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The Ultimate Guide to Data Pipelines for Generative AI: Five Criteria to Evaluate Tools
This ultimate guide defines five product criteria to help data leaders evaluate data pipeline tools that support generative AI: functional breadth, ease of use, governance, performance & scale, and cost.
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Chief data officers and other data leaders should read this report to learn:
- The defining characteristics of GenAI data pipeline tools
- How these products prepare and deliver data for to GenAI applications
- Five primary product evaluation criteria
- Key questions to pose to vendors for each criteria
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Further information
As companies apply Generative AI language models to their own domain-specific data, they create both promise and peril. The promise: to boost productivity and gain competitive advantage by enriching business functions such as customer service, document processing, and content development. But the peril ranges from broken workflows to angry customers and inquisitive regulators. To realize the promise and avoid the peril, companies need to prepare GenAI inputs that accurately describe business reality. Achieving this requires a new class of data pipelines – and new tools to manage them.
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