Stewart Bond
Research VP, Data Intelligence (IDC)
Menjelajahi bagaimana organisasi dapat mengatasi tantangan data dengan membangun fondasi data yang siap menghadapi masa depan yang memungkinkan data yang tepercaya, terukur, dan siap AI.
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Research VP, Data Intelligence (IDC)
According to the IDC Analyst Spotlight, sponsored by Qlik and Artha Solutions, missing the next 12 months on data readiness may allow rivals to lock in key AI advantages around speed, accuracy, and customer trust. Deploying robust enterprise intelligence services is a strategic necessity to scale AI successfully.
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Open the recording on VimeoUnderstand why AI initiatives often struggle when data is fragmented, inconsistently defined, or owned by disconnected teams.
Learn the core data readiness capabilities required for trusted, consistent, scalable AI programs.
Review a practical 60-90 day roadmap for prioritizing gaps, use cases, and follow-up action plans.
See how an AI Data Readiness Assessment can identify what to fix first before scaling production AI.
Research VP, Data Intelligence and Integration Software at IDC
Stewart Bond leads IDC’s Data Intelligence and Integration Software research, focusing on emerging trends in data movement, ingestion, transformation, mastering, cleansing, and consumption in the digital era.
Co-founder and COO at Artha Solutions
As COO and co-founder of Artha Data Solutions, Srinivas Poddutoori drives global data-driven innovation across enterprises through data engineering, master data management, data governance, and AI initiatives, including AI governance.
Enterprise Intelligence Architect at Qlik Partner Ambassador
Madhav works at the intersection of data strategy, digital transformation, advanced analytics, and AI, focusing on trusted, scalable data ecosystems that power intelligent automation and decision-making.
Product Marketing Director at Qlik
Sidney Drill is a product marketer at Qlik specializing in integration and analytics, known as an enthusiastic presenter, data for good advocate, and former Solution Architect.
Learn how to fix fragmented ownership, inconsistent definitions, and disconnected systems so AI can scale with trust.
Understand why AI success depends on trusted, well-governed, and consistently defined enterprise data.
Learn how to identify the data readiness gaps that keep AI pilots from reaching production.
Use a practical 60-90 day roadmap to prioritize fixes, use cases, and operating-model changes.
Connect data governance, integration, quality, and ownership to measurable AI production value.