Manuel Hernández Giuliani

  Original Post in LinkedIn Enterprise Data and AI leadership in 2026 and beyond is defined by the ability to translate business intent into scalable, governed, and AI-ready data platforms. It is no longer about managing technologies, but about orchestrating data, architecture, and decision systems as a unified capability. Leaders must balance innovation with control, embedding governance, metadata, and quality into every layer while enabling real-time, autonomous intelligence through AI and agents. The focus shifts from delivering data to delivering outcomes, where platforms become products, teams operate with ownership, and every data asset is designed to drive measurable business value.

  Original Post in LinkedIn I remember when I started working in IT back in 1995 reporting meant green-bar paper coming out of big machines, and big noise, in cold data centers. If you wanted a new report, it could take weeks. Sometimes months. Then came Data Warehousing; we structured everything. ETL before loading. Star schemas (thanks Ralph Kimball and your Bus Matrix). Governance and Methodology first. It was powerful, clean, reliable, controlled. But rigid. Then the world exploded with data; we enter to the age of Data Lakes (Big Data here and there). We moved from ETL to ELT. From structured-only to everything: logs, sensors, clicks, documents, images, videos. It felt like freedom. But without governance, many lakes became swamps. The next natural step was inevitable: Data Lakehouse. We wanted scale and control. Flexibility and trust. Governance embedded into the platform. One foundation for BI, Data Science, and now AI. And today, we are living another evolution emerging. No...

  Original Post in LinkedIn Really valuable initiative. AI conversations must move beyond hype and into execution, scale, governance, architecture, and domain expertise. Sustainable AI is not about models alone. It is about integrating AI into real enterprise data platforms and operational systems. Looking forward to listening to these episodes, especially the intersection between AI and industrial transformation. AI at scale requires engineering discipline. hashtag # AI hashtag # DataArchitecture hashtag # EnterpriseAI https://lnkd.in/egTAgNrR