What do you like best about the product?

The most impressive part, however, is how AI and analytics work together, enabling data query and management on both structured and unstructured formats from a single platform. It is also Agile, scale-outable, and interoperable with open data formats like Parquet and Iceberg.

What do you dislike about the product?

IBM watsonx is undoubtedly powerful, but it is not without its drawbacks. For teams inexperienced with IBM’s ecosystem, the setup is multifaceted, the integration is tedious, and the ramp-up phase can be frustrating due to the advanced learning curve. Pricing models are often ambiguous for smaller teams, and along with uneven performance on larger datasets, it becomes increasingly costly. Furthermore, community support is limited and still in the developmental phase, leading to fears around vendor lock-in.

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

IBM Watsonx.data addresses critical issues concerning the accessibility, integration, and analytics of data at scale. It helps by consolidating structured and unstructured data across multiple clouds and on-premises systems utilizing an open data lakehouse framework. This allows me to analyze and parse through extensive datasets from various locations without physically relocating them, thus optimizing processes and minimizing expenses associated with storage. It also ensures governance, security, and AI model readiness which supports me by accelerating trusted decision-making while simplifying the operational processes from raw data into insights.