Thoughtspot has raised $30 million to build out its sales and marketing staff

Thoughtspot, the business analytics startup with a founding team comprised of ex-Nutanix, Google and Yahoo folks, has raised $30 million in a Series B round, bringing the company’s total to $40.7 million. The company is targeting the business intelligence market to provide a “Google-like experience” in regards to database analytics and will use the money to build out its sales and marketing staff. Thoughtspot offers a hardware appliance that comes loaded with software that can connect to a company’s existing data infrastructure — as well as services like Hadoop — from which it can provide users with search capabilities that will supposedly save the time it takes for employees to gather the data they want, said Thoughtspot co-founder and CEO Ajeet Singh.

There is one sure fire way to raise a truckload of money from Venture Capitalists – deliver a compelling story that promises something related to big data. And this isn’t just hype fuelled funding – the promise that big data solutions hold to deliver actionable insights from the ever increasing amount of data organizations are awash in is an attractive funding proposition. The latest company to leverage good timing and market fit to raise a monster round is ThoughtSpot. ThoughtSpot is a company that aims to reinvent Business Intelligence (BI) in the enterprise. Existing BI solutions are big, monolithic tools that tend to deliver very fine detail, but require a lot of work and tend to be slow to implement and run. ThoughtSpot Data Search Appliance is a plug-and-play solution that provides a search-based user experience for business data access and analysis. Based in Redwood City, the team that is building ThoughtSpot was also behind one of the stand out enterprise tech companies of the past few years, Nutanix. Founded by Nutanix co-founder Ajeet Singh and Amit Prakash, who was previously a founding engineer of Microsoft’s Bing team, ThoughtSpot has developed a relational search engine and in-memory database that promises to allow companies to search mass data quickly and easily.