The past year has been active for analytics vendor ThoughtSpot.
In the fall of 2020, ThoughtSpot unveiled plans to make a major technology change, transitioning its platform to become cloud-first after previously gearing it toward on-premises users.
Toward that end, the vendor, founded in 2012 and based in Sunnyvale, Calif., released ThoughtSpot Cloud, a SaaS version of its platform, in September 2020.
Subsequent new capabilities, including the introduction of ThoughtSpot One, with which users can easily search and share data assets, and most recently the release of prebuilt applications for SaaS offerings such as Salesforce and Snowflake called SpotApps, have built out ThoughtSpot's cloud-first analytics capabilities.
But the year between Beyond 2020 and the Beyond 2021 virtual user conference, which the vendor hosted on Nov. 16, also included the release of an entire ecosystem for developers, the first two acquisitions in ThoughtSpot's history, continued positioning for a potential initial public stock offering, and the vendor's Series F funding round.
The day before Beyond 2021 -- and the day ThoughtSpot unveiled SpotApps and revealed its most recent funding round -- Sudheesh Nair, ThoughtSpot's CEO, discussed all the vendor has done over the past year, and what lies ahead.
He touched on ThoughtSpot's prioritization of the cloud, the new capabilities it's added to improve the analytics capabilities of its platform and what's on the product roadmap for the near future, and even whether more acquisitions might be forthcoming.
A year ago, ThoughtSpot really accelerated the process of becoming a cloud-first analytics vendor -- where are you now on that journey?
Sudheesh Nair: ThoughtSpot's transformation to becoming a cloud company is nearly complete. What is never complete is our ability to improve search. Even Google is still improving search. It happens underneath the search bar, so you don't realize it, but the searches are getting better and faster, and we will be on that never-ending journey of improving search.
What has ThoughtSpot done of late to improve search?
Nair: Our primary search was built for creating content, understanding insights and understanding logic, but what ThoughtSpot One did was flip that by enabling users to discover content and modify it as opposed to create all the time. That is still a young product and still has a long way to go, but it's an exciting journey. The reason why TikTok is so popular is because it lowered the barrier for creation -- everyone can easily create. Democratizing creation is one of the most important things a company can do to bring virility to the product. Our mission has always been to make the world more fact-driven, and that means lowering the barrier.
Sudheesh NairCEO, ThoughtSpot
[Coding language] SQL is a very high barrier for people to learn, so ThoughtSpot One enables people to discover what others have created, modify it to make it their own, and share. And then, people can also follow other influencers [within their organization] to see how they make sense of something and then learn and improve. BI wasn't built like that before. It was built by experts for experts.
What other additions did you make to the ThoughtSpot analytics platform designed to improve search?
Nair: The other big pieces are ThoughtSpot Blocks and ThoughtSpot Everywhere. They are low-code, embedded analytics tools that enable anyone trying to build an application to not have to go back and start learning the basics of BI and analytics. BI is a complex thing to learn, so if you are a supply-chain software maker, we want you to focus on your domain and then analytics to be infused into every one of your applications with a low-code tool. Those two things will continue to improve because 10 years from now, we will look back at this notion of discover-and-modify and it will be something others will be copying.
Two of the big developments over the past year were your acquisitions of SeekWell and Diyotta. Why, after nine years, was 2021 the right time for ThoughtSpot to make its first acquisitions?
Nair: If you want to feast in the spring, you have to seed in the winter. When COVID hit, we knew it was the time to double down on innovation. Customers don't come to ThoughtSpot because of the brand, or because of our size. There are always bigger companies and better brands out there. They come to us because we are honest people and because we're an innovative company. So we absolutely doubled down on innovation. One of the key things I always try to remind companies I'm participating in is that we should never assume that entrepreneurship and a founder's mentality are things that only we possess, so we're on a hunt for as many founders as possible to come to ThoughtSpot and make this their home. The primary reason a company like ThoughtSpot makes an acquisition is for entrepreneurial DNA.
What analytics capabilities did SeekWell and Diyotta add to ThoughtSpot?
Nair: The acquisitions also filled a missing piece for ThoughtSpot. We were talking about data-to-insight-to-knowledge-to-action, and the action piece was missing, so SeekWell really fit nicely into that knowledge-to-action part. Diyotta brought a good-sized team to us with data connectivity in the cloud experience that we didn't have. That allowed us to connect to Snowflake and others really fast without losing performance. They allowed us to do live query, and today, live query and live analytics are what ThoughtSpot is about. Looking forward, now we can consider acquisitions that are not just people acquisitions but also technology, and as the company gets bigger, there will be opportunities to do more interesting things.
Do you anticipate acquiring more vendors in the near future, or is it better to step back for now and work to integrate SeekWell and Diyotta into ThoughtSpot?
Nair: The honest answer there is that companies die from indigestion and not starvation, so we want to make sure that we acquire and thrive. But having said that, as a tech-sided CEO of a company in a market that is changing fast, it's impossible for me not to get excited about some of the people and technologies out there. Whatever answer I might give you might change in the future because we're extremely opportunistic when it comes to acquisitions. In general, I know what the direction of the company should be, but I haven't figured out all missing pieces and steps. If we find the right piece, we'll absolutely go for it.
Speaking of next steps, what analytics capabilities are on the roadmap for ThoughtSpot?
Nair: From a roadmap point of view, as data is moving to the cloud, now is the time analytics really needs to grow up, so our unique opportunity is to define how analytics is done in the modern data stack. What's also interesting is that this is not a market where a company can just come in and take over -- it takes time to build visualizations, a BI layer. We happen to be lucky because nine years later, we are mature enough and have hit our stride by making the company cloud-first.
There are three missing pieces that we are going to address. One is mining the insight through AI, which is a huge focus for us. Second is to deliver the insight wherever you are -- Slack or Teams -- just when you need it. And third, drive it to actions through other platforms through developers and APIs. We're building products in each of these three areas.
Finally, looking beyond ThoughtSpot, what are some trends you're seeing that are shaping the analytics market?
Nair: I would say there are two things. One is analytics and machine learning coming together. Today, we think of data and analytics as separate from machine learning, and I think that all analytics will be driven through machine learning. It's going to be a race to consolidate those two layers and deliver outcomes without thinking about which one is analytics and which one is machine learning. That's something every company, including ThoughtSpot, will have to invest in to create better quality stuff.
The second thing is the idea of headless BI, where the BI itself is done from machine to machine. The amount of data that machines are generating right now far exceeds the amount of data that humans can consume, which means that machines should actually consume, curate and deliver actionable insights for humans. The way we currently understand BI, where humans take data to be consumed by other humans, is so archaic. It will require a massive amount of distributed computing, a massive amount of machine learning, and knowledge in terms of an API-driven world. It's an exciting area.
Editor's note: This Q&A has been edited for clarity and conciseness.