The blind spot of the data accuracy & integrity process

The blind spot of the data accuracy & integrity process
The data accuracy process from a layman perspective

Can data accuracy and integrity as the vital quality standard for reliable and trustful data ever be assured to be 100 percent correct to reality ?

Are unknown undetected data errors a given but have proliferated in particular in social media apps ?

What are the implications of erroneous data to the development of AI machine learning applications ?

Remember the basic activities' model of the quality paradigm

  • Prevention
  • Detection
  • Correction

which is good practice in any context.

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Integrated Quality Management within GRC: A Foundation for Responsible Agentic AI

Integrated Quality Management within GRC: A Foundation for Responsible Agentic AI

Governance defines what should be achieved. Risk Management determines what could go wrong. Compliance establishes what must be respected. Quality Management ensures that processes and their outcomes consistently fulfill requirements & intended objectives. Together, they establish an integrated management system for responsible human & AI-driven enterprise operations. Event-driven SIPOC model

By Peter Haenni