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Data is often described with cliches like, it's "the new oil" or “the new air.” No matter how you describe it, there is no denying the increasing importance of data monetization across every industry. Forward-thinking organizations recognize data apps as both a revenue stream and a differentiated service to increase customer loyalty.

It’s an exciting time to be in the world of data and business intelligence. Recent advances in AI and machine learning are not only changing the way we interact with data, but also pushing those of us who build analytics and BI platforms to think critically about how our products can best serve our customers moving forward. Some will always love getting hands-on with data—but that’s no longer the only option.

Every winter, members of ThoughtSpot’s research and development teams participate in a company-wide hackathon called Codex. The ideas that come out of Codex are always inspiring, but the Winter 22/23 hackathon was special—OpenAI had just released ChatGPT and the world was buzzing about generative AI.

ThoughtSpot is taking Snowpark use cases to the next level with generative AI, connecting the dots between ML-powered insights and business action. If you’re new to Snowpark, this is Snowflake’s set of libraries and runtimes that securely deploy and process non-SQL code including Python, Java, and Scala.

Are you a ThoughtSpot enthusiast? Maybe you built a liveboard that saved your department hours each work week, or perhaps you figured out a unique way to gamify adoption across your team. You put in the hard work, now it’s time to show it off. ThoughtSpot User Groups were designed to help users connect—a place where you can share stories and get new ideas to empower your organization with data.

Data is key to building resilience and achieving operational excellence—but first, your data must be intelligible. Luckily, modern BI solutions have intuitive interfaces that allow business users to build interactive data visualizations and contextual data stories. With this knowledge at their fingertips, your entire organization is empowered to make data-driven decisions.

Every business that analyzes their operational (or transactional) data needs to build a custom data pipeline involving several batch or streaming jobs to extract transactional data from relational databases, transform it, and load it into the data warehouse. In this post, we show how you can leverage Amazon Aurora zero-ETL integration with Amazon Redshift and ThoughtSpot for GenAI driven near real-time operational analytics.

In The fundamentals of data warehouse architecture, we covered the standard layers and shared components of a well-formed data warehouse architecture. In this second part, we’ll cover the core components of the multi-tiered architectures for your data warehouse.

When using data to make impactful business decisions, certain doubts may start to arise, like “What does this column exactly mean?” or “Can I trust this data source I want to use?” Questions like these speak to a larger need for increased data literacy and trust in data. ThoughtSpot continually invests in this area, giving users the confidence to build the correct Answers needed for their analysis—and ensuring they can trust the data they are shown.

We’ve talked about the many ways large language models (LLMs) and artificial intelligence (AI) are impacting business efficiency, data and analytics, and even FinOps. But we’ve yet to talk about arguably one of the most important areas of concern: security.