ThoughtSpot BI platform an early adopter of AI

The ThoughtSpot BI platform and AI have been closely intertwined since the company was founded in 2012.

Many analytics software vendors are now investing heavily in augmented intelligence and machine learning. SAS, for example, said early in 2019 that it was earmarking $1 billion for AI, and recent product updates from Qlik, Tableau and Tibco have all been heavy on new AI features. But what are now being called third-generation analytics features were part of the ThoughtSpot BI platform from its inception.

That's part of what got ThoughtSpot, based in Sunnyvale, Calif., noticed right away when its first product was released in late 2015, and why the vendor has been growing quickly since then.

ThoughtSpot is hosting its Beyond 2019 user conference this week in Dallas, and on Tuesday unveiled ThoughtSpot 6, the latest iteration of the ThoughtSpot BI platform. Naturally, its new features are infused with AI and machine learning, including Answer Explorer and ThoughtSpot Monitor. Thoughtspot also introduced a new mobile app.

Before Beyond 2019, Andrew Yeung, ThoughtSpot's senior director of product marketing, talked in a Q&A about ThoughtSpot's early recognition of AI and machine learning as the future of business intelligence, and how the startup aims to remain innovative regarding the relationship between AI and the ThoughtSpot BI platform.

Right from the start, the ThoughtSpot BI platform was focused on featuring augmented intelligence and machine learning -- this at a time when data visualization was super popular. How was ThoughtSpot able to see the future back in 2012?

With AI and machine learning, that's how we were able to realize the vision of how to transform the way people get access to their data and ultimately get answers to their questions.

Andrew Yeung: I think it really gets down to serving our end users. A lot of other vendors have been focused on data visualizations to make it easier for the data analysts -- creating these dashboards with pretty visualizations for those end users. For us it was all about who we're serving, and those are businesspeople, the people in an enterprise, and for them we needed to provide the smarts in a platform. They needed speed and ease of use just to be able to get to the answers that they needed for their day-to-day work. When our founders came together -- these are folks who came from the likes of Google and Bing and so forth -- they brought with them a lot of experience from the consumer space. With AI and machine learning, that's how we were able to realize the vision of how to transform the way people get access to their data and ultimately get answers to their questions.

With other BI and analytics software vendors now seeing the light and also investing heavily in AI and machine learning capabilities, how does ThoughtSpot plan to keep the ThoughtSpot BI platform ahead in terms of the pace of innovation?

Yeung: There's a lot of technology required to actually make a search work, and to make a search work on enterprise data at scale. That's where we believe there are a lot of things that the product and the platform we've built out continue to lead. From a perspective of speed and smarts, the architecture is really built purely for the purpose of search and AI. At the same time, our customers are also driving us toward transforming the way that they're accessing and getting insights with their data. That could be pointing us toward doing more things around smarter visualizations -- more data storytelling and stuff like that. Our customers are pulling us in those directions, but at the same time we're leveraging all the underlying innovations that we've built out.

At a basic level, what do AI and machine learning allow BI platforms to do that they couldn't previously?

Andrew Yeung

Yeung: I typically like to think of this as a mental 2x2. On the Y axis you have a pull mechanism to pull answers when you're analyzing data and also a push mechanism to push insights out toward your end users. On the X axis is a combination of known questions and unknown questions. With AI -- through searches and machine learning -- these capabilities start to drive toward the top end of this 2x2. It starts to take you toward an experience where you get continuous AI-driven insights, and I would go as far as saying we're starting to get toward an era of autonomous self-driving insights where the system is now telling you what's most important to you -- things that you should be aware of, what has changed -- and really keeps track of informing you and pushing you toward the most relevant, most personalized answers and insights.

Natural language processing and the machines learning a user's patterns to become more predictive seem to be where AI is today, but where do you think it will be a in another couple of years -- what's next?

Yeung: Similar to how I was talking about how we're going into an era of going beyond where people are purely pulling the system for insights based on the questions that they already had in mind, we're going toward an era where the system makes suggestions to you. The system is going to be smart enough to tell you that if you're looking at this particular chart based on this data, maybe you should ask this question, or maybe you should break this down further across these types of dimensions. This is because the system is learning who you are as an end user based on your role and your profile and usage in order to make suggestions to you and recommend what else might be interesting to you. Think of when you're buying something on Amazon and you search for a particular item, and then Amazon tells you that if you're looking at this item these are what other customers like you have also bought. That's where the industry is going, that's where analytics is going to be heading. It's going toward ways that the system will proactively tell you what insight you need to be aware of.

About six weeks ago ThoughtSpot raised $248 million in venture funding. What does that money allow ThoughtSpot to do that may not have otherwise been possible?

Yeung: That one is maybe a little above my pay grade, but I can give you my perspective. For us, ultimately what this allows us to do is further invest in innovation, further invest in our product, further invest in how our product can more adequately support our customers' use cases and also expand their use cases as well. It also allows us to further grow globally -- we are already a global company but we're establishing more offices around the world and we're landing more customers around the world as well -- and so this infusion from this funding round is going to help us across all these different areas.