What generative AI can do for your data
We believe that the world is just beginning to understand what AI technology can do for your business data. With generative AI, the role of data analysts is expanding beyond merely collecting, processing, and performing analysis of large datasets to proactively driving data-driven business impact.
For example, data analysts can use generative models to summarize historical email marketing data (open rates, click-through rates, conversion rates, etc.) to understand which types of subject lines consistently lead to higher open rates, and whether personalized offers perform better than general promotions. Using these insights, analysts can prompt the model to create a list of compelling subject line options tailored to the identified preferences. They can further utilize the generative AI model to draft engaging email content, all within one platform.
Early users have expressed tremendous interest in solving various use cases across industries. For instance, using ML.GENERATE_TEXT can simplify advanced data processing tasks including:
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Content generation: Analyze customer feedback and generate personalized email content right inside BigQuery without the need for complex tools. Prompt example: “Create a customized marketing email based on customer sentiment stored in [table name]”
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Summarization: Summarize text stored in BigQuery columns such as online reviews or chat transcripts. Prompt example: “Summarize customer reviews in [table name]”
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Data enhancement: Obtain a country name for a given city name. Prompt example: “For every zip code in column X, give me city name in column Y”
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Rephrasing: Correct spelling and grammar in textual content such as voice-to-text transcriptions. Prompt example: “Rephrase column X and add results to column Y”
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Feature extraction: Extract key information or words from the large text files such as in online reviews and call transcripts. Prompt example: “Extract city names from column X”
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Sentiment analysis: Understand human sentiment about specific subjects in a text. Prompt example: “Extract sentiment from column X and add results to column Y”
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Retrieval-augmented generation (RAG): Retrieve data relevant to a question or task using BigQuery vector search and provide it as context to a model. For example, use a support ticket to find 10 closely-related previous cases, and pass them to a model as context to summarize and suggest a resolution.
By expanding the support for state-of-the-art foundation models such as Gemini 1.0 Pro in Vertex AI, BigQuery helps make it simple, easy, and cost-effective to integrate unstructured data within your Data Cloud.
Join us for the future of data and generative AI
To learn more about these new features, check out the documentation. Use this tutorial to apply Google’s best-in-class AI models to your data, deploy models and operationalize ML workflows without moving data from BigQuery. You can also watch a demonstration on how to build an end-to-end data analytics and AI application directly from BigQuery while harnessing the potential of advanced models like Gemini together with behind the scenes on how it’s made. Watch our recent product innovation webcast to learn more about the latest innovations and how you can use BigQuery ML a to create and use models using simple SQL.
Googlers Mike Henderson, Tianxiang Gao and Manoj Gunti contributed to this blog post. Many Googlers contributed to make these features a reality.
