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Join Ehrar Jameel, Head of Data and Analytics, as he demystifies the concept of data strategy in this enlightening snippet from our Art of Data Leadership series. In this segment, Ehrar delves into the fundamental question: What is a data strategy? Ready to delve deeper into the world of data leadership? Click here for the full Art of Data Leadership playlist and gain invaluable insights from Ehrar and other industry experts.
Executives across various industries are under pressure to reach insights and make decisions quickly. This is driving the importance of streaming data and analytics, which play a crucial role in making better-informed decisions that likely lead to faster, better outcomes.
In the life sciences industry, where breakthroughs in research and healthcare are fueled by data, data silos can be a big problem. Data silos might be caused by things like legacy systems, departmental divisions, disparate data formats, or lack of interoperability standards. Data silos can manifest at any point in the product lifecycle and make it hard for the right people to access and use the information they need, when they need it.
Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Confluent’s cloud-native offering is the foundational platform for data in motion – designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organization. With Confluent, organizations can meet the new business imperative of delivering rich, digital front-end customer experiences and transitioning to sophisticated, real-time, software-driven backend operations.
Confluent Cloud for Apache Flink®, a leading cloud-native, serverless Flink service is now available on AWS, Google Cloud, and Microsoft Azure. Confluent's fully managed, cloud-native service for Flink helps customers build high-quality data streams for data pipelines, real-time applications, and analytics.
Initiative involves development of AI frameworks to expedite time to market and reduce the complexity of building and deploying infrastructure for generative AI.