For more and more companies, data is their competitive advantage – but it is only an advantage if those data assets can be leveraged across the enterprise, in the hands of business people.
– Paul O’Rourke, VP Sales, Coalesce
When I joined ThoughtSpot in late 2018, there were 250 employees and the company was selling an on-premise analytics platform that was delivered via a server appliance and racked in data centers. Fortune 50 customers wanted ThoughtSpot so badly that they’d buy 40+ servers and rack ‘em and stack ‘em for Search Driven BI. 2018 was another record year for ThoughtSpot.
At the end of my first year (late 2019), it became abundantly clear that Snowflake was revolutionizing the data warehousing market and that customers wanted their data in the cloud. And once their data was on Snowflake, they didn’t want to move it. The next few years were a pretty wild ride, but to ThoughtSpot’s credit this clear market shift was acknowledged and the engineering and product teams (and marketing and sales teams) executed the proverbial “rebuild the plane while flying it” product transition.
In 2020, ThoughtSpot’s Cloud SaaS offering was born and the Snowflake partnership took off. After an impressive four-year run, ThoughtSpot is still selling that same game-changing search based analytics platform–but is now a 1,000 employee cloud company with over 1,000 customers, and has become a very important part of the modern data stack.
I feel fortunate to have been a part of this journey, and the product evolution and win stories that came with it.
The Largest Bottleneck in Automating Data Analytics
I’ve spent the last decade in the analytics ecosystem. The most consistent objection from customers over that time period–but particularly during my five years with ThoughtSpot–has been data readiness. It has long been an issue for customers trying to automate analytics and machine learning processes. We consistently spoke with customers who had embarked on a data modernization project and made investments in a cloud data platform, and their biggest bottleneck was transforming and prepping data.
ThoughtSpot excels in the retail space, delivering significant value to merchandise teams who need to slice and dice data on a daily basis to better understand inventory levels, in-store promotion effectiveness, seasonal patterns, etc. Data readiness is critical in this use case. If not accurately prepped, there is no value to that merchandiser because they can’t trust the data.
On multiple occasions we saw projects delayed by 6-12 months while data was moved and transformed due to the manual nature of that process. During this time, end users were forced to wait and struggle with legacy tools, directly impacting productivity and decision making. One retailer explained that accelerating or automating that data transformation phase would equate to better, more informed decision-making and potentially millions of dollars in direct revenue impact.
Coalesce
I first learned about Coalesce two years ago when the company was about to introduce their product to the market. Taking an enterprise approach to data transformation, making it simple, consistent, and accessible to a broader user community paralleled ThoughtSpot’s approach to the BI segment. Fast forward two years and the Coalesce vision is becoming reality. The company is gaining significant traction and momentum in the market and customer feedback has been outstanding.
For more and more companies, data is their competitive advantage but it is only an advantage if those data assets can be leveraged across the enterprise, in the hands of business people. Coalesce is uniquely positioned to help companies achieve this goal and has a truly world class team driving the execution of this mission. That is why this move to Coalesce feels like such a natural progression. I’m excited and honored to be a part of what’s ahead.
