What do you like best about the product?

IBM watsonx.data shines with its ability to integrate smoothly into hybrid cloud setups, existing data lakes, and diverse sources like SQL databases or legacy systems-no pricey migrations needed. Built-in AI tools, including real-time anomaly detection and automated governance, speed up analytics and boost fraud detection accuracy. It scales effortlessly for large datasets (structured or unstructured) without lag, ideal for high-volume needs. Users praise its intuitive interface, strong security protocols, and unified data management, which simplifies access and analysis.

What do you dislike about the product?

The platform’s learning curve is steep, especially for non-technical teams or those new to IBM’s ecosystem. Costs can escalate with data growth, and AI features demand hefty infrastructure. Some users report limited customization, slower support, and occasional hiccups integrating niche legacy tools. While robust, its smaller developer community (compared to open-source rivals) might slow peer-driven troubleshooting.

What problems is the product solving and how is that benefiting you?

It pulls scattered data from silos—legacy systems, SQL databases, even cloud apps—into one place, so we’re not stuck fixing broken workflows or paying for messy migrations. The AI tools auto-detect risks (like fraud) and handle governance tasks that used to eat up hours. It also scales smoothly when we’re slammed with data-heavy projects, without crashing or slowing us down.