As artificial intelligence (AI) matures from experimentation into production use cases, the symbiotic relationship between data and AI becomes increasingly clear. To deliver real business impact—smarter automation, better customer experiences, and massive cost takeout—AI use cases are only as powerful as the data they’re running on.

Enterprise Performance Management (EPM) is the backbone of smarter business decisions—it brings structure to planning, budgeting, reporting, and analysis. But when handled manually, it can quickly become a grind: endless spreadsheets, complex data mapping, and reports that swallow valuable time. The work is essential, yet the process often slows teams down, leaving less room for the insights leaders actually need.

By Erez Schnaider, Technical Product Marketing Manager, ClearML The number of AI applications are rapidly increasing, and it can be difficult to keep up. Every month brings a new protocol, LLM, or tool. In this environment, the true strength of a platform is measured not only by its core features but also by its extensibility and adaptability to change. Many platforms address this challenge by hosting OSS tools or exposing API connections.

After spending months of development work to build ODBC connectors for REST APIs, the performance still falls short of your expectations. You can’t recover the time spent, and you need a new solution. This scenario is all-too familiar for developers. Here, we discuss how you can improve your REST API, making it ready for enterprise-level users.

If you are looking for a reporting solution that can handle complex data and deliver fast results, you might want to check out Jet Reports from insightsoftware. Jet Reports is a powerful Excel-based reporting tool that directly connects to your Microsoft Dynamics ERP and allows you to create custom reports and dashboards with ease using real-time data. Jet Reports is designed to make your reporting experience fast and smooth.

Your AI-powered data science partner just got a major upgrade. The Data Science Agent now automates ML workflows with Spark, BigQuery ML, & BigQuery DataFrames.

In addition to its existing text embedding models, you can now use both Google’s Gemini and open-source (OSS) models directly within BigQuery.