about the various personas that you need to address? Because you guys have this concept of builders, which is really interesting, but there’s BI folks, there’s product folks, there’s engineer folks. How do you serve all those different personas?
[AK] 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? I would say there’s three different main ones. The person that’s going to find more of our Periscope-like code-driven technology, is the data teams. The people that typically have some sort of data title, data engineer, or 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 the head of analytics or the manager of analytics. Sisense – 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. That one person, because they downloaded something.
It’s more of an organization has an issue and they want to bring us in to get everybody together, to collaborate and solve. So that’s more of the manager of BI type level. And what they’re looking for is, “I need single sources of truth on the data side, which Sisense provides. And I need an easy end user experience, so that my team can be quickly successful.” Then on the final side, which you mentioned the builder, that is the 50% of our business, our customers that are tasked with, we can really get more value out of our product or even monetize on data if we were to put analytics into our product.
UiPath is a public one that we do that with. So when you’re using UiPath’s RPA, all of the monitoring and all of the analytics within is Sisense. And so from that perspective, you probably know as well as I do, those developers, they don’t want to talk to salespeople. They don’t want to talk to or read marketing lingo. So what we did for them last year is we introduced a really great developer experience in a portal where they can get hands-on, start tapping into our APIs and instantly seeing results. So it’s a little bit complicated, but those are the three different types of personas that we see as our initial land within new customer accounts.
[MT] Great. Any quick overview of the roadmap, maybe? You guys raised a hundred million at the beginning of the year, and making the company a unicorn as well, which is always a nice milestone. What’s on the docket for the next 12 to 18 months.
[AK] So we will always continue as data volumes grow to continue optimizing the way that we work with data. I have been privy to many analytics companies that have failed because nobody wants to drag and drop and wait 30 seconds to get their results. So we’ll always be doing things under the hood. We have a large team focused on optimizing how we work with data, particularly as that data moves. On the insight side, AI will be a big part of our story. How do we do better insight generation, global anomaly detection, and something that I even like to call decision automation. So if I’m a customer success rep, it’s great that Sisense told me my customer might churn. It would be even better if it told me three things to do based on past context in this customer profile that might help keep them on board.
So that’s where we’ll go in that area. Then from a platform perspective, we continue to have the great cloud agnostic and cloud native architecture. So as we continue to scale, we’ll continue innovating on how to continue to optimize, save our customers cost and make sure that they’re getting the best analytics experience that they can. So we have teams focused on all of those.
Then the final thing is Periscope is now integrated with Sisense from the data pipeline perspective. Coming soon, we will also integrate the rest of the notebook-like experience. Being able to put Python and R within the experience and instantly get your insights. So exciting stuff ahead. It’ll be a fun year.
So many other questions, but I’m trying to be on the latter part of at least my part. Let’s see, I’d love maybe to just talk quickly about your organization. I think it’s always interesting for people to understand how those groups run. So first of all, you have a very interesting title that I personally hadn’t seen before, but I’m sure it exists, where you cover both CPO and CMO. What do those organizations look like today at a company that’s scaling like Sisense?
Yeah, sure. I think from our perspective, you generally see product and engineering as great partners, and marketing and sales as great partners. That always leaves a gap between what’s happening in product and how we are messaging it. What we’re doing at Sisense is bringing marketing and products together. So I run the product team , they have their own investment areas. Some of that we’ve 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 events and press.
Then we have 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 lock step is really critical, particularly at this stage company. By bringing that all together, we’re seeing massive, massive movement as far as being able to really take all of our marketing and all of our product to the next level.
Yeah. It makes perfect sense. It’s especially interesting that this doesn’t happen more often, or maybe, I don’t know.
Maybe we’re on the forefront of it, maybe. I will say, if you don’t surround yourself with great leaders, you’re in trouble trying to take on two C-level roles. But I am fortunate to have that as part of my team.
Great. Speaking of which, one aspect that’s noticeable – the team at Sisense, that this great diversity. The core executive operating team is 50% women, including a Chief Customer Officer, Chief People Officer, and you Chief Product and Marketing Officer. That’s the way it should be. Any thoughts and tips on how we can do a better job as an industry? From my perspective, it feels like things are starting to move a little bit. I don’t know if that’s what you see, but how do we do a better job?
Yeah. I have been fortunate to have mentors – bosses and people in my life that are both men and women. I think we’ll start seeing more of this, I’m hopeful that we will. It definitely gives a better balance to the team. Everybody thinks differently. It’s definitely a good sign when I come into an organization, I look around and think, “Oh, okay, there’s just as many women at the leadership level as not.” I do hope I see a trend, but it also starts early too. The reason I’m a chief product officer is I was a computer science major at a school that had, I think, 400 people graduate in my year and three of us were women. We’re all still friends. And so it’s also important for the whole STEM side of the house and girls who code and everything earlier, because we do tend to see more of the marketing and the HR functions still have the greater amount of women leaders versus some of the more technical. And I do think we’re doing the right things earlier in girls’ lives to encourage that.
Great. All right. Thank you so much. I do have more questions, but let’s switch to Q&A, Jack.
First question from Juan. How does Sisense engage with companies to roll out industry specific solutions? Which industries do you currently work with the most?
Yeah, that’s a great question. So, one thing I didn’t mention, and I will now, is that we are known for our customer success. The sale does not end with the final close date you’ve paid for the product. We have a very large customer success organization by design, which is why we have such an NPS. Our NPS is over 60.
The industries that we see a lot of are software and tech. We see a lot of supply chain, healthcare, financial professional services. And because our customer success team starts on day one of your purchase with understanding what you need, helping you implement. They’ve seen a lot of these different types of use cases. So they’re able to say, “Oh, okay, you’re a financial customer and you have this need.” They’re able to come to you with knowledge and in even certain instances, pre-canned solutions to get you started. So it’s a very, very good model that’s been very successful for us, to help make sure our customers are successful, not just on the purchase day, but the year and beyond as they expand.
Where do you see the explosion of data assets, so BI, tables, notebooks, leading organizations to? Put differently, is Sisense looking at focusing on pushing beyond the data management and cataloguing space?
We are sitting alongside, in a lot of cases, the data management and cataloguing space. What we do is then take what we’re able to extract from those different ecosystems, and bring actionable outcomes to customers. We see a lot of the notebooks and even the ad hoc analytics as a great first step. But if you don’t have other people in the organization, collaborating, also seeing those insights, making the same insights and putting their brains together to drive change, you’re not actually going to see better outcomes. And so we see going beyond that to really drive this culture of analytics and allow organizations to drive change. And that’s hard to do because it’s not just the technology.
It’s a lot of times people don’t want to change what they’re doing. They don’t want to try new things. They don’t have time. Everybody’s busy. And so a lot of times it takes a cultural shift as well. The most successful products I’ve seen are the ones that are fun to use. And so Sisense never wants to be a burden in people’s lives. We want it to be there. We want it to be fun and easy for them to use. And we want it to fundamentally change their business, and in a lot of cases, how they personally feel about their jobs and their role within their company.
With so many analytics tools on the market, and I know you’ve discussed a bunch of these, so maybe just distilling the most important in your view, what’s Sisense’s key selling proposition? Is there a secret sauce that you typically focus on?
There is. And I’ll go through what I’ve seen as the wave of analytics. I believe we are on the cusp of wave three of analytics. Wave one was all about IT-led reporting. You go to IT, you ask for a report, they send it. It’s often outdated by the time you get it. This was a long time ago. Wave two was all about self-service. Let’s give everybody a tool where they can drag and drop and build a dashboard and make an insight. And again, that fails at the adoption point. So I believe Sisense, our core differentiator and where we will really thrive in the market is wave three, which is “I need a customizable experience”. So it might not even be a dashboard, but it’s an analytic experience. So it’s customization. I need to have flexibility, because I might have this on-prem data in some business apps today, but it’s not going to look like that in two years.
And it needs to be extensible. We’re seeing a lot of companies ready to start trying different services and things like auto-ML, which we don’t do naturally at Sisense, but we want you to be able to plug that into the Sisense platform. So I’d say customizable, flexible and extensible is where we’ll thrive within wave three of analytics.
With your clients, what are the maybe two or three metrics that you use to show that Sisense is bringing more business or is achieving the goal that they set out to achieve?
So if we’re talking about internal analytics, we like to measure the ROI with them. So we had a customer that was able to work with us and within three months, because of how they were able to optimize their inventory – they do carts within airports – so this was obviously before COVID. They were seeing a 400% return on investment. So a lot of it for internal analytics is about the outcomes and the return on investment. When it comes to our OEM business, a lot of times customers just need it because their competitors have it and they feel behind. And we are the easiest best way to get analytics into the product. But the other side of the house is they want to monetize data. So that’s a bottom line exercise we do with them. If you put analytics into your product and charge a little bit more, you now have a $10 million business in two years. And so there’s actually a monetary value we can put on OEM customers a lot of times.
Thank you. Really interesting dive into the world of BI and the product and what you have on the docket for the next few months. Sisense is just having this amazing journey. Again, like we had a conversation in 2014 and 2016. And now they’re bringing people like you to help it to scale to all what it can be. It’s really exciting to have been witnessing the progress over the years.
I am fortunate to be a part of it. And I’m looking forward to speaking again in hopefully less than two years.
Wonderful. Thank you so much, Ashley. And that’s a wrap for today. Thanks everyone for joining and we’ll have the videos online in the next week or so. Thanks everyone.