BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//hacksw/handcal//NONSGML v1.0//EN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20260416T070513Z
DESCRIPTION:Click for Latest Location Information: http://dgiq-edw2026.data
 versity.net/sessionPop.cfm?confid=165&proposalid=16658\nOrganizations are r
 apidly investing in AI initiatives, expecting to unlock value from their da
 ta. At the same time, many recognize the need for data governance to suppor
 t these efforts. However, a critical gap often remains unaddressed: the lac
 k of a clear understanding of how data is created, transformed, and used ac
 ross end-to-end business processes.\nIn a large, multi-decade organization 
 operating across complex operational and supply chain processes, this gap b
 ecame evident when the Director of Analytics identified the need to enable 
 AI-driven use cases and improve trust in data. While data governance was re
 cognized as a priority, it quickly became clear that governance alone would
  not be sufficient without a strong understanding of the underlying busines
 s architecture and data flows.\nThis session presents a real-world case stu
 dy where AI readiness is being addressed by first establishing clarity in b
 usiness processes and how they generate data. Rather than starting with mod
 els or tools, the initiative focuses on building a foundation for trusted, 
 decision-ready data—while also improving the way the business operate
 s on a daily basis.\nThe approach includes:\n\n
 Identification and prioritization of critical operational processes require
 d to support analytics and AI use cases\n
 Mapping of end-to-end processes (AS-IS) to understand how work is actually 
 performed, how data is generated, and how inefficiencies in operations dire
 ctly impact data quality and decision-making\n
 Identification of gaps in process definition, ownership, and data flows imp
 acting data quality and governance\n
 Development of a heatmap that connects each macro business process with key
  data, technology, and AI initiatives\n
 Design of future-state processes (TO-BE) to improve operational performance
 , simplify daily activities, and enable more consistent, reliable, and deci
 sion-ready data across the organization\n
 Definition of an initial data governance model tailored to the Operations f
 unction\n
 Establishment of metrics to measure improvements in business processes, dat
 a quality, consistency, and AI readiness\n\nThis work provides a structured
  roadmap that connects business processes, data, technology, and AI—a
 llowing the organization to move from fragmented initiatives toward a more 
 aligned and scalable approach.\nAttendees will learn:\n\n
 Why AI readiness depends on understanding how data is generated across busi
 ness processes\n
 How to connect business architecture with data governance and data manageme
 nt practices\n
 How to design a practical, domain-specific governance approach (e.g., Opera
 tions)\n
 How to build a roadmap that aligns data, technology, and AI initiatives to 
 business processes\n
 How to use artifacts like process maps and heatmaps to drive alignment and 
 prioritization\n
DTSTART:20260505T164500
SUMMARY:Building Data Readiness for AI: A Practical Framework Connecting Bu
 siness Processes, Data, and Governance (presented in Spanish)
DTEND:20260505T172959
LOCATION: See Description
END:VEVENT
END:VCALENDAR