Without sufficient data to feed and train models, your data science initiatives can’t get off the ground. With best-of-breed data ingestion and broad set of connectivity, you’re able to keep cloud data lakes and lakehouses hydrated with the data your AI and machine learning teams need.
Data science and AI projects that use inaccurate, unreliable data can cause real business risk. Intelligent, cloud-native data quality—with pre-built rules and templates—helps you cleanse, standardize, and enrich data at scale. Now you can trust the data that’s feeding your AI and machine learning models.
Data scientists spend more time finding and preparing raw data than they do using it for AI and machine learning. Intelligent, automated data preparation accelerates time to insights by giving teams access to data that’s transformed, fit for use, and masked for privacy.
To accelerate AI initiatives, you need fast, end-to-end, intelligent data pipelines. Manual, hand-coded efforts require constant tweaking. Instead, take advantage of zero-code, automated solutions that use AI-powered recommendations and intelligent mappings so data engineers can build and process pipelines fast.
- Shyam Dadala
Enterprise Analytics Architecture; Engineer, Shire Pharmaceuticals