ThoughtSpot has previously described itself as the ‘Google for numbers’, whereby it aims to make facts as ‘easily accessible as opinions’ via search. The company has been undergoing a transformation in recent years, as it shifts its users to a cloud-based model in pursuit of ‘killing off the dashboard’.
With the core cloud platform in place, CEO Sudheesh Nair is now filling out ThoughtSpot’s proposition by building out an ecosystem, via integrations, that allows customers to competitively differentiate on their data/infrastructure stack. He is also shifting the company’s pricing model towards true usage-based pricing, drastically reducing the cost of getting started with ThoughtSpot.
On the platform upgrades, which ThoughtSpot is thinking about in the categories of ‘connect, build, launch’, Nair told diginomica the significance of expanding the company’s integration reach. He said:
ThoughtSpot has always been focused on delivering things that people have not experienced before in the world of BI - search, AI, cloud first scale. But one of the main themes that we’re seeing in the customer’s mind is that there is a shift with respect to how companies want to work, by putting together the best of breed stacks with respect to the architecture, as opposed to going for this one stop shop.
It used to be you sign a contract and give the customer the best discount and they buy the entire menu. What’s happening now is that the data, or the infrastructure, is a competitive differentiator. So if we’re all shopping from the same place, all buying the same stack, then I need to find differentiation with the people, or the service that I build myself. And that is a high level of risk.
So what they’re doing is they're spreading the risk by saying let’s differentiate across the entire company - product needs to be differentiated, service needs to be differentiated, but so does the infrastructure - including data. In that context what they want is a suite of companies and products that are committed to work as an ecosystem, where there are opportunities to go build a superior architecture, by picking the best of breed, but with those companies committed to working together. This is the shift we’re seeing in the market.
ThoughtSpot’s new platform integrations include:
The ThoughtSpot platform now connects to a variety of data sources in the cloud, which include:
Amazon Redshift Serverless - customers can now leverage ThoughtSpot’s Modern Analytics Cloud to run and scale analytics on Amazon Web Services without having to provision and manage data warehouse clusters.
Snowflake Data Marketplace, including the new Snowflake Data Explorer app - ThoughtSpot customers can now create insights from third party data in the Snowflake Data Marketplace. This makes it possible to rapidly prototype insights with new data sources for leading indicators.
Databricks Partner Connect - giving Databricks customers the ability to launch a ThoughtSpot free trial from the Databricks console.
Support for new architectures with connectors for both Dremio and Starburst Galaxy, giving customers the ability to leverage Live Analytics from different architectures, including data mesh and the data lake.
ThoughtSpot customers can now benefit from new app building capabilities and integrations, including:
CodeSpot - a searchable repository of open-source ThoughtSpot blocks and code samples for developers to accelerate embedding analytics and app development with ThoughtSpot Everywhere. CodeSpot includes reusable, best practice examples of the most common development tasks, such as custom actions, visualization, API and tooling, formulas, and more.
ELT Live Analytics templates are readily available custom ELT jobs built to work with SpotApps and Matillion.
New third party data blocks leverage TML (ThoughtSpot Modeling Language) giving customers use of external data to enrich their own proprietary data for more meaningful insights. Data providers include Ibotta, Safegraph, Windfall, and FactSet.
With the aim of helping data professionals and analytics engineers get use cases from development to production more quickly, ThoughtSpot is also announcing the following features:
Integration with dbt - this lets analytics engineers translate dbt models to TML (ThoughtSpot Modeling Language). With dbt, data teams can collaborate on modeling data in the cloud data platform, then make those data models immediately consumable with ThoughtSpot.
New SpotApps - including different templates for transaction systems like ServiceNow, Snowflake, HubSpot, Okta, Google Analytics, Google Ads, Jira, RedShift, and Databricks.
Commenting on the range of integrations, Nair said:
We are giving customers a different proposition compared to the buffet that the other large cloud vendors are offering, which is: you can go and build something cutting edge, but underneath it we are also giving you the ecosystem advantage. There will be a lot of APIs, a lot of SDKs, and a lot of joint products.
And Nair was particularly keen to highlight the dbt integration, as he sees this as critical to how ThoughtSpot is developing its ‘kill off BI dashboards’ mantra. He added:
This whole idea of headless BI is something that we have been advocating from the beginning. This machine to machine conversation is as important as what machines do to humans, with respect to delivering insights. So one of the things that we have delivered is one of the first integrations with dbt.
dbt is trying to build the central unified metric layer, so that the business speak and the data speak can be bridged in a single sheet. Our ability to simply point to a dbt project and then start searching, will significantly shrink that gap as well - not just for people, but also for machines. So when your people cannot differentiate how good the product or service is, the product and the machine should do that. That’s a new way of thinking. The old BI cannot get to this, because their endpoint is a dashboard report.
This week ThoughtSpot is also launching three new editions: Team Edition, Pro Edition and a special bundle for startups, nonprofits and education institutions. As part of these editions, ThoughtSpot also announced a consumption-based pricing model, where customers pay only for what they use.
ThoughtSpot customers can get started with Team Edition for a flat fee of $95 per month for a single user-group with unlimited users. Pro Edition starts at $2500 per month for up to 5 user-groups with unlimited users. Pro Edition customers will only pay for what they consume, based on actual queries. Startups, nonprofits, and education institutions with less than 100 people and less than $10M in annual revenue will get access to a flat fee offer for Pro Edition, with per query charges waived.
ThoughtSpot’s pricing changes form part of its transition to a true cloud-based model, as well as improve ease of access for smaller teams and broaden ThoughtSpot’s reach. The average price of a ThoughtSpot deal used to be $200,000 - now users in small teams can get access for as little as $95, Nair said. He added:
We are relaunching the entire pricing strategy with two key principles behind it. With the first one being, we have to make sure that we kill shelfware. Enterprise software is an appreciating asset when it is leased. When it is bought it is a depreciating asset.
If you want something more, they will charge you more. Whereas we will keep adding software and capabilities that will become automatically available.
I really believe it is important to take this archaic idea, of this user based standard licences, that are not tethered to a pro-tem variant, that is extremely exploitative, and get rid of it. In the world of cloud, when customers are only renting, they should have complete clarity - they should not be paying for things unless they use it. And there will be peace of mind if people really use the heck out of it. That’s what we are actually doing with these three models.
This feels like a much more mature and sophisticated approach to taking on enterprise BI, from ThoughtSpot. There’s still that spice from Nair of wanting to change things up, but it now comes with an understanding of the realities of what enterprise buyers need. Nair isn’t wrong about going after an ecosystem approach. Its integration with dbt to take advantage of live machine to machine data, and make that accessible, is particularly interesting. I’ve got some customer interviews lined up in the coming weeks, so keep an eye out on diginomica for how all of this is being applied.