Systems | Development | Analytics | API | Testing

Introducing the Snowflake Connector for ServiceNow analytics

In a world where user experience and IT support can mean the difference between hitting or missing your ARR marks, businesses have to find smarter ways to build workflows and support their IT departments. That’s where companies like ServiceNow come into play. A few years back, we created our ServiceNow SpotApp, a pre-built analytics template to help companies analyze and understand their data—so they can increase efficiencies across their complex IT environments.

5 essential steps to building great data products

There are millions of data products out there, some successful and others…less so. But the truly standout data products are the ones that change users’ behavior. You know you’ve built something special when your users start forming habits around your product. The question is, how do you create something that stands out in a sea of data products? We believe it comes down to one thing: a relentless focus on delivering user value.

Gartner Data & Analytics Summit: Building data fluency with AI-Powered Analytics

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.

4 ways GPT will change the data and analytics industry

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.

How to perform data analysis in spreadsheets

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?" 🤦

What is the modern data experience?

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.

Managing technical debt: How to go from 12 BI tools to 1

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.

How to optimize your cloud data costs: 4 steps to reduce cloud data platform costs

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.

5 engineering tools every analytics and data engineer needs to know

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.

Data modeling best practices for data and analytics engineers

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.