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...
Key Takeaways Leverage AI to achieve digital transformation goals: enhanced efficiency, decision-making, customer experiences, and more. Address common challenges in managing SAP master data by using...
The Precisely team recently had the privilege of hosting a luncheon at the Gartner Data & Analytics Summit in London. It was an engaging gathering of industry leaders from various sectors, who...
Key Takeaways: Data governance has transitioned from a regulatory necessity to a strategic asset that enhances innovation. Robust data governance supports AI applications, ensuring data is AI-ready,...
Insurance industry leaders are just beginning to understand the value that generative AI can bring to the claims management process. By harnessing the power of machine learning and natural language...
