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ThoughtSpot is helping customers reimagine how they use data, analytics, and AI with Google Cloud tools like BigQuery and Looker.

ChatGPT’s power is astounding and even more importantly, it is growing incredibly fast. Almost every BI and analytics vendor has initiatives in this area, and while several have implemented integrations, applications are still primarily experimental. The promise is huge, but of course, there are plenty of skeptics with valid reasons. This blog highlights areas where ChatGPT can help business intelligence initiatives, and discusses areas where there may be a bit more hype than reality, for now.

Businesses everywhere have engaged in modernization projects with the goal of making their data and application infrastructure more nimble and dynamic. By breaking down monolithic apps into microservices architectures, for example, or making modularized data products, organizations do their best to enable more rapid iterative cycles of design, build, test, and deployment of innovative solutions.

Every application provider has the same goals: to help their users work more efficiently, and to drive user adoption. But many companies fail to achieve this goal because they struggle to provide the reporting and analytics users have come to expect. The Definitive Guide to Embedded Analytics is designed to answer any and all questions you have about the topic. It will show you what embedded analytics are and how they can help your company.

BigCommerce has teamed up with BigQuery to create a native integration to its ecommerce platform that makes data analysis easier than ever.

Lost in the talk about OpenAI is the tremendous amount of compute needed to train and fine-tune LLMs, like GPT, and Generative AI, like ChatGPT. Each iteration requires more compute and the limitation imposed by Moore’s Law quickly moves that task from single compute instances to distributed compute. To accomplish this, OpenAI has employed Ray to power the distributed compute platform to train each release of the GPT models.

When it comes to choosing a business intelligence (BI) solution vendor for your business, there are a variety of factors to consider. One important area of comparison is the natural language query (NLQ) features offered by different BI vendors. NLQ is increasingly becoming a key capability of the modern self-service analytics experience, as it allows users to ask complex questions of their data, and receive insightful answers in the form of a pre-generated, best practice data visualizations.