After 20 years of dashboards, today’s line-of-business users expect more value from their analytics, and it’s up to your business to keep updated - or risk getting left behind. There was a time when product teams could purchase basic dashboards and data visualizations, and that was more than enough to satisfy the average user’s business intelligence (BI) and analytics needs. Today, however? Not so much.

An effective dashboard requires careful design to present data in the best way, and to help more people (users, customers) find insights without feeling overwhelmed. Yellowfin BI comes with a wide variety of chart types as part of its extensive data visualization tools, and while it is tempting to use a lot of eye-catching charts to make a dashboard that looks great, it is important to select the right chart for the right situation.

Tips for writing natural language prompts to generate SQL and charts using BigQuery data canvas’ NL2SQL and NL2Chart capabilities.

Learn about BigQuery data canvas in this technical walkthrough with a real-world example based on the public github_repos dataset.

How to use BigQuery multilingual embeddings, vector index and vector search to search and get results in a chosen language.