👋 In this document you will discover the integration of LLM models in BigQuery, to enhance data analysis and automation capabilities, as well as the various potential use cases for this integration.

Organizations now have access to the power of LLM models, offering cutting-edge generative AI capabilities via BigQuery ML (BQML). BigQuery ML revolutionizes the application of machine learning to data in BigQuery, making it not only accessible, but also intuitive for analysts and SQL users. This integration radically transforms the way you interact with your data, enabling tasks such as text generation, summarization, translation and a host of other innovative possibilities to be performed with remarkable efficiency.

AI use cases in BigQuery

Drawing in particular on Google's documentation, we have identified several concrete use cases for AI models in BigQuery. This list is not exhaustive, as new use cases will naturally emerge as companies' data ecosystems evolve. Here are some examples of AI applications in BigQuery, from data homogenization and standardization to machine translation:

Address standardization

Context: A company has a customer database with inconsistently entered addresses.

• Analyze each address and reformat it according to a standard format.
• Correct common spelling mistakes in street and town names.
• Standardize abbreviations (e.g. "Apt" to "Appt", "blvd" to "bd", "rennes" to "Rennes").

Benefit: Improves data quality for geocoding, customer segmentation and logistics operations.

Standardization of measurement units

Context: A scientific database contains measurements in different units.

How to use :

• Identify the unit of measurement used in each entry.
• Convert all measurements to a predefined standard unit.
• Report uncertain or potentially erroneous conversions.

Benefit: Ensures data consistency for scientific analysis, reduces interpretation errors and facilitates comparison of results.

Localization of e-commerce content

Context: An international e-commerce platform wants to translate its product descriptions into several languages.

How to use :

• Automatically translate product descriptions from the source language into several target languages.
• Adapt descriptions to take account of cultural nuances and local preferences.
• Generate relevant keywords in each language to improve SEO.

Benefit: Remarkable time and cost savings by automating the generation of large-scale localized content.

Other use cases for this functionality are conceivable, depending on three main factors: the size of the company, its sector of activity and the expertise of its teams. This adaptability enables each organization to create tailor-made solutions that meet its exact needs and optimize the return on its technological investments.

The integration of LLM models into BigQuery ML enables advanced analytics and automations such as data normalization, machine translation and content localization, giving companies a strategic advantage.

☝ Convictions
‍Theintegration of artificial intelligence into BigQuery confers a significant strategic advantage on organizations that adopt it. Whether it's to standardize data, optimize the customer experience by better analyzing customer feedback, or generate relevant content, we're convinced that this technology will profoundly transform the field of data analytics.Many companies have yet to discover the potential of this integration; we'll help them harness it so they can achieve their goals