Key Takeaways: Observability is essential for trusted AI – Yet most organizations lack the structured programs, tools, and cross-team collaboration needed to make it effective. North America is...
Is your data AI-ready? That was a consistent theme at this year’s Gartner Data & Analytics Summit in Orlando, Florida. There were many Gartner keynotes and analyst-led sessions that had...
Key Takeaways Enrich your raw data with context to unlock its full potential and enable smarter, data-driven decision-making. Combine data enrichment and AI for more accurate predictions,...
Artificial Intelligence (AI) is all the rage, and rightly so. By now most of us have experienced how Gen AI and the LLMs (large language models) that fuel it are primed to transform the way we...
Key Takeaways: Centralized visibility of data is key. Modern IT environments require comprehensive data for successful AIOps, that includes incorporating data from legacy systems like IBM i and IBM Z...
Key Takeaways: The significance of using legacy systems like mainframes in modern AI. How mainframe data helps reduce bias in AI models. The challenges and solutions involved in integrating legacy...
Key Takeaways: Trusted AI requires data integrity. For AI-ready data, focus on comprehensive data integration, data quality and governance, and data enrichment. A structured, business-first approach...
Key Takeaways: Data integrity is essential for AI success and reliability – helping you prevent harmful biases and inaccuracies in AI models. Robust data governance for AI ensures data privacy,...
Key Takeaways Data quality ensures your data is accurate, complete, reliable, and up to date – powering AI conclusions that reduce costs and increase revenue and compliance. Data observability...
