Generative AI (GenAI) is a game changer for business intelligence and analytics—taking self-service BI to a whole new level. And by injecting Gemini into Looker and BigQuery, Google is bringing new capabilities to its analytics and data management platforms.
Gemini is Google’s large language model (LLM) that can be used as a foundation for developing GenAI applications. Looker is Google’s analytics platform, while BigQuery is Google’s fully managed data warehouse. Google is also baking GenAI into its cloud databases with Gemini in Databases, which can help with a range of functions, from migration to management to optimization.
All three—Gemini in Looker, Gemini in BigQuery, and Gemini in Databases—are in public preview.
The evolution of GenAI
A Boston Consulting Group survey found that 89% of C-suite executives rank AI and GenAI as a top-three tech priority for 2024. Yet, only 6% of companies had trained more than a quarter of their people on GenAI tools.
That may be why we’re seeing hyperscale cloud providers make a push to bake GenAI capabilities directly into their offerings—no add-ons or integrations required. This could potentially make it easier to boost adoption, both from technical and non-technical users.
For example, Google is baking GenAI directly into its Google Cloud and Workspace ecosystem (its previous GenAI platform, Duet AI, was folded into Gemini earlier this year). So, rather than having to integrate add-on GenAI tools, there’s already a unified system that supports GenAI, analytics, and data management. Microsoft and AWS are also following this same path.
Gemini in Looker
With Gemini in Looker, users can ‘chat’ with their data for self-service analytics. Google has introduced what it calls Conversational Analytics (part of Gemini in Looker), which uses natural language processing (NLP). This allows you to ask queries and even create visualizations in natural language.
This means non-technical users can make queries and do data analysis without the need for a data science background. That doesn’t mean data scientists will be out of a job; rather, they’ll become more efficient, able to do much more in much less time. Gemini in Looker also integrates with Google Workspace applications, such as Docs, Sheets, and Slides, so graphs and pie charts can be added directly into reports and presentations to share with coworkers.
Notably, Looker will show you the “data behind the insights,” according to a Google blog, “so you know the foundation is accurate and the method is true.” Ensuring data authenticity isn’t a nice to have in today’s GenAI era, but critical for reliability and consistency.
Gemini in BigQuery
Gemini in BigQuery can help across the entire data management cycle, from data ingestion to data preparation, data cleansing, and even low-code visual data pipeline development. This can all be done using natural language—without the need for writing code.
The GenAI capabilities in BigQuery allow for query recommendations, semantic search, and the ability to translate text to SQL or Python code. BigQuery can also connect to Vertex AI, which offers users access to a wide array of both proprietary and open-source AI models.
Getting started
There are a lot of changes happening in the Google Cloud ecosystem and it can be hard to know where to start—or how to stay on top of all the latest announcements. That’s where a partner can help. For example, at Pythian, we’re helping companies use GenAI by providing cost-effective discovery, proof of concept, and production offerings in concert with our existing data services.
With our AI/ML Services and GenAI Workshop, we can help you unlock the possibilities of GenAI for analytics and data management—even educating your senior team on the key tools for the creation and execution of your AI strategy.
Ready to get started? Email us at info@pythian.com for more information.
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