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In 2023, generative AI took center stage. However, as companies boosted their AI strategies, they recognized the importance of having clean, high-quality data, which underscored the need for solid infrastructure. Companies like Snowflake and Microsoft capitalized on this, even acquiring major players to help customers access their data for various AI applications and integrate AI capabilities into their products.
Here are VentureBeat’s top 5 data stories of 2023:
1. Microsoft’s Ambitious Move in the Cloud War
In May, Microsoft introduced Fabric, an all-in-one analytics platform that combines tools like Azure Synapse Analytics and Power BI into a single product. This platform aims to unlock data potential and serve as a foundation for AI, potentially giving Microsoft an edge over Amazon and Google in supporting large enterprises.
2. The Emergence of Vector Databases
Charles Xie, CEO of Zilliz, highlighted the rise of vector databases, a new type of database management designed for the AI era. These databases excel at searching unstructured data, but can also handle semi-structured and structured data. Xie discussed how businesses should use vector databases to address their specific needs.
3. Databricks Acquires MosaicML for $1.3 Billion
Ahead of its annual summit, Databricks acquired AI company MosaicML, integrating its team and AI models to offer a unified platform for managing data assets and building secure generative AI applications. This move is seen as a way to democratize AI, giving organizations more control over their data.
4. Salesforce’s Enhanced Data Foundations
In September, Salesforce announced enhancements to its AI strategy, revealing that its Data Cloud would now support bi-directional data sharing with Databricks’ and Snowflake’s platforms. This allows customers to enrich their datasets and build more robust models for various business-critical functions.
5. Snowflake’s Document AI for Unstructured Data Search
In June, Snowflake launched Document AI, a large language model tool designed to help enterprises quickly extract value from unstructured documents like PDF invoices. This marked a significant development for Snowflake, which originally focused on structured data, by enabling teams to mobilize useful unstructured information previously locked in different formats and locations.
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