Data

The quality of AI output depends on the quality of the context we provide. The fourth episode is about the journey from data to decision.

AI won’t save bad data; it will only amplify and spread their problems faster. In the fourth episode, we’ll go through the path from data to decision and look at where data most often “dies” in companies—usually long before anyone gets to it. We’ll analyze the anatomy of a dashboard that someone will actually use and open the topic of sensitive data and anonymization. Because AI will find even what everyone has long forgotten.

Three perspectives

• Individual – how to read data, verify outputs, and recognize when the model is making things up
• Team leader – data ownership, single source of truth, documentation, decision-driven instead of data-driven
• Company – data governance, access levels, safe use of data in AI tools

What will you learn?

• The process to verify a model’s output before passing it on.
• Typical points where data quality gets lost within a company.
• A practical view of what anonymization means when AI works with data.