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A Conversation with Elastic’s CEO Ash Kulkarni About the Company’s 2023 Sales Kick Off

In this episode of the Futurum Tech Webcast Interview Series, I had the opportunity to speak with Ash Kulkarni, the CEO of Elastic, about the company’s upcoming sales kickoff and the challenges faced by its customers. During our conversation, Ash highlighted the positive momentum they have experienced at Elastic over the past year. The company has successfully released new products and formed partnerships with hyper-scaler partners, resulting in significant progress.

Elastic’s customers have expressed their desire to achieve more with less, leveraging insights from both structured and unstructured data in real-time. As enterprises seek a platform that enables efficient data analysis and drives business growth, Elastic have listened closely to their needs. In our discussion Ash emphasized the importance of Elastic’s relevance engine in making sense of data while ensuring privacy and security.

We discussed the following topics:

  • Elastic’s evolution from being primarily a search analytics platform to offering observability and security solutions.
  • The impact of large language models and AI, highlighting the opportunities they present.
  • The immense potential for Elastic to connect private enterprise data with large language models, enabling the development of generative AI applications.
  • The alignment between Elastic existing user base, its popularity, and the company’s developer appeal positions Elastic favorably to seize this opportunity.
  • Elastics’ ongoing innovations, including platform capabilities, AIOps, and security analytics.
  • The establishment of Elastic’s Security Threat Research Labs, that aims to research open attacks and enhance protection for customers.
  • The company’s collaborations with hyper-scaler partners like AWS, GCP, and Azure, as they play a crucial role in building momentum for the company’s offerings.
  • How Elastic is committed to addressing its customers’ challenges, such as the skills gap and the increasing volume of data. Elastic helps enterprises overcome these obstacles by providing real-time insights from all types of data, including both human-generated and machine-generated data, such as logs.

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Transcript:

Steven Dickens: Hello and welcome. My name’s Steven Dickens from The Futurum Group, and I’m joined today by Ash Kulkarni, the CEO of Elastic, the company behind the popular Elastic service platform. Hey Ash, welcome to the show.

Ash Kulkarni: Great to be here.

Steven Dickens: So we are here to have a little chat ahead of your sales kickoff that’s next week. Obviously looking to get some messages out to the team and rally them. What are you hearing and really what’s the sort of call to action for the team?

Ash Kulkarni: Yeah. Look, it’s really about the positive energy, the momentum with which we are moving into the new year. We did an amazing job of releasing new innovations throughout the year, everything from core capabilities and innovations in our platform to the enhancements that we made around AIOps and security analytics. And that really carried us forward and showed up in the positive feedback that we got at conferences and events like RSA, like our ElasticON event series, our user conference that we did throughout the world, at various events like AWS re:Invent. It was wonderful to see new product capabilities.

But also we launched our Security Threat Research Labs, and the mission of that lab is to do research on what are the open attacks that are happening, and what’s happening in the cyber threat landscape, and really coming up with new ways to protect our customers and detect issues that they’re seeing. And we continue to do more with our community. We’ve been working really well with all of our hyper-scaler partners, with AWS, with GCP, with Azure, and I’m just seeing that momentum continuing to build.

So to me it’s about all of these good things. It’s about keeping an eye on our customers, making sure that we keep them front and center of everything that we do, but doing it in such a way that we continue to nurture our employees, continue to make Elastic one of the best places to work. And that reflects in the kinds of awards that we’ve received. So very excited about the new year and very excited about working with the team to deliver even more in the year to come.

Steven Dickens: So lots of change, lots of things going on in the market. I think fundamental dynamics in just how the world has changed throughout 2023 and back at the end of last year, we’re seeing a lot of evolution in the tech stack. We’re seeing new impacts come through onto customers. You are chatting to customers every day in your role and you’re talking to probably more senior levels in those customers than maybe some of the rest of the sales team. What are those challenges that you are hearing day-to-day from those clients? What are they facing and what are they telling you?

Ash Kulkarni: Yeah, I spend a lot of time with our customers. We sell from the smallest of customers to the largest of organizations, the largest of financial service institutions, manufacturing companies, technology organizations, federal and state government agencies throughout the world, just big brand names. The ones that you would typically just know of as a consumer. Brand names like Walmart, brand names like Netflix, Uber, et cetera. And the one constant refrain that I hear from everybody is it’s just getting harder out there. It’s getting harder because there is a skills gap. The amount of data that we generate continues to grow. There are all the macroeconomic pressures that we just talked about that is making it really important for people to find ways to do more with less. But that doesn’t mean business has stopped. It means that they just need to be more efficient. They need to be more effective in how they get value out of their data.

So people often talk about wanting to gain insights, but the bar has gone up quite a bit because now it’s all about getting insights from all of their data, not just structured, but all their unstructured information because that’s really where the nuggets are. Everything from human-generated data to machine-generated data like logs, et cetera, trying to get the right insights in real time so they can drive their business forward. Whether it is ensuring the uptime of their systems through monitoring, resolving issues quickly so they can ensure business continuity, whether it’s cybersecurity and making sure that they understand what’s going on throughout their environment in real time so they can detect, protect, and remediate threats, actively making sure that they can deliver the right information to their customers.

Imagine if you are a eCommerce organization or any company that is in the business of ensuring that when your customers come to your website, come to your applications, that they find what they’re looking for quickly because that is the difference between whether they transact with you or with your competition. The value of insights in real time is priceless. That’s what we are constantly hearing, that they need a platform that can enable this for them. Elastic has always been about allowing you to get insights from all data in real time, and that’s really where we shine when it comes to the messiest of data, the most complex of unstructured information, machine logs, et cetera. And that need that I just talked about is only growing because unstructured data is one of the fastest growing forms of information.

The other thing that’s happening now is in this, with the advent of large language models and technologies like ChatGPT, whether it’s the GPT-4 model, it’s changing the discussion on what new services can be enabled? How can we do it more efficiently and reduce the overall cost that we have to incur? But this is also now forcing IT organizations to rethink what they need in the infrastructure stack to build this next generation of generative AI applications. That’s another really important inflection point that I’m seeing and a massive opportunity for Elastic with everything that we do. We are the underlying platform for helping our customers get that connection between their private data and all of these large language models so they can build this next host of generative AI applications. So this is the role that I believe we are going to be really playing more of in the year to come.

Steven Dickens: You guys have been leading from a democratization of search for over a decade now, so it’s really interesting. I think it’s a perfect time and a perfect inflection point to having this conversation.

As we talk about getting insights from data and doing that in real time, what should the teams be thinking about as they look to focus on that conversation right now as these large language models are starting to impact the market?

Ash Kulkarni: Yeah. I’d describe us as the world’s most amazing relevance… We help our customers make sense of all of their data and get insights that matter to them in real time. That’s what we do. It’s all about the speed, the scale, but it’s about the relevance. That relevance engine is really what was the genesis of Elastic. It was Elasticsearch. The core of Elasticsearch was this relevance engine that Shay built and that really became this ubiquitous technology that people started using for getting insights that really helped them surface the things that mattered and doing it across all sorts of data. And it worked incredibly well for the messiest of unstructured data, and that’s how we democratized access to insights from all data for our customers over 10 years ago.

That was the first phase. And then we went on to look at, by staying close to our community, what are all the things that customers are trying to do with this technology? We realized that they were using it to address the problem of logs, address the problem of machine data, and they needed something along those lines because the incumbent technologies were too expensive. Incumbent technologies like Splunk, they were just way too expensive, they weren’t as scalable and they had challenges. Elastic, our customers have always told us that they love not just our technology but our business practices. And with that, we evolved to deliver our observability solution. We’ve built out functionality like application performance monitoring, metrics, the more recent functionalities that we’ve delivered around synthetics, profiling, et cetera. It’s allowed us to today be at the point where the Gartners and Forresters of the world consider us to be one of the strongest technology platforms out there for observability.

We did something similar in security. We took that same platform that was all about insights, it was all about search analytics, and we helped deliver security analytics through that platform by bringing in all relevant information related to security, network logs, application logs, identity and access management logs. And then doing the machine learning on top of it, the behavioral detections that helped our customers understand very quickly where the threats lie. And then we added functionality that allowed them to do protection, whether it’s ransomware protection or malware protection and protection. That was the genesis of our security product. And today, Forrester considers us to be a leader in that space.

So we’ve gotten from these humble roots to a point where our platform, the search analytics platform that we’ve built with this amazing relevance engine, has delivered a set of capabilities that allows our customers to solve the most impactful observability, security, and search problems across their entire landscape. This is where I feel the next frontier is going to come up, using the power of what we have, this relevance engine, and the way we are morphing it, the way we are evolving it by adding all the vector related capabilities to it now makes this relevance engine that much more sophisticated. We delivered that functionality a year and a half ago and now all of this is coming together with the advent of these large language models where, as you go forward, we will be developing an even easier way for our users to interact with their data through our platform. I think that’s going to be exciting and I know that this is what our customers have been asking from us and I’m really looking forward to that.

Steven Dickens: So I think that perspective around observability and security, that’s probably a great perspective looking backwards and I think there’s still some headway and still strong focus to go forward there and still more share for your teams to capture. But we seem to be at an inflection point where AI seems to have captured the sort of corporate attention. What are you seeing specifically in that space and what’s the opportunity that lies ahead? Searches being disrupted, it’s less around sort of the classic search, it’s more around asking questions and trying to find data and new formats and new ways. What do you think is the opportunity that lies ahead for Elastic with that context?

Ash Kulkarni: It just brings a smile to my face because when I think about everything that we have done in this last decade has all been about that relevance functionality, and in the last two years we’ve been adding tremendous amounts of capabilities. Our machine learning functionality is… We’ve had it in the last almost eight years in our products and it’s been constantly getting more and more sophisticated. Two years ago we added the capability for you to bring your own transformer models onto Elastic. A year and a half ago we added vector search functionality. And all of this made our relevance engine that much more capable and one of the most sophisticated out there.

Because as you think about these large language models, what they let you do is have a more human, intuitive conversational approach to your systems and your data. But all of these large language models are trained on publicly available information. So these large language models know everything that’s on the internet. But all the examples of customers that I gave you, whether it’s large banks or government institutions or even the Ubers of the world, they don’t want their private data to be out there in the open. Government agencies are never going to do that. Banks are never going to have their private transactional information out in the open. That’d be a disaster. So you need to marry those two together, and that’s really where Elastic comes in. Because what our customers are now able to do is they’re able to store all their data on Elastic and then provide the context of that private information as needed, as context windows to these large language models. And through that interaction that we facilitate, we make it possible for our customers to deliver an experience for their end users, for their employees, for their partners, that brings the power of that conversational approach of large language models to their private information and their private context.

We are the glue. We are the glue that gives you the ability to take all your private data, your private information and in the safest, most privacy centric way, connect the dots, connect the context to these large language models to enable all these kinds of use cases. You’re going to see this in every industry, whether it’s in remote process automation, robotic process automation, whether it’s in transportation, whether it’s in workplace search, whether it’s in security and observability, you’re going to see a whole host of these, and Elastic will be the platform that powers these generative AI applications. We will integrate with all the large language models out there. As you know, we do a lot of work with the hyper-scalers already so I expect this just to be a natural extension of all of that work.

So exciting times and a pretty interesting opportunity, but it’s an opportunity that we are going to be very, very focused in the coming year on making sure that we capture.

Steven Dickens: Yeah. Ash, I think you make a really good point. The sort of general public’s attention has been grabbed by these AI models interacting with the internet and pulling sort of publicly available data and putting that at our fingertips. But I think what people are missing in this discussion is the enterprise corpus of data, and I think you make a fantastic point. So specifically, what’s unique there for Elastic as you look to capture that enterprise opportunity? It might be, as you say, a B2C use case, but it’s based off an enterprise corpus of data. What do you see as the opportunity and what’s the sort of unique value that Elastic brings?

Ash Kulkarni: Yeah. The main thing about all the enterprise corpus of data as you called it, is first and foremost that businesses think of that data as their private data. They don’t want to open it up to these large language models because then they’d be creating other kinds of downstream issues for their own businesses. So privacy is a big aspect here.

Second, if you think about just how widely proliferated Elasticsearch is already out in the marketplace, over the years, since the first release of Elasticsearch, and just our incorporation as a company Elastic, we’ve had about four billion downloads of our software. That just gives you a sense of the scale and the popularity of the technology. And a lot of customer information is already sitting within their own environments on top of Elasticsearch. Now imagine us with all of these new innovations that we’ve brought to bear, the new vector search capabilities that we’ve delivered. Now, bringing all of that and allowing all of these users that are already using Elasticsearch to now build the next generation of generative AI applications. In the past, they were using the same technology to build search applications. Now we’ve enabled them to build generative AI applications without doing anything other than just upgrading to our latest version.

That is the power. That is what really makes this so exciting and interesting that we are able to connect the dots between their private data and the power of these large language models and are… The fact that we are already out there, the fact that we have tremendous developer appeal, the fact that we are the fastest platform out there when it comes to really being able to, with relevance, search across all of your information is what now gives us the unique opportunity to play in this space from a position of strength.

Steven Dickens: Fantastic. Ash, I don’t think I could find a better way to summarize our time here this morning. Great talk. Wish you and the team every success in the SKO. It’s been very informative and I appreciate you telling us the ways that Elastic’s delivering for your customers and in the market. Thank you very much for your time and hopefully-

Ash Kulkarni: Thank you very much, Steven. Appreciate it.

Author Information

Regarded as a luminary at the intersection of technology and business transformation, Steven Dickens is the Vice President and Practice Leader for Hybrid Cloud, Infrastructure, and Operations at The Futurum Group. With a distinguished track record as a Forbes contributor and a ranking among the Top 10 Analysts by ARInsights, Steven's unique vantage point enables him to chart the nexus between emergent technologies and disruptive innovation, offering unparalleled insights for global enterprises.

Steven's expertise spans a broad spectrum of technologies that drive modern enterprises. Notable among these are open source, hybrid cloud, mission-critical infrastructure, cryptocurrencies, blockchain, and FinTech innovation. His work is foundational in aligning the strategic imperatives of C-suite executives with the practical needs of end users and technology practitioners, serving as a catalyst for optimizing the return on technology investments.

Over the years, Steven has been an integral part of industry behemoths including Broadcom, Hewlett Packard Enterprise (HPE), and IBM. His exceptional ability to pioneer multi-hundred-million-dollar products and to lead global sales teams with revenues in the same echelon has consistently demonstrated his capability for high-impact leadership.

Steven serves as a thought leader in various technology consortiums. He was a founding board member and former Chairperson of the Open Mainframe Project, under the aegis of the Linux Foundation. His role as a Board Advisor continues to shape the advocacy for open source implementations of mainframe technologies.

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