ThoughtSpot CEO - ‘I want to kill BI and I want all dashboards to die’

Just before the Christmas break I had what turned out to be a very interesting conversation with ThoughtSpot CEO, Sudheesh Nair. Interesting because it highlighted some of the new challenges facing companies as they enter 2021, in terms of how they think about using data to better serve their customers. 

The key theme being: as the world has changed around us due to COVID-19, the use of historical, aggregate data is becoming less relevant given that patterns of the past may no longer ring true. Companies, in turn, need to get better at understanding the individual needs of their customers in order to navigate the continued uncertainty. 

Of course, Nair believes ThoughtSpot is the platform to enable this. Whether or not that is true on a large scale, remains to be seen. But the discussion around the use of traditional BI and dashboards, particularly within the context of the changing nature of work as a result of COVID-19, is particularly relevant. 

By way of background, ThoughtSpot likes to think of itself as the ‘Google for numbers'. In other words, it hopes to deliver users key facts, much the way Google delivers you information online via search. There are, however, some key differences between the two approaches. Nair explained:

The problem Google created is that when you make written text as easily as accessible as it is, people want the same thing for facts. And when I say facts versus opinions, I'm broadly differentiating between numbers and texts. The problem with numbers is that more often than not is that numbers need calculations. And you cannot have thousands and thousands of results, like Google shows you for text. So the opportunity we saw was to make facts as easily accessible as opinions have been in the world. 

ThoughtSpot does this by allowing users to search enterprise data with hyper-personalised questions using natural language processing. It aims to not only give a result for the question you ask, but then also uses AI to offer up alternative questions and results that may be helpful. This is very different to traditional BI, which typically offers you a template for which to present historical, aggregate data. Nair said: 

This used to be something that was only available if you knew SQL, if you knew Python, if you knew data. You could have 1000 questions if you are trying to appeal to the next coffee drinker in Starbucks, for example. We want you to have the power to do it. This is what Thoughtspot does very differently. One is that we deliver the insights you are looking for, using natural language processing and search. The second thing that we do, using AI in the backend, is that the platform will answer questions you did not ask. For example, if you do X, did you know you will get 25% more hits from New York City? You may not have asked for it, but you may want to know it. 

When dealing with such thorny technology issues such as search and AI, it's unsurprising that ThoughtSpot too has had to undergo some rethinks and changes over the years. For instance, Nair explained that in the early days customers did not have the database performance to effectively deliver the experience ThoughtSpot was aiming for. Waiting three or four minutes for an answer to a question is hardly ‘Google for the enterprise'. 

With this in mind, ThoughtSpot initially inserted a high performance caching layer in between the database and the application, in order to deliver on the desired usability. However, this too came with challenges - mostly that it was costly, required maintenance and meant new hardware for customers. 

Since then, however, ThoughtSpot is shifting its model to the use of cloud data warehouses - such as Snowflake and AWS Redshift - to deliver the performance needed, elastically. Nair explained: 

ThoughSpot Cloud now sits on top of any cloud data warehouse and natively connects and delivers. So it removes a huge barrier. Now what customers need is to create a lightweight model called Worksheet. We now use a metadata layer called Worksheet, but we actually now provide machine learning tools to automate that too. To give you an idea, our deployments used to take five and a half months. In our new model we are now anywhere from 24 to 34 minutes. 

This unconstrained exploration into the world of data was impossible for businesses, prior to ThoughtSpot. 

The death of dashboards

Back to the original point - COVID-19 has cemented that enterprises can't rely on a historical view of their data in order to effectively serve their customers. Nair argues that BI tools effectively decide what you want to see, which is counter to the idea of hyper-personalisation (much like what we experience in the consumer world). 

ThoughtSpot is approaching this from a use case point of view. For example, Nair said that customer churn is an area that he believes the company can seriously ‘move the needle' for its customers. He gave the example of a large bank, which is unlikely to win lots of new customers in a saturated market, and as such, pleasing and keeping its existing customers is key. 

In this use case, Nair said, take a bank that has a customer that has a car loan, but is also now looking for a new home loan. But that same customer is annoyed with the bank, because they got charged interest for the car loan for making one payment a day late. This experience may put them off getting a home loan with the same bank and if the bank is just using aggregate, historical data on all customers with car loans, then they will not know the details of this unique customer. 

The problem is that just throwing more stuff at customers is creating more noise, not signal. So you need to distil the personalised data that you have. If the bank could go back to that customer and say ‘we messed up, we're sorry, here's the interest back, and by the way would you like a home loan?' - that's the bespoke experience and where data matters. 

Dashboards showing me how many people have missed a payment is not useful here either. I want to focus on that one user. Tell me everything about that one user. 

If you're banking, you'd have specific requirements and the bank should know those, if they want to earn your business and keep it. And that may not be based on the model of the average person your age. So how do you deliver personalised action for every business user? And underpinning all of that is data. 

Nair said that focusing on these specific use cases will be key to ThoughtSpot's future success, as it's akin to going fishing for business with a spear, rather than a trawler net. ThoughtSpot, as such, has adopted a use case driven sales approach, where it targets companies where it feels it can make a tangible difference and "move the needle". 

But the key takeaway is that traditional BI and the use of dashboards will not see enterprises through the coming years. Companies need to stop looking behind them, and start to look forwards, Nair said. He added: 

I really want to kill BI. Because if BI is a dashboard, I want all of them to die. It's like the rear view mirror in the car. You use it once in a while, but if you're always driving looking in the rear view mirror it's a recipe for disaster. You want to look through the windscreen and Thoughtspot is a windscreen. We don't just try to answer the ‘what' questions, we answer the ‘what if' and ‘what next' questions. 

In the world of COVID-19, customers have realised that you can't look at the history to predict the future, because there is no history for what we are going through. Look at occupancy rates - I don't want to know the occupancy rate for 2019, it's useless to me. I want to know week by week for this year, so I can do new modelling. The shelf life of the data that you've been hogging has been significantly compressed into mere weeks or days. 

My take

Nair's pitch rings true, in that a lot of the conversations I've been having with enterprise buyers no longer focus on what has come before, but what comes next. The C-Suite is no longer interested in what has worked over the past five years, because so much has changed in the past 10 months. And no one knows what the future is going to look like in the short to medium term. With this in mind, data will be valuable for navigating uncertainty, but not if you're just focusing on stale, historical data. ThoughtSpot obviously needs to prove its platform is capable of providing this agility and foresight (and we've been promised customers to speak to) - but its thinking is spot on.