Exploring the Data & AI Landscape: Unveiling Industry Trends, Practical Challenges, and Expert Insights

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Presented by

Doug Henschen Vice President and Principal Analyst Constellation Research, Yasmeen Ahmed Managing Director of Products Google Cloud, Shahzad Salim Head of Data and AI Solutions Google Cloud

About this talk

Data analytics is transforming as organizations increasingly embrace AI and ML. Join us for an interactive digital roundtable discussion in which Google Cloud leaders explore the evolving landscape of data and AI with industry analyst Doug Henschen, VP at Constellation Research. You’ll learn about the latest trends shaping the industry, the challenges organizations face in harnessing the power of data and AI, and Google Cloud's unique perspective on addressing these challenges. Key Discussion Points: - Trends in Data & AI: Uncover emerging trends in data analytics and AI/ML that are transforming businesses across industries. - Customer Challenges: Gain insights into the common pain points organizations encounter when implementing data and AI solutions, such as data silos, talent shortages, and integration complexities. - Real-world solutions: Hear directly from Google Cloud experts on how leading organizations are tackling these challenges head-on. - Evaluation criteria: Understand the key factors to consider when evaluating analytics and AI platforms. This is your chance to engage in a candid conversation with industry analysts and Google Cloud leaders, gaining valuable insights and perspectives to inform your own data and AI strategies. Register today and be part of this enlightening discussion!
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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).