Reimagining data analytics in the AI era

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Presented by

Firat Tekiner, Google Cloud | Shahzad Salim, Google Cloud | Miguel de Luna, Google Cloud

About this talk

Over 40% of organizations cite disparate analytics tools, data sources, and poor data quality as their biggest challenges — and the onset of generative AI has only made it more of a priority. Organizations are increasingly looking for: • The ability to access, manage, and activate structured and unstructured data across diverse storage environments • Flexibility and scalability to run any type of data, anywhere, while continuously adapting to evolving consumer needs. • Seamless integration of data with the latest AI/ML technologies - using AI for deeper data understanding, using data to enhance AI systems and performance, or swiftly combining both to tackle new challenges. Google Cloud BigQuery helps you unify data and connect it with groundbreaking AI to unleash transformative insights in a single, streamlined solution. It empowers you to develop modern, data-driven applications using popular open-source engines and models. It also provides powerful AI/ML and generative capabilities that are readily available for your data, enabling users of all skill sets to easily access the data they need and unlock its value quickly. All of this is delivered with enterprise-grade efficiency, scalability, and security. Join this webinar to learn: • Best practices of building a AI-ready data foundation • How to lower your data analytics cost of ownership by up to 54% • Why Gartner positioned Google’s data cloud and BigQuery as a leader
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Google’s Data and AI Cloud is not only the most complete and unified data analytics and AI solution provider in the market but also a total-cost-of-ownership (TCO) leader. Learn more about Google Data Cloud and how it can help you transform your business. It supports various data use cases (e.g. applications, analytics, predictions, visualizations) through products that cater to different data personas (e.g. application developers and system builders, data engineers and data analysts, data scientists and ML engineers, and business users).