Question: I'm looking for a solution that can handle common data problems like missing values and outliers, can you suggest something?

If you need something to handle more mundane data issues like missing values and outliers, you might want to check out the Gretel Navigator. It's an AI system that lets you generate, edit and amplify tabular data, including modes to fill in gaps with plausible data and to transform data with SQL or natural language prompts. With real-time inference and a real-time inference API, Gretel Navigator can help you improve data quality and speed up product development.

Another good choice is Dataiku, a tool designed to make data useful for business purposes. It covers a lot of data project bases, including data preparation, machine learning, MLOps, collaboration and governance. Dataiku offers a variety of prepackaged solutions for different teams and industries, letting you build, deploy and maintain machine learning models with a focus on safety and efficiency. It's a Leader in the Gartner Magic Quadrant for Data Science & ML Platforms.

For a data intelligence platform, check out Collibra. It's got a broad range of data management tools, including AI Governance, Data Catalog, Data Governance, Data Quality & Observability and more. With data quality monitoring, pipeline reliability tracking and integrated data management, Collibra is designed to automate data processes and give you a unified view of data assets across the organization. That can help you work more productively and cut manual labor.

Last, Metaplane offers automated end-to-end data observability so data teams can quickly identify and fix data problems. Its features include ML-based monitoring and anomaly detection, data CI/CD, column-level lineage and real-time schema change alerts. By integrating with other tools in the modern data stack, Metaplane helps teams of any size cut time spent on data quality problems and build data trust.