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How would you describe data quality processes in your organization?

There is no structured methods but do it adhoc depending on business needs

There is no check & balances

Mature processes, having dedicated Data Governance & steward and Data QA practices handles Data Quality

Business manages it on their own way

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Do you believe that (RPA, Automation, and intelligent Automation), coupled with clear guidelines, robust process mapping, and effective monitoring and control capabilities, can serve as a key enabler for successfully integrating AI and GenAI into an organization's operations and strategies?

Strongly Agree

Agree

Disagree

Strongly Disagree

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What experiences have you had with AI Guardrails across Google Gemini (for Workspaces). We’re concerned about amplifying access to sensitive files (I.e. access already exists but Gemini now surfaces insights from these files so much more readily). We'll tighten up access controls and likely implement Google Workspace AI Classification, but we’re also considering selecting and implementing an AI Guardrails vendor. Which vendors do people have experiences with? Aporia, Arize, Bedrock, Databricks, Guardrails AI, IBM, Nvidia, Whylabs Which features were most relevant and useable? Bias Mitigation, Explainability, Data Privacy, Security, Regulatory Compliance Is a vendor needed? Could a homespun collection of Gems suffice?

SAP continues to drive 'Clean the Core' or more recently 'Cleaner Core'. Besides removal of unnecessary or unused WRICEF code (a waste of time effort in my eyes), looking for any real clean you have done and found beneficial.  One thought we have had is to move off some of the SAP capability and move to SaaS solutions integrated via an orchestration layer. Thus reducing our SAP footprint and moving us towards a viable S/4 upgrade. Any thoughts on this 'cleaning' approach?

Does anyone has experience with Databricks? We would like to learn about your experience.

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James Knapp, VP, Strategy & Architecture at Nielsen | Gartner Peer Community