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Enterprise Data Strategy Management For Pro Analytics & Ai
Published 8/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.38 GB | Duration: 2h 20m
Enterprise data management for analytics and AI: assess data readiness, identify risks, and build a 90-day roadmap plan.
What you'll learn
Identify critical data bottlenecks stalling AI projects and calculate the true financial cost of poor data quality.
Perform comprehensive data fitness audits and stress-test enterprise data specifically for machine learning readiness.
Draft a customized 90-day transition roadmap for your data infrastructure to successfully secure executive funding.
Implement modern executive frameworks that reduce enterprise analytics and AI project failure rates by up to 60%.
Requirements
A basic understanding of general data concepts (like databases or business intelligence) is helpful. However, no advanced coding, Python, or SQL skills are required. This is a strategic, architectural course designed for leadership and implementation, making it accessible even if you don't write code.
Description
"This course contains the use of artificial intelligence."Your AI strategy is only as reliable as the enterprise data supporting it.Managing Enterprise Data for Analytics and AI teaches a practical, technology-neutral method for determining whether organizational data is fit for analytics, machine learning, and generative AI use cases.Rather than treating governance, data quality, lineage, metadata, and AI readiness as separate topics, you will bring them together into a measurable readiness assessment.Who this course is for
ata managers, data governance professionals, and data product managers.Analytics and BI managers responsible for trusted business information.AI, ML, and GenAI program leaders evaluating enterprise data readiness.Architects, consultants, transformation leaders, and Chief Data Office teams.Aspiring data and AI leaders who need an executive-level decision framework.What you will learniagnose how enterprise data problems undermine analytics and AI.Measure analytics fitness using ten readiness dimensions.Calculate a 0-100 Analytics Readiness Score.Distinguish traditional data quality from AI data readiness.Evaluate provenance, privacy, usage rights, representativeness, metadata, retrieval readiness, and monitoring.Calculate a 0-100 AI Readiness Score.Translate readiness gaps into quantified risks and controls.Prioritize remediation using impact, severity, urgency, and effort.Build an actionable 90-day data and AI readiness roadmap.Present a Go, Conditional-Go, or No-Go recommendation to executive stakeholders.Requirements:No programming is required. Basic familiarity with data, business intelligence, databases, analytics, or AI terminology is useful but not mandatory.Final project:You will complete an Enterprise Data & AI Readiness Assessment for a realistic business use case. You will evaluate ten analytics and ten AI-readiness dimensions, document at least ten gaps, produce quantitative readiness scores, prioritize five interventions, create a 90-day roadmap, and deliver a one-page executive recommendation.This course is designed for Data Managers, IT Directors, and Lead Data Engineers tasked with scaling enterprise analytics or preparing pipelines for machine learning. It is also perfect for mid-level tech professionals and project managers looking to pivot into Data Strategy and Architecture roles by mastering how to secure C-suite buy-in for data governance.
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