How To Make Sense of Enterprise-Level Data With Google Cloud’s Vertex AI and BigQuery

As an application developer integrating analytics into your application, your users expect a scalable, flexible solution that adapts to changing business needs. While organizations strive to capitalize on new AI tools, they’re also still wrestling with big data: massive, fast-moving datasets that traditional tools can’t handle easily. Today, platforms like BigQuery and Vertex AI give you the scale and intelligence to work with that data directly, without the bottlenecks of the old big data era.

These massive datasets are an asset to larger companies, but incorporating AI-powered analytics can pose unforeseen problems when the data isn’t AI-ready. Here’s how Google Cloud’s Vertex AI and BigQuery can help make sense of your wealth of information while helping developers deploy customizable chat and analytics capabilities.

Machine Learning With Vertex AI

If you’re a Google Cloud developer, you already have the power of Vertex AI at your fingertips. Vertex AI is a machine learning (ML) platform that helps developers deploy ML models more quickly into AI-powered applications. The ML model in Vertex AI trains on the data you provide while allowing you to stay fully within the Google Cloud ecosystem.

With Vertex AI, teams can quickly implement custom chatbots, advanced analytics, and dynamic data experiences that engage users and uncover insights. This unique adaptability not only enhances user engagement but also gives your applications a competitive advantage in a fast-evolving market.

Managing Datasets With BigQuery

Large datasets offer great opportunities for analysis, but unearthing and sharing the insights can pose unique challenges. You can easily run into complex processes and high costs when trying to extract value from your enterprise data.

AI adds additional complications. Gartner projects that through 2026, organizations will abandon 60% of AI initiatives that aren’t supported by AI-ready data.

Google Cloud’s answer to roadblocks like these is BigQuery. It’s an AI-ready, enterprise data warehouse that stores and helps make sense of big data. Because BigQuery handles data infrastructure, it frees up time for users to analyze data even at a massive scale. When users need to visualize data, BigQuery integrates with business intelligence tools to create visualizations that help users and their stakeholders make sense of organizational information.

But even with Vertex AI and BigQuery, you may find that you need a tool purpose-built for embedding.

Customizing Your AI Analysis With Logi Symphony

To enhance AI and data offerings, Google Cloud has partnered with Logi Symphony to deliver powerful scale and analytics that drive smarter business decisions. Logi Symphony lets you design dashboards, reports, and AI chats directly into your application. With no extra tabs or clunky embedding, it looks and behaves like it was built into your product from day one.

And it works seamlessly with Google Cloud’s AI and BigQuery offerings. Simply start by getting your Vertex Key from Google Cloud, then connect it to Logi Symphony AI.

The AI within Logi Symphony allows users to bring their own large language model (LLM). With this setup, Google Cloud users can leverage Vertex AI directly within their environment. This flexibility means application teams can select and tailor the foundational model to their unique needs, eliminating the limitations of one-size-fits-all solutions.

With Logi Symphony on Google Cloud Marketplace, application teams gain the powerful backing of BigQuery as well. This integration enables your application to efficiently analyze massive first- and third-party datasets, which empower users to make informed decisions even with enterprise-scale data.

Logi Symphony enables you to get started quickly using your existing Google budget to provide your users with AI-powered insights, without ever leaving your application.

Ready to learn more? Watch our on-demand webinar to learn about the future of analytics with Logi Symphony and Google Cloud.

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