It’s that time of the year again! I’m still buzzing about this year’s Showfloor Showdown at Gartner Data & Analytics Summit in London, where I had the opportunity to showcase ThoughtSpot's AI-Powered Analytics. In the spirit of facilitating a side-by-side comparison, we were all invited to look at global flooding and weather station data, analyze the variables affecting these natural disasters, and present key findings to the crowd.

The GPT euphoria got doused with some reality recently as Samsung employees realized they were sending false information to customers and Italy outright banned ChatGPT. The hype and concerns further accelerated last week with the godfather of AI, Hinton, resigning from Google, President Biden summoning AI leaders to Washington, and several stocks nose-diving on the threats generative AI poses to their business models.

Traditional BI has always been wrought with login friction. It’s very much a “pull” motion. In order to get the answers you need, you have to stop what you are doing “over there” and access the data you need “over here.” To disrupt this old way of thinking we launched ThoughtSpot for Sheets back in October 2022. And of course, the first thing a lot of customers asked was – "this is great, do you have something for Excel?" 🤦

Business is won or lost based on the quality of the experience you deliver to customers, partners, vendors, and employees. These experiences are built entirely on data. Harnessing data to deliver value is the single most powerful way to engage today’s demanding consumers—not to mention capturing market share and accelerating strategic decision-making. But there's a problem.

CIOs are fed up with having a plethora of BI and analytics tools with business units seemingly chasing the shiniest new solution. And although most industry surveys show data and analytics budgets continuing to grow despite a faltering economy, there is closer scrutiny and belt tightening to rid teams of overlapping capabilities. Here’s a look at how BI tool portfolios have become such a mess and how to streamline them.

If you have managed a cloud data platform, you have undoubtedly gotten that call. You know the one, it's usually from finance or the office of the CFO, inquiring about your monthly spend. And it usually comes in one of two forms: While both are clear and present dangers to cloud data platform owners, they don’t have to be.

Are you considering venturing into the world of analytics engineering? Analytics engineers are the newest addition to data teams and sit somewhere between data engineers and data analysts. They are technical, business savvy, and love to learn. A huge part of an analytics engineer’s role is learning new modern data tools to implement within data stacks.

Recently, I published an article on whether self-service BI is attainable, and spoiler alert: it certainly is. Of course, anything of value usually does require a bit of planning, collaboration, and effort. After the article was published, I began having conversations with technical leaders, analysts, and analytics engineers, and the topic of data modeling for self-service analytics came up repeatedly.

Whether you call it self-service analytics or self-service business intelligence (BI), there has been much discussion about the perils, myths, promises, and prospects of successfully building self-service capability. Going forward, I’ll use the phrase “self-service BI” but you are welcome to substitute the words “self-service analytics”. So, is self-service BI actually attainable or just snake oil?

Today we’re excited to announce ThoughtSpot Sage, our new search experience that combines the power of GPT’s natural language processing and generative AI capabilities with the accuracy and security of our patented self-service analytics platform. With this new integration, data teams will be able to exponentially increase their impact across an organization as business users self-serve personalized, actionable, and trustworthy insights like never before.